Zoltan2_AlgMultiJagged.hpp

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#ifndef _ZOLTAN2_ALGMultiJagged_HPP_
#define _ZOLTAN2_ALGMultiJagged_HPP_
 
#include <Zoltan2_MultiJagged_ReductionOps.hpp>
#include <Zoltan2_CoordinateModel.hpp>
#include <Zoltan2_Parameters.hpp>
#include <Zoltan2_Algorithm.hpp>
#include <Zoltan2_IntegerRangeList.hpp>
#include <Teuchos_StandardParameterEntryValidators.hpp>
 
#include <Tpetra_Distributor.hpp>
#include <Teuchos_ParameterList.hpp>
#include <Zoltan2_CoordinatePartitioningGraph.hpp>
#include <new>          // ::operator new[]
#include <algorithm>    // std::sort
#include <Zoltan2_Util.hpp>
#include <vector>
 
#if defined(__cplusplus) && __cplusplus >= 201103L
#include <unordered_map>
#else
#include <Teuchos_Hashtable.hpp>
#endif // C++11 is enabled
 
#ifdef ZOLTAN2_USEZOLTANCOMM
#ifdef HAVE_ZOLTAN2_MPI
#define ENABLE_ZOLTAN_MIGRATION
#include "zoltan_comm_cpp.h"
#include "zoltan_types.h" // for error codes
#endif
#endif
 
#ifdef HAVE_ZOLTAN2_OMP
#include <omp.h>
#endif
 
#define LEAST_SIGNIFICANCE 0.0001
#define SIGNIFICANCE_MUL 1000
 
//if the (last dimension reduce all count) x the mpi world size
//estimated to be bigger than this number then migration will be forced
//in earlier iterations.
#define FUTURE_REDUCEALL_CUTOFF 1500000
//if parts right before last dimension are estimated to have less than
//MIN_WORK_LAST_DIM many coords, migration will be forced in earlier iterations.
#define MIN_WORK_LAST_DIM 1000
 
 
 
 
#define ZOLTAN2_ABS(x) ((x) >= 0 ? (x) : -(x))
//imbalance calculation. Wreal / Wexpected - 1
#define imbalanceOf(Wachieved, totalW, expectedRatio) \
        (Wachieved) / ((totalW) * (expectedRatio)) - 1
#define imbalanceOf2(Wachieved, wExpected) \
        (Wachieved) / (wExpected) - 1
 
 
#define ZOLTAN2_ALGMULTIJAGGED_SWAP(a,b,temp) temp=(a);(a)=(b);(b)=temp;
 
 
namespace Teuchos{
 
template <typename Ordinal, typename T>
class Zoltan2_BoxBoundaries  : public ValueTypeReductionOp<Ordinal,T>
{
private:
    Ordinal size;
    T _EPSILON;
 
public:
    Zoltan2_BoxBoundaries ():size(0), _EPSILON (std::numeric_limits<T>::epsilon()){}
 
    Zoltan2_BoxBoundaries (Ordinal s_):
        size(s_), _EPSILON (std::numeric_limits<T>::epsilon()){}
 
    void reduce( const Ordinal count, const T inBuffer[], T inoutBuffer[]) const
    {
        for (Ordinal i=0; i < count; i++){
            if (Z2_ABS(inBuffer[i]) >  _EPSILON){
                inoutBuffer[i] = inBuffer[i];
            }
        }
    }
};
} // namespace Teuchos
 
namespace Zoltan2{
 
template <typename T>
T *allocMemory(size_t size){
    if (size > 0){
        T * a = new T[size];
        if (a == NULL) {
            throw  "cannot allocate memory";
        }
        return a;
    }
    else {
        return NULL;
    }
}
 
template <typename T>
void freeArray(T *&array){
    if(array != NULL){
        delete [] array;
        array = NULL;
    }
}
 
 
template <typename IT, typename CT, typename WT>
class uMultiSortItem
{
public:
    //TODO: Why volatile?
    //no idea, another intel compiler faiulure.
    volatile IT index;
    volatile CT count;
    //unsigned int val;
    volatile WT *val;
    volatile WT _EPSILON;
 
    uMultiSortItem(){
        this->index = 0;
        this->count = 0;
        this->val = NULL;
        this->_EPSILON = std::numeric_limits<WT>::epsilon() * 100;
    }
 
 
    uMultiSortItem(IT index_ ,CT count_, WT *vals_){
        this->index = index_;
        this->count = count_;
        this->val = vals_;
        this->_EPSILON = std::numeric_limits<WT>::epsilon() * 100;
    }
 
    uMultiSortItem( const uMultiSortItem<IT,CT,WT>& other ){
        this->index = other.index;
        this->count = other.count;
        this->val = other.val;
        this->_EPSILON = other._EPSILON;
    }
 
    ~uMultiSortItem(){
        //freeArray<WT>(this->val);
    }
 
    void set(IT index_ ,CT count_, WT *vals_){
        this->index = index_;
        this->count = count_;
        this->val = vals_;
    }
 
 
    uMultiSortItem<IT,CT,WT> operator=(const uMultiSortItem<IT,CT,WT>& other){
        this->index = other.index;
        this->count = other.count;
        this->val = other.val;
        return *(this);
    }
 
    bool operator<(const uMultiSortItem<IT,CT,WT>& other) const{
        assert (this->count == other.count);
        for(CT i = 0; i < this->count; ++i){
            //if the values are equal go to next one.
            if (ZOLTAN2_ABS(this->val[i] - other.val[i]) < this->_EPSILON){
                continue;
            }
            //if next value is smaller return true;
            if(this->val[i] < other.val[i]){
                return true;
            }
            //if next value is bigger return false;
            else {
                return false;
            }
        }
        //if they are totally equal.
        return this->index < other.index;
    }
    bool operator>(const uMultiSortItem<IT,CT,WT>& other) const{
        assert (this->count == other.count);
        for(CT i = 0; i < this->count; ++i){
            //if the values are equal go to next one.
            if (ZOLTAN2_ABS(this->val[i] - other.val[i]) < this->_EPSILON){
                continue;
            }
            //if next value is bigger return true;
            if(this->val[i] > other.val[i]){
                return true;
            }
            //if next value is smaller return false;
            else //(this->val[i] > other.val[i])
            {
                return false;
            }
        }
        //if they are totally equal.
        return this->index > other.index;
    }
};// uSortItem;
 
template <class IT, class WT>
struct uSortItem
{
    IT id;
    //unsigned int val;
    WT val;
};// uSortItem;
 
template <class IT, class WT>
void uqsort(IT n, uSortItem<IT, WT> * arr)
{
 
    int NSTACK = 50;
    int M = 7;
    IT         i, ir=n, j, k, l=1;
    IT         jstack=0, istack[50];
    WT aval;
    uSortItem<IT,WT>    a, temp;
 
    --arr;
    for (;;)
    {
        if (ir-l < M)
        {
            for (j=l+1;j<=ir;j++)
            {
                a=arr[j];
                aval = a.val;
                for (i=j-1;i>=1;i--)
                {
                    if (arr[i].val <= aval)
                        break;
                    arr[i+1] = arr[i];
                }
                arr[i+1]=a;
            }
            if (jstack == 0)
                break;
            ir=istack[jstack--];
            l=istack[jstack--];
        }
        else
        {
            k=(l+ir) >> 1;
 
            ZOLTAN2_ALGMULTIJAGGED_SWAP(arr[k],arr[l+1], temp)
            if (arr[l+1].val > arr[ir].val)
            {
                ZOLTAN2_ALGMULTIJAGGED_SWAP(arr[l+1],arr[ir],temp)
            }
            if (arr[l].val > arr[ir].val)
            {
                ZOLTAN2_ALGMULTIJAGGED_SWAP(arr[l],arr[ir],temp)
            }
            if (arr[l+1].val > arr[l].val)
            {
                ZOLTAN2_ALGMULTIJAGGED_SWAP(arr[l+1],arr[l],temp)
            }
            i=l+1;
            j=ir;
            a=arr[l];
            aval = a.val;
            for (;;)
            {
                do i++; while (arr[i].val < aval);
                do j--; while (arr[j].val > aval);
                if (j < i) break;
                ZOLTAN2_ALGMULTIJAGGED_SWAP(arr[i],arr[j],temp);
            }
            arr[l]=arr[j];
            arr[j]=a;
            jstack += 2;
            if (jstack > NSTACK){
                std::cout << "uqsort: NSTACK too small in sort." << std::endl;
                exit(1);
            }
            if (ir-i+1 >= j-l)
            {
                istack[jstack]=ir;
                istack[jstack-1]=i;
                ir=j-1;
            }
            else
            {
                istack[jstack]=j-1;
                istack[jstack-1]=l;
                l=i;
            }
        }
    }
}
 
template <class IT, class WT, class SIGN>
struct uSignedSortItem
{
    IT id;
    //unsigned int val;
    WT val;
    SIGN signbit; // 1 means positive, 0 means negative.
    bool operator<(const uSignedSortItem<IT, WT, SIGN>& rhs) const {
      /*if I am negative, the other is positive*/
      if (this->signbit < rhs.signbit){
        return true;
      }
      /*if both has the same sign*/
      else if (this->signbit == rhs.signbit){
 
        if (this->val < rhs.val){//if my value is smaller,
          return this->signbit;//then if we both are positive return true.
                              //if we both are negative, return false.
        }
        else if (this->val > rhs.val){//if my value is larger,
          return !this->signbit; //then if we both are positive return false.
                                //if we both are negative, return true.
        }
        else { //if both are equal.
          return false;
        }
      }
      else {
        /*if I am positive, the other is negative*/
        return false;
      }
 
    }
    bool operator>(const uSignedSortItem<IT, WT, SIGN>& rhs) const {
      /*if I am positive, the other is negative*/
      if (this->signbit > rhs.signbit){
        return true;
      }
      /*if both has the same sign*/
      else if (this->signbit == rhs.signbit){
 
        if (this->val < rhs.val){//if my value is smaller,
          return !this->signbit;//then if we both are positive return false.
                              //if we both are negative, return true.
        }
        else if (this->val > rhs.val){//if my value is larger,
          return this->signbit; //then if we both are positive return true.
                                //if we both are negative, return false.
        }
        else { // if they are equal
          return false;
        }
      }
      else {
        /*if I am negative, the other is positive*/
        return false;
      }
    }
    bool operator<=(const uSignedSortItem<IT, WT, SIGN>& rhs){
      return !(*this > rhs);}
    bool operator>=(const uSignedSortItem<IT, WT, SIGN>& rhs){
      return !(*this  < rhs);}
};
 
template <class IT, class WT, class SIGN>
void uqSignsort(IT n, uSignedSortItem<IT, WT, SIGN> * arr){
 
    IT NSTACK = 50;
    IT M = 7;
    IT         i, ir=n, j, k, l=1;
    IT         jstack=0, istack[50];
    uSignedSortItem<IT,WT,SIGN>    a, temp;
 
    --arr;
    for (;;)
    {
        if (ir < M + l)
        {
            for (j=l+1;j<=ir;j++)
            {
                a=arr[j];
                for (i=j-1;i>=1;i--)
                {
                    if (arr[i] <= a)
                    {
                        break;
                    }
                    arr[i+1] = arr[i];
                }
                arr[i+1]=a;
            }
            if (jstack == 0)
                break;
            ir=istack[jstack--];
            l=istack[jstack--];
        }
        else
        {
            k=(l+ir) >> 1;
            ZOLTAN2_ALGMULTIJAGGED_SWAP(arr[k],arr[l+1], temp)
            if (arr[l+1] > arr[ir])
            {
                ZOLTAN2_ALGMULTIJAGGED_SWAP(arr[l+1],arr[ir],temp)
            }
            if (arr[l] > arr[ir])
            {
                ZOLTAN2_ALGMULTIJAGGED_SWAP(arr[l],arr[ir],temp)
            }
            if (arr[l+1] > arr[l])
            {
                ZOLTAN2_ALGMULTIJAGGED_SWAP(arr[l+1],arr[l],temp)
            }
            i=l+1;
            j=ir;
            a=arr[l];
            for (;;)
            {
                do i++; while (arr[i] < a);
                do j--; while (arr[j] > a);
                if (j < i) break;
                ZOLTAN2_ALGMULTIJAGGED_SWAP(arr[i],arr[j],temp);
            }
            arr[l]=arr[j];
            arr[j]=a;
            jstack += 2;
            if (jstack > NSTACK){
                std::cout << "uqsort: NSTACK too small in sort." << std::endl;
                exit(1);
            }
            if (ir+l+1 >= j+i)
            {
                istack[jstack]=ir;
                istack[jstack-1]=i;
                ir=j-1;
            }
            else
            {
                istack[jstack]=j-1;
                istack[jstack-1]=l;
                l=i;
            }
        }
    }
}
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
class AlgMJ
{
private:
    typedef coordinateModelPartBox mj_partBox_t;
    typedef std::vector<mj_partBox_t> mj_partBoxVector_t;
 
    RCP<const Environment> mj_env; //the environment object
    RCP<const Comm<int> > mj_problemComm; //initial comm object
 
    double imbalance_tolerance; //input imbalance tolerance.
    mj_part_t *part_no_array; //input part array specifying num part to divide along each dim.
    int recursion_depth; //the number of steps that partitioning will be solved in.
    int coord_dim, num_weights_per_coord; //coordinate dim and # of weights per coord
 
    size_t initial_num_loc_coords; //initial num local coords.
    global_size_t initial_num_glob_coords; //initial num global coords.
 
    mj_lno_t num_local_coords; //number of local coords.
    mj_gno_t num_global_coords; //number of global coords.
 
    mj_scalar_t **mj_coordinates; //two dimension coordinate array
    mj_scalar_t **mj_weights; //two dimension weight array
    bool *mj_uniform_parts; //if the target parts are uniform
    mj_scalar_t **mj_part_sizes; //target part weight sizes.
    bool *mj_uniform_weights; //if the coordinates have uniform weights.
 
    ArrayView<const mj_gno_t> mj_gnos; //global ids of the coordinates, comes from the input
    size_t num_global_parts; //the targeted number of parts
 
    mj_gno_t *initial_mj_gnos; //initial global ids of the coordinates.
    mj_gno_t *current_mj_gnos; //current global ids of the coordinates, might change during migration.
    int *owner_of_coordinate; //the actual processor owner of the coordinate, to track after migrations.
 
    mj_lno_t *coordinate_permutations; //permutation of coordinates, for partitioning.
    mj_lno_t *new_coordinate_permutations; //permutation work array.
    mj_part_t *assigned_part_ids; //the part ids assigned to coordinates.
 
    mj_lno_t *part_xadj; //beginning and end of each part.
    mj_lno_t *new_part_xadj; // work array for beginning and end of each part.
 
    //get mj specific parameters.
    bool distribute_points_on_cut_lines; //if partitioning can distribute points on same coordiante to different parts.
    mj_part_t max_concurrent_part_calculation; // how many parts we can calculate concurrently.
 
    bool mj_run_as_rcb; //if this is set, then recursion depth is adjusted to its maximum value.
    int mj_user_recursion_depth; //the recursion depth value provided by user.
    bool mj_keep_part_boxes; //if the boxes need to be kept.
 
    int check_migrate_avoid_migration_option; //whether to migrate=1, avoid migrate=2, or leave decision to MJ=0
    int migration_type; // when doing the migration, 0 will aim for perfect load-imbalance, 
          //1 - will aim for minimized number of messages with possibly bad load-imbalance
    double minimum_migration_imbalance; //when MJ decides whether to migrate, the minimum imbalance for migration.
    int num_threads; //num threads
   
    // Nonuniform first level partitioning (Currently available only for sequential_task_partitioning):
    // Used for Dragonfly task mapping by partitioning Dragonfly RCA 
    // machine coordinates and application coordinates.
    // An optimization that completely partitions the most important machine dimension 
    // first (i.e. the Dragonfly group coordinate, or RCA's x coordinate). The standard 
    // MJ alg follows after the nonuniform first level partitioning. 
    //
    // Ex. (first level partitioning): If we have 120 elements, 
    // num_first_level_parts = 3, first_level_distribution = [4, 10, 6], then
    // part sizes after first level will be [24, 60, 36]. Standard uniform MJ 
    // continues for all subsequent levels.
    mj_part_t num_first_level_parts; // If used, number of parts requested for a nonuniform first level partitioning
    const mj_part_t *first_level_distribution; // If used, the requested distribution of parts for the nonuniform first level partitioning
 
    mj_part_t total_num_cut ; //how many cuts will be totally
    mj_part_t total_num_part;    //how many parts will be totally
 
    mj_part_t max_num_part_along_dim ;         //maximum part count along a dimension.
    mj_part_t max_num_cut_along_dim; //maximum cut count along a dimension.
    size_t max_num_total_part_along_dim; //maximum part+cut count along a dimension.
 
    mj_part_t total_dim_num_reduce_all;    //estimate on #reduceAlls can be done.
    mj_part_t last_dim_num_part; //max no of parts that might occur
                                //during the partition before the
                                //last partitioning dimension.
 
    RCP<Comm<int> > comm; //comm object than can be altered during execution
    float fEpsilon; //epsilon for float
    mj_scalar_t sEpsilon; //epsilon for mj_scalar_t
 
    mj_scalar_t maxScalar_t; //max possible scalar
    mj_scalar_t minScalar_t; //min scalar
 
    mj_scalar_t *all_cut_coordinates;
    mj_scalar_t *max_min_coords;
    mj_scalar_t *process_cut_line_weight_to_put_left; //how much weight should a MPI put left side of the each cutline
    mj_scalar_t **thread_cut_line_weight_to_put_left; //how much weight percentage should each thread in MPI put left side of the each outline
 
    // work array to manipulate coordinate of cutlines in different iterations.
    //necessary because previous cut line information is used for determining
    //the next cutline information. therefore, cannot update the cut work array
    //until all cutlines are determined.
    mj_scalar_t *cut_coordinates_work_array;
 
    //cumulative part weight array.
    mj_scalar_t *target_part_weights;
 
    mj_scalar_t *cut_upper_bound_coordinates ;  //upper bound coordinate of a cut line
    mj_scalar_t *cut_lower_bound_coordinates ;  //lower bound coordinate of a cut line
    mj_scalar_t *cut_lower_bound_weights ;  //lower bound weight of a cut line
    mj_scalar_t *cut_upper_bound_weights ;  //upper bound weight of a cut line
 
    mj_scalar_t *process_local_min_max_coord_total_weight ; //combined array to exchange the min and max coordinate, and total weight of part.
    mj_scalar_t *global_min_max_coord_total_weight ;//global combined array with the results for min, max and total weight.
 
    //isDone is used to determine if a cutline is determined already.
    //If a cut line is already determined, the next iterations will skip this cut line.
    bool *is_cut_line_determined;
    //my_incomplete_cut_count count holds the number of cutlines that have not been finalized for each part
    //when concurrentPartCount>1, using this information, if my_incomplete_cut_count[x]==0, then no work is done for this part.
    mj_part_t *my_incomplete_cut_count;
    //local part weights of each thread.
    double **thread_part_weights;
    //the work manupulation array for partweights.
    double **thread_part_weight_work;
 
    //thread_cut_left_closest_point to hold the closest coordinate to a cutline from left (for each thread).
    mj_scalar_t **thread_cut_left_closest_point;
    //thread_cut_right_closest_point to hold the closest coordinate to a cutline from right (for each thread)
    mj_scalar_t **thread_cut_right_closest_point;
 
    //to store how many points in each part a thread has.
    mj_lno_t **thread_point_counts;
 
    mj_scalar_t *process_rectilinear_cut_weight;
    mj_scalar_t *global_rectilinear_cut_weight;
 
    //for faster communication, concatanation of
    //totalPartWeights sized 2P-1, since there are P parts and P-1 cut lines
    //leftClosest distances sized P-1, since P-1 cut lines
    //rightClosest distances size P-1, since P-1 cut lines.
    mj_scalar_t *total_part_weight_left_right_closests ;
    mj_scalar_t *global_total_part_weight_left_right_closests;
 
    RCP<mj_partBoxVector_t> kept_boxes;  // vector of all boxes for all parts;
                                         // constructed only if
                                         // mj_keep_part_boxes == true
    RCP<mj_partBox_t> global_box;
    int myRank, myActualRank; //processor rank, and initial rank
 
    bool divide_to_prime_first;
 
    /* \brief Either the mj array (part_no_array) or num_global_parts should be provided in
     * the input. part_no_array takes
     * precedence if both are provided.
     * Depending on these parameters, total cut/part number,
     * maximum part/cut number along a dimension, estimated number of reduceAlls,
     * and the number of parts before the last dimension is calculated.
     * */
    void set_part_specifications();
 
    /* \brief Tries to determine the part number for current dimension,
     * by trying to make the partitioning as square as possible.
     * \param num_total_future how many more partitionings are required.
     * \param root how many more recursion depth is left.
     */
    inline mj_part_t get_part_count(
                mj_part_t num_total_future,
                double root);
 
    /* \brief Allocates the all required memory for the mj partitioning algorithm.
     *
     */
    void allocate_set_work_memory();
 
    /* \brief for part communication we keep track of the box boundaries.
     * This is performed when either asked specifically, or when geometric mapping is performed afterwards.
     * This function initializes a single box with all global min and max coordinates.
     * \param initial_partitioning_boxes the input and output vector for boxes.
     */
    void init_part_boxes(RCP<mj_partBoxVector_t> & outPartBoxes);
 
    /* \brief compute global bounding box:  min/max coords of global domain */
    void compute_global_box();
 
    /* \brief Function returns how many parts that will be obtained after this dimension partitioning.
     * It sets how many parts each current part will be partitioned into in this dimension to num_partitioning_in_current_dim vector,
     * sets how many total future parts each obtained part will be partitioned into in next_future_num_parts_in_parts vector,
     * If part boxes are kept, then sets initializes the output_part_boxes as its ancestor.
     *
     *  \param num_partitioning_in_current_dim: output. How many parts each current part will be partitioned into.
     *  \param future_num_part_in_parts: input, how many future parts each current part will be partitioned into.
     *  \param next_future_num_parts_in_parts: output, how many future parts each obtained part will be partitioned into.
     *  \param future_num_parts: output, max number of future parts that will be obtained from a single
     *  \param current_num_parts: input, how many parts are there currently.
     *  \param current_iteration: input, current dimension iteration number.
     *  \param input_part_boxes: input, if boxes are kept, current boxes.
     *  \param output_part_boxes: output, if boxes are kept, the initial box boundaries for obtained parts.
     */
    mj_part_t update_part_num_arrays(
                std::vector<mj_part_t> &num_partitioning_in_current_dim, //assumes this vector is empty.
                std::vector<mj_part_t> *future_num_part_in_parts,
                std::vector<mj_part_t> *next_future_num_parts_in_parts, //assumes this vector is empty.
                mj_part_t &future_num_parts,
                mj_part_t current_num_parts,
                int current_iteration,
                RCP<mj_partBoxVector_t> input_part_boxes,
                RCP<mj_partBoxVector_t> output_part_boxes,
                mj_part_t atomic_part_count);
 
    void mj_get_local_min_max_coord_totW(
                mj_lno_t coordinate_begin_index,
                mj_lno_t coordinate_end_index,
                mj_lno_t *mj_current_coordinate_permutations,
                mj_scalar_t *mj_current_dim_coords,
                mj_scalar_t &min_coordinate,
                mj_scalar_t &max_coordinate,
                mj_scalar_t &total_weight);
 
    void mj_get_global_min_max_coord_totW(
        mj_part_t current_concurrent_num_parts,
        mj_scalar_t *local_min_max_total,
        mj_scalar_t *global_min_max_total);
 
    void mj_get_initial_cut_coords_target_weights(
        mj_scalar_t min_coord,
        mj_scalar_t max_coord,
        mj_part_t num_cuts/*p-1*/ ,
        mj_scalar_t global_weight,
        mj_scalar_t *initial_cut_coords /*p - 1 sized, coordinate of each cut line*/,
        mj_scalar_t *target_part_weights /*cumulative weights, at left side of each cut line. p-1 sized*/,
 
        std::vector <mj_part_t> *future_num_part_in_parts, //the vecto
        std::vector <mj_part_t> *next_future_num_parts_in_parts,
        mj_part_t concurrent_current_part,
        mj_part_t obtained_part_index,
        mj_part_t num_target_first_level_parts = 1,
        const mj_part_t *target_first_level_dist = NULL);
 
    void set_initial_coordinate_parts(
        mj_scalar_t &max_coordinate,
        mj_scalar_t &min_coordinate,
        mj_part_t &concurrent_current_part_index,
        mj_lno_t coordinate_begin_index,
        mj_lno_t coordinate_end_index,
        mj_lno_t *mj_current_coordinate_permutations,
        mj_scalar_t *mj_current_dim_coords,
        mj_part_t *mj_part_ids,
        mj_part_t &partition_count);
 
    void mj_1D_part(
        mj_scalar_t *mj_current_dim_coords,
        double imbalanceTolerance,
        mj_part_t current_work_part,
        mj_part_t current_concurrent_num_parts,
        mj_scalar_t *current_cut_coordinates,
        mj_part_t total_incomplete_cut_count,
        std::vector <mj_part_t> &num_partitioning_in_current_dim);
 
    void mj_1D_part_get_thread_part_weights(
        size_t total_part_count,
        mj_part_t num_cuts,
        mj_scalar_t max_coord,
        mj_scalar_t min_coord,
        mj_lno_t coordinate_begin_index,
        mj_lno_t coordinate_end_index,
        mj_scalar_t *mj_current_dim_coords,
        mj_scalar_t *temp_current_cut_coords,
        bool *current_cut_status,
        double *my_current_part_weights,
        mj_scalar_t *my_current_left_closest,
        mj_scalar_t *my_current_right_closest);
 
    void mj_accumulate_thread_results(
        const std::vector <mj_part_t> &num_partitioning_in_current_dim,
        mj_part_t current_work_part,
        mj_part_t current_concurrent_num_parts);
 
    void mj_get_new_cut_coordinates(
        const size_t &num_total_part,
        const mj_part_t &num_cuts,
        const mj_scalar_t &max_coordinate,
        const mj_scalar_t &min_coordinate,
        const mj_scalar_t &global_total_weight,
        const double &used_imbalance_tolerance,
        mj_scalar_t * current_global_part_weights,
        const mj_scalar_t * current_local_part_weights,
        const mj_scalar_t *current_part_target_weights,
        bool *current_cut_line_determined,
        mj_scalar_t *current_cut_coordinates,
        mj_scalar_t *current_cut_upper_bounds,
        mj_scalar_t *current_cut_lower_bounds,
        mj_scalar_t *current_global_left_closest_points,
        mj_scalar_t *current_global_right_closest_points,
        mj_scalar_t * current_cut_lower_bound_weights,
        mj_scalar_t * current_cut_upper_weights,
        mj_scalar_t *new_current_cut_coordinates,
        mj_scalar_t *current_part_cut_line_weight_to_put_left,
        mj_part_t *rectilinear_cut_count,
        mj_part_t &my_num_incomplete_cut);
 
    void mj_calculate_new_cut_position (
        mj_scalar_t cut_upper_bound,
        mj_scalar_t cut_lower_bound,
        mj_scalar_t cut_upper_weight,
        mj_scalar_t cut_lower_weight,
        mj_scalar_t expected_weight,
        mj_scalar_t &new_cut_position);
 
    void mj_create_new_partitions(
        mj_part_t num_parts,
        mj_scalar_t *mj_current_dim_coords,
        mj_scalar_t *current_concurrent_cut_coordinate,
        mj_lno_t coordinate_begin,
        mj_lno_t coordinate_end,
        mj_scalar_t *used_local_cut_line_weight_to_left,
        double **used_thread_part_weight_work,
        mj_lno_t *out_part_xadj);
 
    bool mj_perform_migration(
        mj_part_t in_num_parts, //current umb parts
        mj_part_t &out_num_parts, //output umb parts.
        std::vector<mj_part_t> *next_future_num_parts_in_parts,
        mj_part_t &output_part_begin_index,
        size_t migration_reduce_all_population,
        mj_lno_t num_coords_for_last_dim_part,
        std::string iteration,
        RCP<mj_partBoxVector_t> &input_part_boxes,
        RCP<mj_partBoxVector_t> &output_part_boxes);
 
    void get_processor_num_points_in_parts(
                mj_part_t num_procs,
                mj_part_t num_parts,
                mj_gno_t *&num_points_in_all_processor_parts);
 
    bool mj_check_to_migrate(
                size_t migration_reduce_all_population,
                mj_lno_t num_coords_for_last_dim_part,
                mj_part_t num_procs,
                mj_part_t num_parts,
                mj_gno_t *num_points_in_all_processor_parts);
 
 
    void mj_migration_part_proc_assignment(
                mj_gno_t * num_points_in_all_processor_parts,
                mj_part_t num_parts,
                mj_part_t num_procs,
                mj_lno_t *send_count_to_each_proc,
                std::vector<mj_part_t> &processor_ranks_for_subcomm,
                std::vector<mj_part_t> *next_future_num_parts_in_parts,
                mj_part_t &out_num_part,
                std::vector<mj_part_t> &out_part_indices,
                mj_part_t &output_part_numbering_begin_index,
                int *coordinate_destinations);
 
    void mj_assign_proc_to_parts(
                mj_gno_t * num_points_in_all_processor_parts,
                mj_part_t num_parts,
                mj_part_t num_procs,
                mj_lno_t *send_count_to_each_proc,
                std::vector<mj_part_t> &processor_ranks_for_subcomm,
                std::vector<mj_part_t> *next_future_num_parts_in_parts,
                mj_part_t &out_part_index,
                mj_part_t &output_part_numbering_begin_index,
                int *coordinate_destinations);
 
    void assign_send_destinations(
                mj_part_t num_parts,
                mj_part_t *part_assignment_proc_begin_indices,
                mj_part_t *processor_chains_in_parts,
                mj_lno_t *send_count_to_each_proc,
                int *coordinate_destinations);
 
    void assign_send_destinations2(
        mj_part_t num_parts,
        uSortItem<mj_part_t, mj_part_t> * sort_item_part_to_proc_assignment, //input sorted wrt processors
        int *coordinate_destinations,
        mj_part_t &output_part_numbering_begin_index,
        std::vector<mj_part_t> *next_future_num_parts_in_parts);
 
    void mj_assign_parts_to_procs(
        mj_gno_t * num_points_in_all_processor_parts,
        mj_part_t num_parts,
        mj_part_t num_procs,
        mj_lno_t *send_count_to_each_proc, //output: sized nprocs, show the number of send point counts to each proc.
        std::vector<mj_part_t> *next_future_num_parts_in_parts,//input how many more partitions the part will be partitioned into.
        mj_part_t &out_num_part, //output, how many parts the processor will have. this is always 1 for this function.
        std::vector<mj_part_t> &out_part_indices, //output: the part indices which the processor is assigned to.
        mj_part_t &output_part_numbering_begin_index, //output: how much the part number should be shifted when setting the solution
        int *coordinate_destinations);
 
    void mj_migrate_coords(
        mj_part_t num_procs,
        mj_lno_t &num_new_local_points,
        std::string iteration,
        int *coordinate_destinations,
        mj_part_t num_parts);
 
    void create_sub_communicator(std::vector<mj_part_t> &processor_ranks_for_subcomm);
 
 
    void fill_permutation_array(
        mj_part_t output_num_parts,
        mj_part_t num_parts);
 
    void set_final_parts(
                mj_part_t current_num_parts,
                mj_part_t output_part_begin_index,
                RCP<mj_partBoxVector_t> &output_part_boxes,
                bool is_data_ever_migrated);
    void free_work_memory();
    void create_consistent_chunks(
        mj_part_t num_parts,
        mj_scalar_t *mj_current_dim_coords,
        mj_scalar_t *current_concurrent_cut_coordinate,
        mj_lno_t coordinate_begin,
        mj_lno_t coordinate_end,
        mj_scalar_t *used_local_cut_line_weight_to_left,
        mj_lno_t *out_part_xadj,
        int coordInd, bool longest_dim_part, uSignedSortItem<int, mj_scalar_t, char> *p_coord_dimension_range_sorted);
 
    mj_part_t find_largest_prime_factor(mj_part_t num_parts){
      mj_part_t largest_factor = 1;
      mj_part_t n = num_parts;
      mj_part_t divisor = 2;
      while (n > 1){
        while (n % divisor == 0){
          n = n / divisor;
          largest_factor = divisor;
        }
        ++divisor;
        if (divisor * divisor > n){
          if (n > 1){
            largest_factor = n;
          }
          break;
        }
      }
      return largest_factor;
    }
public:
    AlgMJ();
 
    void multi_jagged_part(
                const RCP<const Environment> &env,
                RCP<const Comm<int> > &problemComm,
 
                double imbalance_tolerance,
                size_t num_global_parts,
                mj_part_t *part_no_array,
                int recursion_depth,
 
                int coord_dim,
                mj_lno_t num_local_coords,
                mj_gno_t num_global_coords,
                const mj_gno_t *initial_mj_gnos,
                mj_scalar_t **mj_coordinates,
 
                int num_weights_per_coord,
                bool *mj_uniform_weights,
                mj_scalar_t **mj_weights,
                bool *mj_uniform_parts,
                mj_scalar_t **mj_part_sizes,
 
                mj_part_t *&result_assigned_part_ids,
                mj_gno_t *&result_mj_gnos);
                  
 
    void set_partitioning_parameters(
                bool distribute_points_on_cut_lines_,
                int max_concurrent_part_calculation_,
                int check_migrate_avoid_migration_option_,
                double minimum_migration_imbalance_, int migration_type_ = 0);
    
    void set_to_keep_part_boxes();
 
    RCP<mj_partBox_t> get_global_box() const;
 
    RCP<mj_partBoxVector_t> get_kept_boxes() const;
 
    RCP<mj_partBoxVector_t> compute_global_box_boundaries(
        RCP<mj_partBoxVector_t> &localPartBoxes) const;
 
    void sequential_task_partitioning(
        const RCP<const Environment> &env,
        mj_lno_t num_total_coords,
        mj_lno_t num_selected_coords,
        size_t num_target_part,
        int coord_dim,
        mj_scalar_t **mj_coordinates,
        mj_lno_t *initial_selected_coords_output_permutation,
        mj_lno_t *output_xadj,
        int recursion_depth,
        const mj_part_t *part_no_array,
        bool partition_along_longest_dim,
        int num_ranks_per_node,
        bool divide_to_prime_first_,
        mj_part_t num_first_level_parts_ = 1,
        const mj_part_t *first_level_distribution_ = NULL);
 
};
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::sequential_task_partitioning(
    const RCP<const Environment> &env,
    mj_lno_t num_total_coords,
    mj_lno_t num_selected_coords,
    size_t num_target_part,
    int coord_dim_,
    mj_scalar_t **mj_coordinates_,
    mj_lno_t *inital_adjList_output_adjlist,
    mj_lno_t *output_xadj,
    int rd,
    const mj_part_t *part_no_array_,
    bool partition_along_longest_dim,
    int num_ranks_per_node,
    bool divide_to_prime_first_,
    mj_part_t num_first_level_parts_,
    const mj_part_t *first_level_distribution_) {
 
    this->mj_env = env;
    const RCP<Comm<int> > commN;
    this->mj_problemComm = 
          Teuchos::DefaultComm<int>::getDefaultSerialComm(commN);
    this->comm = 
          Teuchos::rcp_const_cast<Comm<int> >(this->mj_problemComm);
    this->myActualRank = this->myRank = 1;
 
#ifdef HAVE_ZOLTAN2_OMP
        //int actual_num_threads = omp_get_num_threads();
        //omp_set_num_threads(1);
#endif
 
    this->divide_to_prime_first = divide_to_prime_first_;
    //weights are uniform for task mapping
 
    //parts are uniform for task mapping
    //as input indices.
    this->imbalance_tolerance = 0;
    this->num_global_parts = num_target_part;
    this->part_no_array = (mj_part_t *)part_no_array_;
    this->recursion_depth = rd;
 
    // If nonuniform first level partitioning, the requested num of parts and the requested distribution of 
    // elements for each part
    this->num_first_level_parts = num_first_level_parts_;
    this->first_level_distribution = (mj_part_t *)first_level_distribution_;
 
    this->coord_dim = coord_dim_;
    this->num_local_coords = num_total_coords;
    this->num_global_coords = num_total_coords;
    this->mj_coordinates = mj_coordinates_;  //will copy the memory to this->mj_coordinates.
 
    this->initial_mj_gnos = allocMemory<mj_gno_t>(this->num_local_coords);
 
    this->num_weights_per_coord = 0;
    bool *tmp_mj_uniform_weights = new bool[1];
    this->mj_uniform_weights = tmp_mj_uniform_weights;
    this->mj_uniform_weights[0] = true;
 
    mj_scalar_t **tmp_mj_weights = new mj_scalar_t *[1];
    this->mj_weights = tmp_mj_weights; //will copy the memory to this->mj_weights
 
    bool *tmp_mj_uniform_parts = new bool[1];
    this->mj_uniform_parts = tmp_mj_uniform_parts;
    this->mj_uniform_parts[0] = true;
 
    mj_scalar_t **tmp_mj_part_sizes = new mj_scalar_t * [1];
    this->mj_part_sizes = tmp_mj_part_sizes;
    this->mj_part_sizes[0] = NULL;
 
    this->num_threads = 1;
    this->set_part_specifications();
 
    this->allocate_set_work_memory();
    //the end of the initial partition is the end of coordinates.
    this->part_xadj[0] = static_cast<mj_lno_t>(num_selected_coords);
    for(size_t i = 0; i < static_cast<size_t>(num_total_coords); ++i){
        this->coordinate_permutations[i] = inital_adjList_output_adjlist[i];
    }
 
    mj_part_t current_num_parts = 1;
 
    mj_scalar_t *current_cut_coordinates =  this->all_cut_coordinates;
 
    mj_part_t future_num_parts = this->total_num_part;
 
    std::vector<mj_part_t> *future_num_part_in_parts = new std::vector<mj_part_t> ();
    std::vector<mj_part_t> *next_future_num_parts_in_parts = new std::vector<mj_part_t> ();
    next_future_num_parts_in_parts->push_back(this->num_global_parts);
    RCP<mj_partBoxVector_t> t1;
    RCP<mj_partBoxVector_t> t2;
 
 
    std::vector <uSignedSortItem<int, mj_scalar_t, char> > coord_dimension_range_sorted(this->coord_dim);
    uSignedSortItem<int, mj_scalar_t, char> *p_coord_dimension_range_sorted = &(coord_dimension_range_sorted[0]);
    std::vector <mj_scalar_t> coord_dim_mins(this->coord_dim);
    std::vector <mj_scalar_t> coord_dim_maxs(this->coord_dim);
 
    for (int i = 0; i < this->recursion_depth; ++i) {
 
        //partitioning array. size will be as the number of current partitions and this
        //holds how many parts that each part will be in the current dimension partitioning.
        std::vector <mj_part_t> num_partitioning_in_current_dim;
 
        //number of parts that will be obtained at the end of this partitioning.
        //future_num_part_in_parts is as the size of current number of parts.
        //holds how many more parts each should be divided in the further
        //iterations. this will be used to calculate num_partitioning_in_current_dim,
        //as the number of parts that the part will be partitioned
        //in the current dimension partitioning.
 
        //next_future_num_parts_in_parts will be as the size of outnumParts,
        //and this will hold how many more parts that each output part
        //should be divided. this array will also be used to determine the weight ratios
        //of the parts.
        //swap the arrays to use iteratively..
        std::vector<mj_part_t> *tmpPartVect= future_num_part_in_parts;
        future_num_part_in_parts = next_future_num_parts_in_parts;
        next_future_num_parts_in_parts = tmpPartVect;
 
        //clear next_future_num_parts_in_parts array as
        //getPartitionArrays expects it to be empty.
        //it also expects num_partitioning_in_current_dim to be empty as well.
        next_future_num_parts_in_parts->clear();
 
 
        //returns the total number of output parts for this dimension partitioning.
        mj_part_t output_part_count_in_dimension =
                        this->update_part_num_arrays(
                                        num_partitioning_in_current_dim,
                                        future_num_part_in_parts,
                                        next_future_num_parts_in_parts,
                                        future_num_parts,
                                        current_num_parts,
                                        i,
                                        t1,
                                        t2, num_ranks_per_node);
 
        //if the number of obtained parts equal to current number of parts,
        //skip this dimension. For example, this happens when 1 is given in the input
        //part array is given. P=4,5,1,2
        if(output_part_count_in_dimension == current_num_parts) {
            tmpPartVect= future_num_part_in_parts;
            future_num_part_in_parts = next_future_num_parts_in_parts;
            next_future_num_parts_in_parts = tmpPartVect;
            continue;
        }
 
        //convert i to string to be used for debugging purposes.
        std::string istring = Teuchos::toString<int>(i);
 
        //alloc Memory to point the indices
        //of the parts in the permutation array.
        this->new_part_xadj = allocMemory<mj_lno_t>(output_part_count_in_dimension);
 
        //the index where in the outtotalCounts will be written.
        mj_part_t output_part_index = 0;
        //whatever is written to outTotalCounts will be added with previousEnd
        //so that the points will be shifted.
        mj_part_t output_coordinate_end_index = 0;
 
        mj_part_t current_work_part = 0;
        mj_part_t current_concurrent_num_parts = 1;
 
        mj_part_t obtained_part_index = 0;
 
        //get the coordinate axis along which the partitioning will be done.
        int coordInd = i % this->coord_dim;
        mj_scalar_t * mj_current_dim_coords = this->mj_coordinates[coordInd];
 
 
        //run for all available parts.
        for (; current_work_part < current_num_parts;
                     current_work_part += current_concurrent_num_parts) {
 
 
            //current_concurrent_num_parts = std::min(current_num_parts - current_work_part,
            //this->max_concurrent_part_calculation);
 
            mj_part_t actual_work_part_count = 0;
            //initialization for 1D partitioning.
            //get the min and max coordinates of each part
            //together with the part weights of each part.
            for (int kk = 0; kk < current_concurrent_num_parts; ++kk) {
                mj_part_t current_work_part_in_concurrent_parts = current_work_part + kk;
 
                //if this part wont be partitioned any further
                //dont do any work for this part.
                if (num_partitioning_in_current_dim[current_work_part_in_concurrent_parts] == 1){
                    continue;
                }
                ++actual_work_part_count;
                mj_lno_t coordinate_end_index= this->part_xadj[current_work_part_in_concurrent_parts];
                mj_lno_t coordinate_begin_index = current_work_part_in_concurrent_parts == 0 ?
                  0 : this->part_xadj[current_work_part_in_concurrent_parts - 1];
 
/*            
                std::cout << "\n\ni:" << i << " j:" << current_work_part + kk
                                << " coordinate_begin_index:" << coordinate_begin_index
                                << " coordinate_end_index:" << coordinate_end_index
                                << " total:" << coordinate_end_index - coordinate_begin_index << "\n\n";
*/   
 
 
                if (partition_along_longest_dim) {
 
                  mj_scalar_t best_weight_coord = 0;
                  for (int coord_traverse_ind = 0; coord_traverse_ind < this->coord_dim; ++coord_traverse_ind){
                    mj_scalar_t best_min_coord = 0;
                    mj_scalar_t best_max_coord = 0;
                    //MD:same for all coordinates, but I will still use this for now.
 
                    this->mj_get_local_min_max_coord_totW(
                        coordinate_begin_index,
                        coordinate_end_index,
                        this->coordinate_permutations,
                        this->mj_coordinates[coord_traverse_ind],
                        best_min_coord, //min coordinate
                        best_max_coord, //max coordinate
                        best_weight_coord //total weight);
                    );
 
                    coord_dim_mins[coord_traverse_ind] = best_min_coord;
                    coord_dim_maxs[coord_traverse_ind] = best_max_coord;
                    mj_scalar_t best_range = best_max_coord - best_min_coord;
                    coord_dimension_range_sorted[coord_traverse_ind].id = coord_traverse_ind;
                    coord_dimension_range_sorted[coord_traverse_ind].val = best_range;
                    coord_dimension_range_sorted[coord_traverse_ind].signbit = 1;
                  }
 
 
                  uqSignsort(this->coord_dim, p_coord_dimension_range_sorted);
                  coordInd = p_coord_dimension_range_sorted[this->coord_dim - 1].id;
 
/*
                  std::cout << "\n\n"; 
                  for (int coord_traverse_ind = 0; coord_traverse_ind < this->coord_dim; ++coord_traverse_ind){
                    std::cout << "i:" << p_coord_dimension_range_sorted[coord_traverse_ind].id 
                      << " range:" << p_coord_dimension_range_sorted[coord_traverse_ind].val << std::endl;
                    std::cout << "i:" << p_coord_dimension_range_sorted[coord_traverse_ind].id 
                      << " coord_dim_mins:" << coord_dim_mins[p_coord_dimension_range_sorted[coord_traverse_ind].id]<< std::endl;
                    std::cout << "i:" << p_coord_dimension_range_sorted[coord_traverse_ind].id 
                      << " coord_dim_maxs:" << coord_dim_maxs[p_coord_dimension_range_sorted[coord_traverse_ind].id] << std::endl;
                  }
                  std::cout << "\n\n";
*/
 
                  mj_current_dim_coords = this->mj_coordinates[coordInd];
 
                  this->process_local_min_max_coord_total_weight[kk] = coord_dim_mins[coordInd];
                  this->process_local_min_max_coord_total_weight[kk+ current_concurrent_num_parts] = coord_dim_maxs[coordInd];
                  this->process_local_min_max_coord_total_weight[kk + 2*current_concurrent_num_parts] = best_weight_coord;
 
                }
                else{
                  this->mj_get_local_min_max_coord_totW(
                                  coordinate_begin_index,
                                  coordinate_end_index,
                                  this->coordinate_permutations,
                                  mj_current_dim_coords,
                                  this->process_local_min_max_coord_total_weight[kk], //min coordinate
                          this->process_local_min_max_coord_total_weight[kk + current_concurrent_num_parts], //max coordinate
                          this->process_local_min_max_coord_total_weight[kk + 2*current_concurrent_num_parts] //total weight);
                  );
                }
            }
 
            //1D partitioning
            if (actual_work_part_count > 0) {
                //obtain global Min max of the part.
                this->mj_get_global_min_max_coord_totW(
                                current_concurrent_num_parts,
                                this->process_local_min_max_coord_total_weight,
                                this->global_min_max_coord_total_weight);
 
                //represents the total number of cutlines
                //whose coordinate should be determined.
                mj_part_t total_incomplete_cut_count = 0;
 
                //Compute weight ratios for parts & cuts:
                //e.g., 0.25  0.25 0.5   0.5  0.75  0.75  1.0
                //      part0 cut0 part1 cut1 part2 cut2  part3
                mj_part_t concurrent_part_cut_shift = 0;
                mj_part_t concurrent_part_part_shift = 0;
 
 
                for (int kk = 0; kk < current_concurrent_num_parts; ++kk) {
                    mj_scalar_t min_coordinate = this->global_min_max_coord_total_weight[kk];
                    mj_scalar_t max_coordinate = this->global_min_max_coord_total_weight[kk +
                                                     current_concurrent_num_parts];
                    mj_scalar_t global_total_weight = this->global_min_max_coord_total_weight[kk +
                                                        2 * current_concurrent_num_parts];
 
                    mj_part_t concurrent_current_part_index = current_work_part + kk;
 
                    mj_part_t partition_count = num_partitioning_in_current_dim[concurrent_current_part_index];
 
                    mj_scalar_t *usedCutCoordinate = current_cut_coordinates + concurrent_part_cut_shift;
                    mj_scalar_t *current_target_part_weights = this->target_part_weights +
                                                                     concurrent_part_part_shift;
                    //shift the usedCutCoordinate array as noCuts.
                    concurrent_part_cut_shift += partition_count - 1;
                    //shift the partRatio array as noParts.
                    concurrent_part_part_shift += partition_count;
 
                    //calculate only if part is not empty,
                    //and part will be further partitioend.
                    if(partition_count > 1 && min_coordinate <= max_coordinate){
 
                        //increase allDone by the number of cuts of the current
                        //part's cut line number.
                        total_incomplete_cut_count += partition_count - 1;
                        //set the number of cut lines that should be determined
                        //for this part.
                        this->my_incomplete_cut_count[kk] = partition_count - 1;
 
                        // Nonuniform partitioning on the first level, providing
                        // requested number of parts (num_first_level_parts) and 
                        // requested distribution in parts (first_level_distribution)
                        if (i == 0 &&  
                            first_level_distribution != NULL &&
                            num_first_level_parts > 1) { 
                          // Get the target part weights given a desired distribution 
                          this->mj_get_initial_cut_coords_target_weights(
                              min_coordinate,
                              max_coordinate,
                              partition_count - 1,
                              global_total_weight,
                              usedCutCoordinate,
                              current_target_part_weights,
                              future_num_part_in_parts,
                              next_future_num_parts_in_parts,
                              concurrent_current_part_index,
                              obtained_part_index,
                              this->num_first_level_parts,
                              this->first_level_distribution);       
                        }
                        // Uniform partitioning
                        else {
                                                    
                          //get the target weights of the parts.
                          this->mj_get_initial_cut_coords_target_weights(
                              min_coordinate,
                              max_coordinate,
                              partition_count - 1,
                              global_total_weight,
                              usedCutCoordinate,
                              current_target_part_weights,
                              future_num_part_in_parts,
                              next_future_num_parts_in_parts,
                              concurrent_current_part_index,
                              obtained_part_index);
                        }
 
                        mj_lno_t coordinate_end_index = this->part_xadj[concurrent_current_part_index];
                        mj_lno_t coordinate_begin_index = concurrent_current_part_index == 0 ? 
                          0 : this->part_xadj[concurrent_current_part_index - 1];
 
                        //get the initial estimated part assignments of the coordinates.
                        this->set_initial_coordinate_parts(
                            max_coordinate,
                            min_coordinate,
                            concurrent_current_part_index,
                            coordinate_begin_index, coordinate_end_index,
                            this->coordinate_permutations,
                            mj_current_dim_coords,
                            this->assigned_part_ids,
                            partition_count);
 
                    }
                    else {
                        // e.g., if have fewer coordinates than parts, don't need to do next dim.
                        this->my_incomplete_cut_count[kk] = 0;
                    }
                    obtained_part_index += partition_count;
                }
 
                //used imbalance, it is always 0, as it is difficult to estimate a range.
                double used_imbalance = 0;
 
 
                // Determine cut lines for k parts here.
                this->mj_1D_part(
                    mj_current_dim_coords,
                    used_imbalance,
                    current_work_part,
                    current_concurrent_num_parts,
                    current_cut_coordinates,
                    total_incomplete_cut_count,
                    num_partitioning_in_current_dim);
            }
            else {
              obtained_part_index += current_concurrent_num_parts;
            }
 
            //create part chunks
            {
 
                mj_part_t output_array_shift = 0;
                mj_part_t cut_shift = 0;
                size_t tlr_shift = 0;
                size_t partweight_array_shift = 0;
 
                for(int kk = 0; kk < current_concurrent_num_parts; ++kk){
                    mj_part_t current_concurrent_work_part = current_work_part + kk;
                    mj_part_t num_parts = num_partitioning_in_current_dim[current_concurrent_work_part];
 
                    //if the part is empty, skip the part.
                    if((num_parts != 1  ) && this->global_min_max_coord_total_weight[kk] >
                             this->global_min_max_coord_total_weight[kk + current_concurrent_num_parts]) {
 
                        for(mj_part_t jj = 0; jj < num_parts; ++jj){
                            this->new_part_xadj[output_part_index + output_array_shift + jj] = 0;
                        }
                        cut_shift += num_parts - 1;
                        tlr_shift += (4 *(num_parts - 1) + 1);
                        output_array_shift += num_parts;
                        partweight_array_shift += (2 * (num_parts - 1) + 1);
                        continue;
                    }
 
                    mj_lno_t coordinate_end = this->part_xadj[current_concurrent_work_part];
                    mj_lno_t coordinate_begin = current_concurrent_work_part==0 ? 0: this->part_xadj[current_concurrent_work_part
                                                             -1];
                    mj_scalar_t *current_concurrent_cut_coordinate = current_cut_coordinates + cut_shift;
                    mj_scalar_t *used_local_cut_line_weight_to_left = this->process_cut_line_weight_to_put_left +
                                                         cut_shift;
 
                    for(int ii = 0; ii < this->num_threads; ++ii){
                        this->thread_part_weight_work[ii] = this->thread_part_weights[ii] +  partweight_array_shift;
                    }
 
                    if(num_parts > 1){
                        // Rewrite the indices based on the computed cuts.
                        this->create_consistent_chunks(
                            num_parts,
                            mj_current_dim_coords,
                            current_concurrent_cut_coordinate,
                            coordinate_begin,
                            coordinate_end,
                            used_local_cut_line_weight_to_left,
                            this->new_part_xadj + output_part_index + output_array_shift,
                            coordInd,
                            partition_along_longest_dim,
                            p_coord_dimension_range_sorted);
                    }
                    else {
                        //if this part is partitioned into 1 then just copy
                        //the old values.
                        mj_lno_t part_size = coordinate_end - coordinate_begin;
                        *(this->new_part_xadj + output_part_index + output_array_shift) = part_size;
                        memcpy(this->new_coordinate_permutations + coordinate_begin,
                        this->coordinate_permutations + coordinate_begin,
                        part_size * sizeof(mj_lno_t));
                    }
 
 
 
                    cut_shift += num_parts - 1;
                    tlr_shift += (4 *(num_parts - 1) + 1);
                    output_array_shift += num_parts;
                    partweight_array_shift += (2 * (num_parts - 1) + 1);
                }
 
                //shift cut coordinates so that all cut coordinates are stored.
                //current_cut_coordinates += cutShift;
 
                //getChunks from coordinates partitioned the parts and
                //wrote the indices as if there were a single part.
                //now we need to shift the beginning indices.
                for(mj_part_t kk = 0; kk < current_concurrent_num_parts; ++kk){
                    mj_part_t num_parts = num_partitioning_in_current_dim[ current_work_part + kk];
                    for (mj_part_t ii = 0;ii < num_parts ; ++ii){
                        //shift it by previousCount
                        this->new_part_xadj[output_part_index+ii] += output_coordinate_end_index;
                        if (ii % 2 == 1){
                          mj_lno_t coordinate_end = this->new_part_xadj[output_part_index+ii];
                          mj_lno_t coordinate_begin = this->new_part_xadj[output_part_index];
 
                          for (mj_lno_t task_traverse = coordinate_begin; task_traverse < coordinate_end; ++task_traverse){
                            mj_lno_t l = this->new_coordinate_permutations[task_traverse];
                            //MARKER: FLIPPED ZORDER BELOW
                            mj_current_dim_coords[l] = -mj_current_dim_coords[l];
                          }
                        }
                    }
                    //increase the previous count by current end.
                    output_coordinate_end_index = this->new_part_xadj[output_part_index + num_parts - 1];
                    //increase the current out.
                    output_part_index += num_parts ;
                }
            }
        }
        // end of this partitioning dimension
 
        //set the current num parts for next dim partitioning
        current_num_parts = output_part_count_in_dimension;
 
        //swap the coordinate permutations for the next dimension.
        mj_lno_t * tmp = this->coordinate_permutations;
        this->coordinate_permutations = this->new_coordinate_permutations;
        this->new_coordinate_permutations = tmp;
 
        freeArray<mj_lno_t>(this->part_xadj);
        this->part_xadj = this->new_part_xadj;
        this->new_part_xadj = NULL;
    }
 
    for(mj_lno_t i = 0; i < num_total_coords; ++i){
        inital_adjList_output_adjlist[i] = this->coordinate_permutations[i];
    }
 
    // Return output_xadj in CSR format
    output_xadj[0] = 0;
    for(size_t i = 0; i < this->num_global_parts ; ++i){
        output_xadj[i+1] = this->part_xadj[i];
    }
 
    delete future_num_part_in_parts;
    delete next_future_num_parts_in_parts;
 
    //free the extra memory that we allocated.
    freeArray<mj_part_t>(this->assigned_part_ids);
    freeArray<mj_gno_t>(this->initial_mj_gnos);
    freeArray<mj_gno_t>(this->current_mj_gnos);
    freeArray<bool>(tmp_mj_uniform_weights);
    freeArray<bool>(tmp_mj_uniform_parts);
    freeArray<mj_scalar_t *>(tmp_mj_weights);
    freeArray<mj_scalar_t *>(tmp_mj_part_sizes);
 
    this->free_work_memory();
 
#ifdef HAVE_ZOLTAN2_OMP
    //omp_set_num_threads(actual_num_threads);
#endif
}
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::AlgMJ():
        mj_env(), mj_problemComm(), imbalance_tolerance(0),
        part_no_array(NULL), recursion_depth(0), coord_dim(0),
        num_weights_per_coord(0), initial_num_loc_coords(0),
        initial_num_glob_coords(0),
        num_local_coords(0), num_global_coords(0), mj_coordinates(NULL),
        mj_weights(NULL), mj_uniform_parts(NULL), mj_part_sizes(NULL),
        mj_uniform_weights(NULL), mj_gnos(), num_global_parts(1),
        initial_mj_gnos(NULL), current_mj_gnos(NULL), owner_of_coordinate(NULL),
        coordinate_permutations(NULL), new_coordinate_permutations(NULL),
        assigned_part_ids(NULL), part_xadj(NULL), new_part_xadj(NULL),
        distribute_points_on_cut_lines(true), max_concurrent_part_calculation(1),
        mj_run_as_rcb(false), mj_user_recursion_depth(0), mj_keep_part_boxes(false),
        check_migrate_avoid_migration_option(0), migration_type(0), minimum_migration_imbalance(0.30),
        num_threads(1), num_first_level_parts(1), first_level_distribution(NULL), 
        total_num_cut(0), total_num_part(0), max_num_part_along_dim(0),
        max_num_cut_along_dim(0), max_num_total_part_along_dim(0), total_dim_num_reduce_all(0),
        last_dim_num_part(0), comm(), fEpsilon(0), sEpsilon(0), maxScalar_t(0), minScalar_t(0),
        all_cut_coordinates(NULL), max_min_coords(NULL), process_cut_line_weight_to_put_left(NULL),
        thread_cut_line_weight_to_put_left(NULL), cut_coordinates_work_array(NULL),
        target_part_weights(NULL), cut_upper_bound_coordinates(NULL), cut_lower_bound_coordinates(NULL),
        cut_lower_bound_weights(NULL), cut_upper_bound_weights(NULL),
        process_local_min_max_coord_total_weight(NULL), global_min_max_coord_total_weight(NULL),
        is_cut_line_determined(NULL), my_incomplete_cut_count(NULL),
        thread_part_weights(NULL), thread_part_weight_work(NULL),
        thread_cut_left_closest_point(NULL), thread_cut_right_closest_point(NULL),
        thread_point_counts(NULL), process_rectilinear_cut_weight(NULL),
        global_rectilinear_cut_weight(NULL),total_part_weight_left_right_closests(NULL),
        global_total_part_weight_left_right_closests(NULL),
        kept_boxes(),global_box(),
        myRank(0), myActualRank(0), divide_to_prime_first(false)
{
    this->fEpsilon = std::numeric_limits<float>::epsilon();
    this->sEpsilon = std::numeric_limits<mj_scalar_t>::epsilon() * 100;
 
    this->maxScalar_t = std::numeric_limits<mj_scalar_t>::max();
    this->minScalar_t = -std::numeric_limits<mj_scalar_t>::max();
 
}
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
RCP<typename AlgMJ<mj_scalar_t,mj_lno_t,mj_gno_t,mj_part_t>::mj_partBox_t>
AlgMJ<mj_scalar_t,mj_lno_t,mj_gno_t,mj_part_t>::get_global_box() const
{
  return this->global_box;
}
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::set_to_keep_part_boxes(){
  this->mj_keep_part_boxes = true;
}
 
 
/* \brief Either the mj array (part_no_array) or num_global_parts should be provided in
 * the input. part_no_array takes
 * precedence if both are provided.
 * Depending on these parameters, total cut/part number,
 * maximum part/cut number along a dimension, estimated number of reduceAlls,
 * and the number of parts before the last dimension is calculated.
 * */
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::set_part_specifications(){
 
        this->total_num_cut = 0; //how many cuts will be totally
        this->total_num_part = 1;    //how many parts will be totally
        this->max_num_part_along_dim = 0;         //maximum part count along a dimension.
        this->total_dim_num_reduce_all = 0;    //estimate on #reduceAlls can be done.
        this->last_dim_num_part = 1; //max no of parts that might occur
        //during the partition before the
        //last partitioning dimension.
        this->max_num_cut_along_dim = 0;
        this->max_num_total_part_along_dim = 0;
 
        if (this->part_no_array) {
                //if user provided part array, traverse the array and set variables.
                for (int i = 0; i < this->recursion_depth; ++i){
                        this->total_dim_num_reduce_all += this->total_num_part;
                        this->total_num_part *= this->part_no_array[i];
                        if(this->part_no_array[i] > this->max_num_part_along_dim) {
                                this->max_num_part_along_dim = this->part_no_array[i];
                        }
                }
                this->last_dim_num_part = this->total_num_part / this->part_no_array[recursion_depth-1];
                this->num_global_parts = this->total_num_part;
        } 
        else {
                mj_part_t future_num_parts = this->num_global_parts;
 
                // If using nonuniform first level partitioning.
                // initial value max_num_part_along_dim == num_first_level_parts  
                if (this->first_level_distribution != NULL && 
                    this->num_first_level_parts > 1) {
                  this->max_num_part_along_dim = this->num_first_level_parts;
                }
 
                // We need to calculate the part numbers now, to determine the maximum along the dimensions.
                for (int rd = 0; rd < this->recursion_depth; ++rd){
 
                        mj_part_t maxNoPartAlongI = 0;
                        mj_part_t nfutureNumParts = 0;
 
                        // Nonuniform first level partitioning sets part specificiations for rd == 0 only,
                        // given requested num of parts and distribution in parts for the first level.
                        if (rd == 0 &&
                            this->first_level_distribution != NULL &&
                            this->num_first_level_parts > 1) {
                                  
                          maxNoPartAlongI = this->num_first_level_parts;              
                          this->max_num_part_along_dim = this->num_first_level_parts;
                        
                          mj_part_t sum_first_level_dist = 0;
                          mj_part_t max_part = 0; 
 
                          // Cumulative sum of distribution of parts and size of largest part 
                          for (int i = 0; i < this->num_first_level_parts; ++i) {
                           
                            sum_first_level_dist += this->first_level_distribution[i];
                          
                            if (this->first_level_distribution[i] > max_part)
                              max_part = this->first_level_distribution[i];
                          }
 
                          // Total parts in largest nonuniform superpart from first level partitioning
                          nfutureNumParts = this->num_global_parts * max_part / sum_first_level_dist; 
                        
                        }
                        // Standard uniform partitioning this level
                        else {
                          maxNoPartAlongI = this->get_part_count(future_num_parts, 
                                                                 1.0f / (this->recursion_depth - rd));
                        
                          if (maxNoPartAlongI > this->max_num_part_along_dim)
                            this->max_num_part_along_dim = maxNoPartAlongI;
                        
 
                          nfutureNumParts = future_num_parts / maxNoPartAlongI;
                          if (future_num_parts % maxNoPartAlongI){
                                  ++nfutureNumParts;
                          }
                        }
 
                        future_num_parts = nfutureNumParts;
                }
                this->total_num_part = this->num_global_parts;
 
                if (this->divide_to_prime_first){
                  this->total_dim_num_reduce_all = this->num_global_parts * 2;
                  this->last_dim_num_part = this->num_global_parts;
                }
                else {
                  //this is the lower bound.
 
                  //estimate reduceAll Count here.
                  //we find the upperbound instead.
                  size_t p = 1;
 
                  for (int i = 0; i < this->recursion_depth; ++i){
                    this->total_dim_num_reduce_all += p;
                    p *= this->max_num_part_along_dim;
                  }
 
                  if (p / this->max_num_part_along_dim > this->num_global_parts){
                    this->last_dim_num_part = this->num_global_parts;
                  }
                  else {
                    this->last_dim_num_part  = p / this->max_num_part_along_dim;
                  }
 
                }
        }
 
        this->total_num_cut = this->total_num_part - 1;
        this->max_num_cut_along_dim = this->max_num_part_along_dim - 1;
        this->max_num_total_part_along_dim = this->max_num_part_along_dim + size_t(this->max_num_cut_along_dim);
        //maxPartNo is P, maxCutNo = P-1, matTotalPartcount = 2P-1
 
        //refine the concurrent part count, if it is given bigger than the maximum possible part count.
    if(this->max_concurrent_part_calculation > this->last_dim_num_part){
        if(this->mj_problemComm->getRank() == 0){
            std::cerr << "Warning: Concurrent part count ("<< this->max_concurrent_part_calculation <<
            ") has been set bigger than maximum amount that can be used." <<
            " Setting to:" << this->last_dim_num_part << "." << std::endl;
        }
        this->max_concurrent_part_calculation = this->last_dim_num_part;
    }
 
}
/* \brief Tries to determine the part number for current dimension,
 * by trying to make the partitioning as square as possible.
 * \param num_total_future how many more partitionings are required.
 * \param root how many more recursion depth is left.
 */
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
inline mj_part_t AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::get_part_count(
                mj_part_t num_total_future,
                double root)
{
        double fp = pow(num_total_future, root);
        mj_part_t ip = mj_part_t (fp);
        if (fp - ip < this->fEpsilon * 100){
                return ip;
        }
        else {
                return ip  + 1;
        }
}
 
/* \brief Function returns how many parts that will be obtained after this dimension partitioning.
 * It sets how many parts each current part will be partitioned into in this dimension to num_partitioning_in_current_dim vector,
 * sets how many total future parts each obtained part will be partitioned into in next_future_num_parts_in_parts vector,
 * If part boxes are kept, then sets initializes the output_part_boxes as its ancestor.
 *
 *  \param num_partitioning_in_current_dim: output. How many parts each current part will be partitioned into.
 *  \param future_num_part_in_parts: input, how many future parts each current part will be partitioned into.
 *  \param next_future_num_parts_in_parts: output, how many future parts each obtained part will be partitioned into.
 *  \param future_num_parts: input/output, max number of future parts that will be obtained from a single
 *  \param current_num_parts: input, how many parts are there currently.
 *  \param current_iteration: input, current dimension iteration number.
 *  \param input_part_boxes: input, if boxes are kept, current boxes.
 *  \param output_part_boxes: output, if boxes are kept, the initial box boundaries for obtained parts.
 */
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
mj_part_t AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::update_part_num_arrays(
    std::vector <mj_part_t> &num_partitioning_in_current_dim, //assumes this vector is empty.
    std::vector<mj_part_t> *future_num_part_in_parts,
    std::vector<mj_part_t> *next_future_num_parts_in_parts, //assumes this vector is empty.
    mj_part_t &future_num_parts,
    mj_part_t current_num_parts,
    int current_iteration,
    RCP<mj_partBoxVector_t> input_part_boxes,
    RCP<mj_partBoxVector_t> output_part_boxes,
    mj_part_t atomic_part_count) {
 
    //how many parts that will be obtained after this dimension.
    mj_part_t output_num_parts = 0;
 
    if(this->part_no_array){
        //when the partNo array is provided as input,
        //each current partition will be partition to the same number of parts.
        //we dont need to use the future_num_part_in_parts vector in this case.
 
        mj_part_t p = this->part_no_array[current_iteration];
        if (p < 1){
            std::cout << "Current recursive iteration: " << current_iteration 
              << " part_no_array[" << current_iteration << "] is given as:" << p << std::endl;
            exit(1);
        }
        if (p == 1){
            return current_num_parts;
        }
        // If using part_no_array, ensure compatibility with num_first_level_parts.
        if (this->first_level_distribution != NULL && 
            current_iteration == 0 && 
            p != this->num_first_level_parts)
        {
            std::cout << "Current recursive iteration: " << current_iteration 
              << " part_no_array[" << current_iteration << "] is given as: " << p 
              << " and contradicts num_first_level_parts: " << this->num_first_level_parts << std::endl;
            exit(1);
        }
 
        for (mj_part_t ii = 0; ii < current_num_parts; ++ii){
            num_partitioning_in_current_dim.push_back(p);
        }
 
/*       
        std::cout << "\n\nme: " << this->myRank << " current_iteration: " << current_iteration 
          << " current_num_parts: " << current_num_parts << "\n\n";
        
        std::cout << "\n\nnum_partitioning_in_current_dim[0]: " << num_partitioning_in_current_dim[0] << "\n\n";
        
        //set the new value of future_num_parts.
        
        std::cout << "\n\nfuture_num_parts: " << future_num_parts
          << " num_partitioning_in_current_dim[0]: " << num_partitioning_in_current_dim[0]
          << " " << future_num_parts / num_partitioning_in_current_dim[0] << "\n\n";
*/
 
        future_num_parts /= num_partitioning_in_current_dim[0];
        output_num_parts = current_num_parts * num_partitioning_in_current_dim[0];
 
        if (this->mj_keep_part_boxes){
            for (mj_part_t k = 0; k < current_num_parts; ++k){
                //initialized the output boxes as its ancestor.
                for (mj_part_t j = 0; j < num_partitioning_in_current_dim[0]; ++j){
                    output_part_boxes->push_back((*input_part_boxes)[k]);
                }
            }
        }
 
        //set the how many more parts each part will be divided.
        //this is obvious when partNo array is provided as input.
        //however, fill this so that weights will be calculated according to this array.
        for (mj_part_t ii = 0; ii < output_num_parts; ++ii){
            next_future_num_parts_in_parts->push_back(future_num_parts);
        }
    }
    else {
        //if partNo array is not provided as input,
        //future_num_part_in_parts  holds how many parts each part should be divided.
        //initially it holds a single number equal to the total number of global parts.
 
        //calculate the future_num_parts from beginning,
        //since each part might be divided into different number of parts.
        future_num_parts = 1;
 
        //std::cout << "i:" << i << std::endl;
 
        for (mj_part_t ii = 0; ii < current_num_parts; ++ii){
            //get how many parts a part should be divided.
            mj_part_t future_num_parts_of_part_ii = (*future_num_part_in_parts)[ii];
 
            //get the ideal number of parts that is close to the
            //(recursion_depth - i) root of the future_num_parts_of_part_ii.
            mj_part_t num_partitions_in_current_dim =
              this->get_part_count(future_num_parts_of_part_ii,
                                   1.0 / (this->recursion_depth - current_iteration) );
 
            if (num_partitions_in_current_dim > this->max_num_part_along_dim){
                std::cerr << "ERROR: maxPartNo calculation is wrong. num_partitions_in_current_dim: "
                          << num_partitions_in_current_dim <<  " this->max_num_part_along_dim: "
                          << this->max_num_part_along_dim <<
                          " this->recursion_depth: " << this->recursion_depth <<
                          " current_iteration: " << current_iteration <<
                          " future_num_parts_of_part_ii: " << future_num_parts_of_part_ii <<
                          " might need to fix max part no calculation for largest_prime_first partitioning." <<
                          std::endl;
                exit(1);
            }
            //add this number to num_partitioning_in_current_dim vector.
//            num_partitioning_in_current_dim.push_back(num_partitions_in_current_dim);
 
//            mj_part_t largest_prime_factor = num_partitions_in_current_dim;
              
            // Update part num arrays when on current_iteration == 0 and 
            // using nonuniform first level partitioning
            // with requested num parts (num_first_level_parts) and
            // a requested distribution in parts (first_level_distribution).
            if (current_iteration == 0 && 
                this->first_level_distribution != NULL && 
                this->num_first_level_parts > 1) {
 
              // Only 1 current part to begin and partitions into 
              // num_first_level_parts many parts 
              num_partitioning_in_current_dim.push_back(this->num_first_level_parts);
 
              // The output number of parts from first level partitioning
              output_num_parts = this->num_first_level_parts;
              
              // Remaining parts left to partition for all future levels
              future_num_parts /= this->num_first_level_parts;
 
              mj_part_t max_part = 0;
              mj_part_t sum_first_level_dist = 0;
             
              // Cumulative sum of distribution of first level parts 
              // and size of largest first level part 
              for (int i = 0; i < this->num_first_level_parts; ++i) {
                sum_first_level_dist += this->first_level_distribution[i];
 
                if (this->first_level_distribution[i] > max_part)
                  max_part = this->first_level_distribution[i];
              }
 
              // Maximum # of remaining parts left to partition for all future levels
              future_num_parts = this->num_global_parts * max_part / sum_first_level_dist;
              
              // Number of parts remaining left to partition for each future_part
              // The sum must exactly equal global_num_parts 
              for (int i = 0; i < this->num_first_level_parts; ++i) {
 
                next_future_num_parts_in_parts->push_back(this->first_level_distribution[i] * 
                    this->num_global_parts / sum_first_level_dist);
              }              
            }
            else if (this->divide_to_prime_first) {
 
              // Add this number to num_partitioning_in_current_dim vector.
              num_partitioning_in_current_dim.push_back(num_partitions_in_current_dim);
              
              mj_part_t largest_prime_factor = num_partitions_in_current_dim;
 
              //increase the output number of parts.
              output_num_parts += num_partitions_in_current_dim;
              
              if (future_num_parts_of_part_ii == atomic_part_count || 
                  future_num_parts_of_part_ii % atomic_part_count != 0) {
                atomic_part_count = 1;
              }
 
              largest_prime_factor = 
                this->find_largest_prime_factor(future_num_parts_of_part_ii / atomic_part_count);
 
              // We divide to  num_partitions_in_current_dim. But we adjust the weights 
              // based on largest prime/ if num_partitions_in_current_dim = 2, 
              // largest prime = 5 --> we divide to 2 parts with weights 3x and 2x.
              // if the largest prime is less than part count, we use the part count 
              // so that we divide uniformly.
              if (largest_prime_factor < num_partitions_in_current_dim) {
                largest_prime_factor = num_partitions_in_current_dim;
              }
 
              //ideal number of future partitions for each part.
              mj_part_t ideal_num_future_parts_in_part = 
                (future_num_parts_of_part_ii / atomic_part_count) / largest_prime_factor;
              //if num_partitions_in_current_dim = 2, largest prime = 5 then ideal weight is 2x
              mj_part_t ideal_prime_scale = largest_prime_factor / num_partitions_in_current_dim;
 
/*
              std::cout << "\ncurrent num part: " << ii 
                << " largest_prime_factor: " << largest_prime_factor 
                << " To Partition: " << future_num_parts_of_part_ii << "\n\n";
*/
 
              for (mj_part_t iii = 0; iii < num_partitions_in_current_dim; ++iii) {
                //if num_partitions_in_current_dim = 2, largest prime = 5 then ideal weight is 2x
                mj_part_t my_ideal_primescale = ideal_prime_scale;
                //left over weighs. Left side is adjusted to be 3x, right side stays as 2x
                if (iii < (largest_prime_factor) % num_partitions_in_current_dim) {
                  ++my_ideal_primescale;
                }
                //scale with 'x';
                mj_part_t num_future_parts_for_part_iii = 
                  ideal_num_future_parts_in_part * my_ideal_primescale;
 
                //if there is a remainder in the part increase the part weight.
                if (iii < (future_num_parts_of_part_ii / atomic_part_count) % largest_prime_factor) {
                  //if not uniform, add 1 for the extra parts.
                  ++num_future_parts_for_part_iii;
                }
 
                next_future_num_parts_in_parts->push_back(num_future_parts_for_part_iii * atomic_part_count);
 
                //if part boxes are stored, initialize the box of the parts as the ancestor.
                if (this->mj_keep_part_boxes) {
                  output_part_boxes->push_back((*input_part_boxes)[ii]);
                }
 
                //set num future_num_parts to maximum in this part.
                if (num_future_parts_for_part_iii > future_num_parts) 
                  future_num_parts = num_future_parts_for_part_iii;
 
              }
            }
            else {
 
              // Add this number to num_partitioning_in_current_dim vector.
              num_partitioning_in_current_dim.push_back(num_partitions_in_current_dim);
              
              //increase the output number of parts.
              output_num_parts += num_partitions_in_current_dim;
 
              if (future_num_parts_of_part_ii == atomic_part_count || 
                  future_num_parts_of_part_ii % atomic_part_count != 0) {
                atomic_part_count = 1;
              }
              //ideal number of future partitions for each part.
              mj_part_t ideal_num_future_parts_in_part = 
                (future_num_parts_of_part_ii / atomic_part_count) / num_partitions_in_current_dim;
              
              for (mj_part_t iii = 0; iii < num_partitions_in_current_dim; ++iii){
                mj_part_t num_future_parts_for_part_iii = ideal_num_future_parts_in_part;
 
                //if there is a remainder in the part increase the part weight.
                if (iii < (future_num_parts_of_part_ii / atomic_part_count) % num_partitions_in_current_dim){
                  //if not uniform, add 1 for the extra parts.
                  ++num_future_parts_for_part_iii;
                }
 
                next_future_num_parts_in_parts->push_back(num_future_parts_for_part_iii * atomic_part_count);
 
                //if part boxes are stored, initialize the box of the parts as the ancestor.
                if (this->mj_keep_part_boxes){
                  output_part_boxes->push_back((*input_part_boxes)[ii]);
                }
 
                //set num future_num_parts to maximum in this part.
                if (num_future_parts_for_part_iii > future_num_parts) 
                  future_num_parts = num_future_parts_for_part_iii;
              }
            }
        }
    }
    return output_num_parts;
}
 
 
/* \brief Allocates and initializes the work memory that will be used by MJ.
 *
 * */
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::allocate_set_work_memory(){
 
        //points to process that initially owns the coordinate.
        this->owner_of_coordinate  = NULL;
 
        //Throughout the partitioning execution,
        //instead of the moving the coordinates, hold a permutation array for parts.
        //coordinate_permutations holds the current permutation.
        this->coordinate_permutations =  allocMemory< mj_lno_t>(this->num_local_coords);
        //initial configuration, set each pointer-i to i.
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp parallel for
#endif
        for(mj_lno_t i = 0; i < this->num_local_coords; ++i){
                this->coordinate_permutations[i] = i;
        }
 
        //new_coordinate_permutations holds the current permutation.
        this->new_coordinate_permutations = allocMemory< mj_lno_t>(this->num_local_coords);
 
        this->assigned_part_ids = NULL;
        if(this->num_local_coords > 0){
                this->assigned_part_ids = allocMemory<mj_part_t>(this->num_local_coords);
        }
 
        //single partition starts at index-0, and ends at numLocalCoords
        //inTotalCounts array holds the end points in coordinate_permutations array
        //for each partition. Initially sized 1, and single element is set to numLocalCoords.
        this->part_xadj = allocMemory<mj_lno_t>(1);
        this->part_xadj[0] = static_cast<mj_lno_t>(this->num_local_coords);//the end of the initial partition is the end of coordinates.
        //the ends points of the output, this is allocated later.
        this->new_part_xadj = NULL;
 
        // only store this much if cuts are needed to be stored.
        //this->all_cut_coordinates = allocMemory< mj_scalar_t>(this->total_num_cut);
 
 
        this->all_cut_coordinates  = allocMemory< mj_scalar_t>(this->max_num_cut_along_dim * this->max_concurrent_part_calculation);
 
        this->max_min_coords =  allocMemory< mj_scalar_t>(this->num_threads * 2);
 
        this->process_cut_line_weight_to_put_left = NULL; //how much weight percentage should a MPI put left side of the each cutline
        this->thread_cut_line_weight_to_put_left = NULL; //how much weight percentage should each thread in MPI put left side of the each outline
        //distribute_points_on_cut_lines = false;
        if(this->distribute_points_on_cut_lines){
                this->process_cut_line_weight_to_put_left = allocMemory<mj_scalar_t>(this->max_num_cut_along_dim * this->max_concurrent_part_calculation);
                this->thread_cut_line_weight_to_put_left = allocMemory<mj_scalar_t *>(this->num_threads);
                for(int i = 0; i < this->num_threads; ++i){
                        this->thread_cut_line_weight_to_put_left[i] = allocMemory<mj_scalar_t>(this->max_num_cut_along_dim);
                }
            this->process_rectilinear_cut_weight = allocMemory<mj_scalar_t>(this->max_num_cut_along_dim);
            this->global_rectilinear_cut_weight = allocMemory<mj_scalar_t>(this->max_num_cut_along_dim);
        }
 
 
        // work array to manipulate coordinate of cutlines in different iterations.
        //necessary because previous cut line information is used for determining
        //the next cutline information. therefore, cannot update the cut work array
        //until all cutlines are determined.
        this->cut_coordinates_work_array = allocMemory<mj_scalar_t>(this->max_num_cut_along_dim *
                        this->max_concurrent_part_calculation);
 
 
        //cumulative part weight array.
        this->target_part_weights = allocMemory<mj_scalar_t>(
                                        this->max_num_part_along_dim * this->max_concurrent_part_calculation);
        // the weight from left to write.
 
    this->cut_upper_bound_coordinates = allocMemory<mj_scalar_t>(this->max_num_cut_along_dim * this->max_concurrent_part_calculation);  //upper bound coordinate of a cut line
    this->cut_lower_bound_coordinates = allocMemory<mj_scalar_t>(this->max_num_cut_along_dim* this->max_concurrent_part_calculation);  //lower bound coordinate of a cut line
    this->cut_lower_bound_weights = allocMemory<mj_scalar_t>(this->max_num_cut_along_dim* this->max_concurrent_part_calculation);  //lower bound weight of a cut line
    this->cut_upper_bound_weights = allocMemory<mj_scalar_t>(this->max_num_cut_along_dim* this->max_concurrent_part_calculation);  //upper bound weight of a cut line
 
    this->process_local_min_max_coord_total_weight = allocMemory<mj_scalar_t>(3 * this->max_concurrent_part_calculation); //combined array to exchange the min and max coordinate, and total weight of part.
    this->global_min_max_coord_total_weight = allocMemory<mj_scalar_t>(3 * this->max_concurrent_part_calculation);//global combined array with the results for min, max and total weight.
 
    //is_cut_line_determined is used to determine if a cutline is determined already.
    //If a cut line is already determined, the next iterations will skip this cut line.
    this->is_cut_line_determined = allocMemory<bool>(this->max_num_cut_along_dim * this->max_concurrent_part_calculation);
    //my_incomplete_cut_count count holds the number of cutlines that have not been finalized for each part
    //when concurrentPartCount>1, using this information, if my_incomplete_cut_count[x]==0, then no work is done for this part.
    this->my_incomplete_cut_count =  allocMemory<mj_part_t>(this->max_concurrent_part_calculation);
    //local part weights of each thread.
    this->thread_part_weights = allocMemory<double *>(this->num_threads);
    //the work manupulation array for partweights.
    this->thread_part_weight_work = allocMemory<double *>(this->num_threads);
 
    //thread_cut_left_closest_point to hold the closest coordinate to a cutline from left (for each thread).
    this->thread_cut_left_closest_point = allocMemory<mj_scalar_t *>(this->num_threads);
    //thread_cut_right_closest_point to hold the closest coordinate to a cutline from right (for each thread)
    this->thread_cut_right_closest_point = allocMemory<mj_scalar_t *>(this->num_threads);
 
    //to store how many points in each part a thread has.
    this->thread_point_counts = allocMemory<mj_lno_t *>(this->num_threads);
 
    for(int i = 0; i < this->num_threads; ++i){
        //partWeights[i] = allocMemory<mj_scalar_t>(maxTotalPartCount);
        this->thread_part_weights[i] = allocMemory < double >(this->max_num_total_part_along_dim * this->max_concurrent_part_calculation);
        this->thread_cut_right_closest_point[i] = allocMemory<mj_scalar_t>(this->max_num_cut_along_dim * this->max_concurrent_part_calculation);
        this->thread_cut_left_closest_point[i] = allocMemory<mj_scalar_t>(this->max_num_cut_along_dim * this->max_concurrent_part_calculation);
        this->thread_point_counts[i] =  allocMemory<mj_lno_t>(this->max_num_part_along_dim);
    }
    //for faster communication, concatanation of
    //totalPartWeights sized 2P-1, since there are P parts and P-1 cut lines
    //leftClosest distances sized P-1, since P-1 cut lines
    //rightClosest distances size P-1, since P-1 cut lines.
    this->total_part_weight_left_right_closests = allocMemory<mj_scalar_t>((this->max_num_total_part_along_dim + this->max_num_cut_along_dim * 2) * this->max_concurrent_part_calculation);
    this->global_total_part_weight_left_right_closests = allocMemory<mj_scalar_t>((this->max_num_total_part_along_dim + this->max_num_cut_along_dim * 2) * this->max_concurrent_part_calculation);
 
 
    mj_scalar_t **coord = allocMemory<mj_scalar_t *>(this->coord_dim);
    for (int i=0; i < this->coord_dim; i++){
        coord[i] = allocMemory<mj_scalar_t>(this->num_local_coords);
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp parallel for
#endif
        for (mj_lno_t j=0; j < this->num_local_coords; j++)
                coord[i][j] = this->mj_coordinates[i][j];
    }
    this->mj_coordinates = coord;
 
 
    int criteria_dim = (this->num_weights_per_coord ? this->num_weights_per_coord : 1);
    mj_scalar_t **weights = allocMemory<mj_scalar_t *>(criteria_dim);
 
    for (int i=0; i < criteria_dim; i++){
        weights[i] = NULL;
    }
    for (int i=0; i < this->num_weights_per_coord; i++){
        weights[i] = allocMemory<mj_scalar_t>(this->num_local_coords);
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp parallel for
#endif
        for (mj_lno_t j=0; j < this->num_local_coords; j++)
                weights[i][j] = this->mj_weights[i][j];
 
    }
        this->mj_weights = weights;
    this->current_mj_gnos = allocMemory<mj_gno_t>(this->num_local_coords);
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp parallel for
#endif
    for (mj_lno_t j=0; j < this->num_local_coords; j++)
        this->current_mj_gnos[j] = this->initial_mj_gnos[j];
 
    this->owner_of_coordinate = allocMemory<int>(this->num_local_coords);
 
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp parallel for
#endif
    for (mj_lno_t j=0; j < this->num_local_coords; j++)
        this->owner_of_coordinate[j] = this->myActualRank;
}
 
/* \brief compute the global bounding box
 */
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t,mj_lno_t,mj_gno_t,mj_part_t>::compute_global_box()
{
    //local min coords
    mj_scalar_t *mins = allocMemory<mj_scalar_t>(this->coord_dim);
    //global min coords
    mj_scalar_t *gmins = allocMemory<mj_scalar_t>(this->coord_dim);
    //local max coords
    mj_scalar_t *maxs = allocMemory<mj_scalar_t>(this->coord_dim);
    //global max coords
    mj_scalar_t *gmaxs = allocMemory<mj_scalar_t>(this->coord_dim);
 
    for (int i = 0; i < this->coord_dim; ++i){
        mj_scalar_t localMin = std::numeric_limits<mj_scalar_t>::max();
        mj_scalar_t localMax = -localMin;
        if (localMax > 0) localMax = 0;
 
 
        for (mj_lno_t j = 0; j < this->num_local_coords; ++j){
            if (this->mj_coordinates[i][j] < localMin){
                localMin = this->mj_coordinates[i][j];
            }
            if (this->mj_coordinates[i][j] > localMax){
                localMax = this->mj_coordinates[i][j];
            }
        }
        //std::cout << " localMin:" << localMin << std::endl;
        //std::cout << " localMax:" << localMax << std::endl;
        mins[i] = localMin;
        maxs[i] = localMax;
 
    }
    reduceAll<int, mj_scalar_t>(*this->comm, Teuchos::REDUCE_MIN,
            this->coord_dim, mins, gmins
    );
 
 
    reduceAll<int, mj_scalar_t>(*this->comm, Teuchos::REDUCE_MAX,
            this->coord_dim, maxs, gmaxs
    );
 
 
 
    //create single box with all areas.
    global_box = rcp(new mj_partBox_t(0,this->coord_dim,gmins,gmaxs));
    //coordinateModelPartBox <mj_scalar_t, mj_part_t> tmpBox (0, coordDim);
    freeArray<mj_scalar_t>(mins);
    freeArray<mj_scalar_t>(gmins);
    freeArray<mj_scalar_t>(maxs);
    freeArray<mj_scalar_t>(gmaxs);
}
 
/* \brief for part communication we keep track of the box boundaries.
 * This is performed when either asked specifically, or when geometric mapping is performed afterwards.
 * This function initializes a single box with all global min and max coordinates.
 * \param initial_partitioning_boxes the input and output vector for boxes.
 */
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::init_part_boxes(
                RCP<mj_partBoxVector_t> & initial_partitioning_boxes
)
{
    mj_partBox_t tmp_box(*global_box);
    initial_partitioning_boxes->push_back(tmp_box);
}
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::mj_get_local_min_max_coord_totW(
                mj_lno_t coordinate_begin_index,
                mj_lno_t coordinate_end_index,
                mj_lno_t *mj_current_coordinate_permutations,
                mj_scalar_t *mj_current_dim_coords,
                mj_scalar_t &min_coordinate,
                mj_scalar_t &max_coordinate,
                mj_scalar_t &total_weight){
 
    //if the part is empty.
    //set the min and max coordinates as reverse.
    if(coordinate_begin_index >= coordinate_end_index)
    {
        min_coordinate = this->maxScalar_t;
        max_coordinate = this->minScalar_t;
        total_weight = 0;
    }
    else {
        mj_scalar_t my_total_weight = 0;
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp parallel num_threads(this->num_threads)
#endif
        {
            //if uniform weights are used, then weight is equal to count.
            if (this->mj_uniform_weights[0]) {
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp single
#endif
                {
                    my_total_weight = coordinate_end_index - coordinate_begin_index;
                }
 
            }
            else {
                //if not uniform, then weights are reducted from threads.
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp for reduction(+:my_total_weight)
#endif
                for (mj_lno_t ii = coordinate_begin_index; ii < coordinate_end_index; ++ii){
                    int i = mj_current_coordinate_permutations[ii];
                    my_total_weight += this->mj_weights[0][i];
                }
            }
 
            int my_thread_id = 0;
#ifdef HAVE_ZOLTAN2_OMP
            my_thread_id = omp_get_thread_num();
#endif
            mj_scalar_t my_thread_min_coord, my_thread_max_coord;
            my_thread_min_coord=my_thread_max_coord
                =mj_current_dim_coords[mj_current_coordinate_permutations[coordinate_begin_index]];
 
 
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp for
#endif
            for(mj_lno_t j = coordinate_begin_index + 1; j < coordinate_end_index; ++j){
                int i = mj_current_coordinate_permutations[j];
                if(mj_current_dim_coords[i] > my_thread_max_coord)
                    my_thread_max_coord = mj_current_dim_coords[i];
                if(mj_current_dim_coords[i] < my_thread_min_coord)
                    my_thread_min_coord = mj_current_dim_coords[i];
            }
            this->max_min_coords[my_thread_id] = my_thread_min_coord;
            this->max_min_coords[my_thread_id + this->num_threads] = my_thread_max_coord;
 
#ifdef HAVE_ZOLTAN2_OMP
//we need a barrier here, because max_min_array might not be filled by some of the threads.
#pragma omp barrier
#pragma omp single nowait
#endif
            {
                min_coordinate = this->max_min_coords[0];
                for(int i = 1; i < this->num_threads; ++i){
                    if(this->max_min_coords[i] < min_coordinate)
                        min_coordinate = this->max_min_coords[i];
                }
            }
 
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp single nowait
#endif
            {
                max_coordinate = this->max_min_coords[this->num_threads];
                for(int i = this->num_threads + 1; i < this->num_threads * 2; ++i){
                    if(this->max_min_coords[i] > max_coordinate)
                        max_coordinate = this->max_min_coords[i];
                }
            }
        }
        total_weight = my_total_weight;
    }
}
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::mj_get_global_min_max_coord_totW(
    mj_part_t current_concurrent_num_parts,
    mj_scalar_t *local_min_max_total,
    mj_scalar_t *global_min_max_total){
 
        //reduce min for first current_concurrent_num_parts elements, reduce max for next
        //concurrentPartCount elements,
        //reduce sum for the last concurrentPartCount elements.
        if(this->comm->getSize()  > 1){
                Teuchos::MultiJaggedCombinedMinMaxTotalReductionOp<int, mj_scalar_t>
                        reductionOp(
                                        current_concurrent_num_parts,
                                        current_concurrent_num_parts,
                                        current_concurrent_num_parts);
                try{
                        reduceAll<int, mj_scalar_t>(
                                        *(this->comm),
                                        reductionOp,
                                        3 * current_concurrent_num_parts,
                                        local_min_max_total,
                                        global_min_max_total);
                }
                Z2_THROW_OUTSIDE_ERROR(*(this->mj_env))
        }
        else {
                mj_part_t s = 3 * current_concurrent_num_parts;
                for (mj_part_t i = 0; i < s; ++i){
                        global_min_max_total[i] = local_min_max_total[i];
                }
        }
}
 
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::mj_get_initial_cut_coords_target_weights(
    mj_scalar_t min_coord,
    mj_scalar_t max_coord,
    mj_part_t num_cuts/*p-1*/ ,
    mj_scalar_t global_weight,
    mj_scalar_t *initial_cut_coords /*p - 1 sized, coordinate of each cut line*/,
    mj_scalar_t *current_target_part_weights /*cumulative weights, at left side of each cut line. p-1 sized*/,
 
    std::vector <mj_part_t> *future_num_part_in_parts, //the vecto
    std::vector <mj_part_t> *next_future_num_parts_in_parts,
    mj_part_t concurrent_current_part,
    mj_part_t obtained_part_index,
    mj_part_t num_target_first_level_parts,
    const mj_part_t *target_first_level_dist) {
 
    mj_scalar_t coord_range = max_coord - min_coord;
 
    // Uniform target weights
    if (num_target_first_level_parts <= 1 && 
        this->mj_uniform_parts[0]) {
      {
        mj_part_t cumulative = 0;
            
        // How many total future parts the part will be partitioned into.
        mj_scalar_t total_future_part_count_in_part = mj_scalar_t((*future_num_part_in_parts)[concurrent_current_part]);
 
        // How much each part should weigh in ideal case.
        mj_scalar_t unit_part_weight = global_weight / total_future_part_count_in_part;
 
        for (mj_part_t i = 0; i < num_cuts; ++i) {
          cumulative += (*next_future_num_parts_in_parts)[i + obtained_part_index];
                
          // Set target part weight.
          current_target_part_weights[i] = cumulative * unit_part_weight;
 
          // Set initial cut coordinate.
          initial_cut_coords[i] = min_coord + (coord_range * cumulative) / total_future_part_count_in_part;
        }
            
        current_target_part_weights[num_cuts] = global_weight;
      }
 
      // Round the target part weights.
      if (this->mj_uniform_weights[0]) { // Repeated if???
        for (mj_part_t i = 0; i < num_cuts + 1; ++i) {
          current_target_part_weights[i] = long(current_target_part_weights[i] + 0.5);
        }
      }
    }
    // Nonuniform target weights for first level of partitioning 
    else if(num_target_first_level_parts > 1 && 
            target_first_level_dist != NULL) {
      {
        // Running sum of the total weight
        mj_part_t cumulative = 0.0;
 
        // Sum of entries in the first level partition distribution vector
        mj_scalar_t sum_target_first_level_dist = 0.0;
 
        for (int i = 0; i < num_target_first_level_parts; ++i) {
          sum_target_first_level_dist += target_first_level_dist[i];
        }
        
        for (mj_part_t i = 0; i < num_cuts; ++i) {
          cumulative += global_weight * target_first_level_dist[i] / sum_target_first_level_dist;
 
          // Set target part weight.
          current_target_part_weights[i] = cumulative; 
 
          // Set initial cut coordinate.
          initial_cut_coords[i] = min_coord + (coord_range * cumulative) / global_weight;
        }
 
        current_target_part_weights[num_cuts] = global_weight;
      }
 
      //round the target part weights.
      for (mj_part_t i = 0; i < num_cuts + 1; ++i) {
        current_target_part_weights[i] = long(current_target_part_weights[i] + 0.5);
      }
    }
    else {
        std::cerr << "MJ does not support non uniform part weights beyond the first partition" << std::endl;
        exit(1);
    }
}
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::set_initial_coordinate_parts(
    mj_scalar_t &max_coordinate,
    mj_scalar_t &min_coordinate,
    mj_part_t &/* concurrent_current_part_index */,
    mj_lno_t coordinate_begin_index,
    mj_lno_t coordinate_end_index,
    mj_lno_t *mj_current_coordinate_permutations,
    mj_scalar_t *mj_current_dim_coords,
    mj_part_t *mj_part_ids,
    mj_part_t &partition_count
){
    mj_scalar_t coordinate_range = max_coordinate - min_coordinate;
 
    //if there is single point, or if all points are along a line.
    //set initial part to 0 for all.
    if(ZOLTAN2_ABS(coordinate_range) < this->sEpsilon ){
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp parallel for
#endif
        for(mj_lno_t ii = coordinate_begin_index; ii < coordinate_end_index; ++ii){
                mj_part_ids[mj_current_coordinate_permutations[ii]] = 0;
        }
    }
    else{
 
        //otherwise estimate an initial part for each coordinate.
        //assuming uniform distribution of points.
        mj_scalar_t slice = coordinate_range / partition_count;
 
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp parallel for
#endif
        for(mj_lno_t ii = coordinate_begin_index; ii < coordinate_end_index; ++ii){
 
            mj_lno_t iii = mj_current_coordinate_permutations[ii];
            mj_part_t pp = mj_part_t((mj_current_dim_coords[iii] - min_coordinate) / slice);
            mj_part_ids[iii] = 2 * pp;
        }
    }
}
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::mj_1D_part(
    mj_scalar_t *mj_current_dim_coords,
    double used_imbalance_tolerance,
    mj_part_t current_work_part,
    mj_part_t current_concurrent_num_parts,
    mj_scalar_t *current_cut_coordinates,
    mj_part_t total_incomplete_cut_count,
    std::vector <mj_part_t> &num_partitioning_in_current_dim
){
 
 
    mj_part_t rectilinear_cut_count = 0;
    mj_scalar_t *temp_cut_coords = current_cut_coordinates;
 
    Teuchos::MultiJaggedCombinedReductionOp<mj_part_t, mj_scalar_t>
                 *reductionOp = NULL;
    reductionOp = new Teuchos::MultiJaggedCombinedReductionOp
                     <mj_part_t, mj_scalar_t>(
                                 &num_partitioning_in_current_dim ,
                                 current_work_part ,
                                 current_concurrent_num_parts);
 
    size_t total_reduction_size = 0;
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp parallel shared(total_incomplete_cut_count,  rectilinear_cut_count) num_threads(this->num_threads)
#endif
    {
        int me = 0;
#ifdef HAVE_ZOLTAN2_OMP
        me = omp_get_thread_num();
#endif
        double *my_thread_part_weights = this->thread_part_weights[me];
        mj_scalar_t *my_thread_left_closest = this->thread_cut_left_closest_point[me];
        mj_scalar_t *my_thread_right_closest = this->thread_cut_right_closest_point[me];
 
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp single
#endif
            {
                //initialize the lower and upper bounds of the cuts.
                mj_part_t next = 0;
                for(mj_part_t i = 0; i < current_concurrent_num_parts; ++i){
 
                    mj_part_t num_part_in_dim =  num_partitioning_in_current_dim[current_work_part + i];
                    mj_part_t num_cut_in_dim = num_part_in_dim - 1;
                    total_reduction_size += (4 * num_cut_in_dim + 1);
 
                    for(mj_part_t ii = 0; ii < num_cut_in_dim; ++ii){
                        this->is_cut_line_determined[next] = false;
                        this->cut_lower_bound_coordinates[next] = global_min_max_coord_total_weight[i]; //min coordinate
                        this->cut_upper_bound_coordinates[next] = global_min_max_coord_total_weight[i + current_concurrent_num_parts]; //max coordinate
 
                        this->cut_upper_bound_weights[next] = global_min_max_coord_total_weight[i + 2 * current_concurrent_num_parts]; //total weight
                        this->cut_lower_bound_weights[next] = 0;
 
                        if(this->distribute_points_on_cut_lines){
                            this->process_cut_line_weight_to_put_left[next] = 0;
                        }
                        ++next;
                    }
                }
            }
 
        //no need to have barrier here.
        //pragma omp single have implicit barrier.
 
        int iteration = 0;
        while (total_incomplete_cut_count != 0){
            iteration += 1;
            mj_part_t concurrent_cut_shifts = 0;
            size_t total_part_shift = 0;
 
            for (mj_part_t kk = 0; kk < current_concurrent_num_parts; ++kk){
                mj_part_t num_parts =  -1;
                num_parts =  num_partitioning_in_current_dim[current_work_part + kk];
 
                mj_part_t num_cuts = num_parts - 1;
                size_t total_part_count = num_parts + size_t (num_cuts) ;
                if (this->my_incomplete_cut_count[kk] > 0){
 
                    //although isDone shared, currentDone is private and same for all.
                    bool *current_cut_status = this->is_cut_line_determined + concurrent_cut_shifts;
                    double *my_current_part_weights = my_thread_part_weights + total_part_shift;
                    mj_scalar_t *my_current_left_closest = my_thread_left_closest + concurrent_cut_shifts;
                    mj_scalar_t *my_current_right_closest = my_thread_right_closest + concurrent_cut_shifts;
 
                    mj_part_t conccurent_current_part = current_work_part + kk;
                    mj_lno_t coordinate_begin_index = conccurent_current_part ==0 ? 0: this->part_xadj[conccurent_current_part -1];
                    mj_lno_t coordinate_end_index = this->part_xadj[conccurent_current_part];
                    mj_scalar_t *temp_current_cut_coords = temp_cut_coords + concurrent_cut_shifts;
 
                    mj_scalar_t min_coord = global_min_max_coord_total_weight[kk];
                    mj_scalar_t max_coord = global_min_max_coord_total_weight[kk + current_concurrent_num_parts];
 
                    // compute part weights using existing cuts
                    this->mj_1D_part_get_thread_part_weights(
                        total_part_count,
                        num_cuts,
                        max_coord,//globalMinMaxTotal[kk + concurrentPartCount],//maxScalar,
                        min_coord,//globalMinMaxTotal[kk]//minScalar,
                        coordinate_begin_index,
                        coordinate_end_index,
                        mj_current_dim_coords,
                        temp_current_cut_coords,
                        current_cut_status,
                        my_current_part_weights,
                        my_current_left_closest,
                        my_current_right_closest);
 
                }
 
                concurrent_cut_shifts += num_cuts;
                total_part_shift += total_part_count;
            }
 
            //sum up the results of threads
            this->mj_accumulate_thread_results(
                num_partitioning_in_current_dim,
                current_work_part,
                current_concurrent_num_parts);
 
            //now sum up the results of mpi processors.
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp single
#endif
            {
                if(this->comm->getSize() > 1){
                        reduceAll<int, mj_scalar_t>( *(this->comm), *reductionOp,
                                        total_reduction_size,
                                        this->total_part_weight_left_right_closests,
                                        this->global_total_part_weight_left_right_closests);
 
                }
                else {
                        memcpy(
                                this->global_total_part_weight_left_right_closests,
                            this->total_part_weight_left_right_closests,
                            total_reduction_size * sizeof(mj_scalar_t));
                }
            }
 
            //how much cut will be shifted for the next part in the concurrent part calculation.
            mj_part_t cut_shift = 0;
 
            //how much the concantaneted array will be shifted for the next part in concurrent part calculation.
            size_t tlr_shift = 0;
            for (mj_part_t kk = 0; kk < current_concurrent_num_parts; ++kk){
                mj_part_t num_parts =  num_partitioning_in_current_dim[current_work_part + kk];
                mj_part_t num_cuts = num_parts - 1;
                size_t num_total_part = num_parts + size_t (num_cuts) ;
 
                //if the cuts of this cut has already been completed.
                //nothing to do for this part.
                //just update the shift amount and proceed.
                if (this->my_incomplete_cut_count[kk] == 0) {
                        cut_shift += num_cuts;
                        tlr_shift += (num_total_part + 2 * num_cuts);
                        continue;
                }
 
                mj_scalar_t *current_local_part_weights = this->total_part_weight_left_right_closests  + tlr_shift ;
                mj_scalar_t *current_global_tlr = this->global_total_part_weight_left_right_closests + tlr_shift;
                mj_scalar_t *current_global_left_closest_points = current_global_tlr + num_total_part; //left closest points
                mj_scalar_t *current_global_right_closest_points = current_global_tlr + num_total_part + num_cuts; //right closest points
                mj_scalar_t *current_global_part_weights = current_global_tlr;
                bool *current_cut_line_determined = this->is_cut_line_determined + cut_shift;
 
                mj_scalar_t *current_part_target_weights = this->target_part_weights + cut_shift + kk;
                mj_scalar_t *current_part_cut_line_weight_to_put_left = this->process_cut_line_weight_to_put_left + cut_shift;
 
                mj_scalar_t min_coordinate = global_min_max_coord_total_weight[kk];
                mj_scalar_t max_coordinate = global_min_max_coord_total_weight[kk + current_concurrent_num_parts];
                mj_scalar_t global_total_weight = global_min_max_coord_total_weight[kk + current_concurrent_num_parts * 2];
                mj_scalar_t *current_cut_lower_bound_weights = this->cut_lower_bound_weights + cut_shift;
                mj_scalar_t *current_cut_upper_weights = this->cut_upper_bound_weights + cut_shift;
                mj_scalar_t *current_cut_upper_bounds = this->cut_upper_bound_coordinates + cut_shift;
                mj_scalar_t *current_cut_lower_bounds = this->cut_lower_bound_coordinates + cut_shift;
 
                mj_part_t initial_incomplete_cut_count = this->my_incomplete_cut_count[kk];
 
                // Now compute the new cut coordinates.
                this->mj_get_new_cut_coordinates(
                                num_total_part,
                                num_cuts,
                                max_coordinate,
                                min_coordinate,
                                global_total_weight,
                                used_imbalance_tolerance,
                                current_global_part_weights,
                                current_local_part_weights,
                                current_part_target_weights,
                                current_cut_line_determined,
                                temp_cut_coords + cut_shift,
                                current_cut_upper_bounds,
                                current_cut_lower_bounds,
                                current_global_left_closest_points,
                                current_global_right_closest_points,
                                current_cut_lower_bound_weights,
                                current_cut_upper_weights,
                                this->cut_coordinates_work_array +cut_shift, //new cut coordinates
                                current_part_cut_line_weight_to_put_left,
                                &rectilinear_cut_count,
                                this->my_incomplete_cut_count[kk]);
 
                cut_shift += num_cuts;
                tlr_shift += (num_total_part + 2 * num_cuts);
                mj_part_t iteration_complete_cut_count = initial_incomplete_cut_count - this->my_incomplete_cut_count[kk];
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp single
#endif
                {
                        total_incomplete_cut_count -= iteration_complete_cut_count;
                }
 
            }
            { //This unnecessary bracket works around a compiler bug in NVCC when compiling with OpenMP enabled
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp barrier
#pragma omp single
#endif
            {
                //swap the cut coordinates for next iteration.
                mj_scalar_t *t = temp_cut_coords;
                temp_cut_coords = this->cut_coordinates_work_array;
                this->cut_coordinates_work_array = t;
            }
            }
        }
 
        //if (myRank == 0)
        //std::cout << "iteration:" << iteration << " partition:" << num_partitioning_in_current_dim[current_work_part] << std::endl;
        // Needed only if keep_cuts; otherwise can simply swap array pointers
        // cutCoordinates and cutCoordinatesWork.
        // (at first iteration, cutCoordinates == cutCoorindates_tmp).
        // computed cuts must be in cutCoordinates.
        if (current_cut_coordinates != temp_cut_coords){
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp single
#endif
                {
                        mj_part_t next = 0;
                        for(mj_part_t i = 0; i < current_concurrent_num_parts; ++i){
                                mj_part_t num_parts = -1;
                                num_parts = num_partitioning_in_current_dim[current_work_part + i];
                                mj_part_t num_cuts = num_parts - 1;
 
                                for(mj_part_t ii = 0; ii < num_cuts; ++ii){
                                        current_cut_coordinates[next + ii] = temp_cut_coords[next + ii];
                                }
                                next += num_cuts;
                        }
                }
 
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp single
#endif
            {
                this->cut_coordinates_work_array = temp_cut_coords;
            }
        }
    }
    delete reductionOp;
}
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::mj_1D_part_get_thread_part_weights(
    size_t total_part_count,
    mj_part_t num_cuts,
    mj_scalar_t max_coord,
    mj_scalar_t min_coord,
    mj_lno_t coordinate_begin_index,
    mj_lno_t coordinate_end_index,
    mj_scalar_t *mj_current_dim_coords,
    mj_scalar_t *temp_current_cut_coords,
    bool * /* current_cut_status */,
    double *my_current_part_weights,
    mj_scalar_t *my_current_left_closest,
    mj_scalar_t *my_current_right_closest){
 
        // initializations for part weights, left/right closest
        for (size_t i = 0; i < total_part_count; ++i){
                my_current_part_weights[i] = 0;
        }
 
        //initialize the left and right closest coordinates
        //to their max value.
        for(mj_part_t i = 0; i < num_cuts; ++i){
                my_current_left_closest[i] = min_coord - 1;
                my_current_right_closest[i] = max_coord + 1;
        }
        //mj_lno_t comparison_count = 0;
        mj_scalar_t minus_EPSILON = -this->sEpsilon;
#ifdef HAVE_ZOLTAN2_OMP
        //no need for the barrier as all threads uses their local memories.
        //dont change the static scheduling here, as it is assumed when the new
        //partitions are created later.
#pragma omp for
#endif
        for (mj_lno_t ii = coordinate_begin_index; ii < coordinate_end_index; ++ii){
                int i = this->coordinate_permutations[ii];
 
                //the accesses to assigned_part_ids are thread safe
                //since each coordinate is assigned to only a single thread.
                mj_part_t j = this->assigned_part_ids[i] / 2;
 
                if(j >= num_cuts){
                        j = num_cuts - 1;
                }
 
                mj_part_t lower_cut_index = 0;
                mj_part_t upper_cut_index = num_cuts - 1;
 
                mj_scalar_t w = this->mj_uniform_weights[0]? 1:this->mj_weights[0][i];
                bool is_inserted = false;
                bool is_on_left_of_cut = false;
                bool is_on_right_of_cut = false;
                mj_part_t last_compared_part = -1;
 
                mj_scalar_t coord = mj_current_dim_coords[i];
 
                while(upper_cut_index >= lower_cut_index)
                {
                        //comparison_count++;
                        last_compared_part = -1;
                        is_on_left_of_cut = false;
                        is_on_right_of_cut = false;
                        mj_scalar_t cut = temp_current_cut_coords[j];
                        mj_scalar_t distance_to_cut = coord - cut;
                        mj_scalar_t abs_distance_to_cut = ZOLTAN2_ABS(distance_to_cut);
 
                        //if it is on the line.
                        if(abs_distance_to_cut < this->sEpsilon){
 
                                my_current_part_weights[j * 2 + 1] += w;
                                this->assigned_part_ids[i] = j * 2 + 1;
 
                                //assign left and right closest point to cut as the point is on the cut.
                                my_current_left_closest[j] = coord;
                                my_current_right_closest[j] = coord;
                                //now we need to check if there are other cuts on the same cut coordinate.
                                //if there are, then we add the weight of the cut to all cuts in the same coordinate.
                                mj_part_t kk = j + 1;
                                while(kk < num_cuts){
                                        // Needed when cuts shared the same position
                                        distance_to_cut =ZOLTAN2_ABS(temp_current_cut_coords[kk] - cut);
                                        if(distance_to_cut < this->sEpsilon){
                                                my_current_part_weights[2 * kk + 1] += w;
                                                my_current_left_closest[kk] = coord;
                                                my_current_right_closest[kk] = coord;
                                                kk++;
                                        }
                                        else{
                                                //cut is far away.
                                                //just check the left closest point for the next cut.
                                                if(coord - my_current_left_closest[kk] > this->sEpsilon){
                                                        my_current_left_closest[kk] = coord;
                                                }
                                                break;
                                        }
                                }
 
 
                                kk = j - 1;
                                //continue checking for the cuts on the left if they share the same coordinate.
                                while(kk >= 0){
                                        distance_to_cut =ZOLTAN2_ABS(temp_current_cut_coords[kk] - cut);
                                        if(distance_to_cut < this->sEpsilon){
                                                my_current_part_weights[2 * kk + 1] += w;
                                                //try to write the partId as the leftmost cut.
                                                this->assigned_part_ids[i] = kk * 2 + 1;
                                                my_current_left_closest[kk] = coord;
                                                my_current_right_closest[kk] = coord;
                                                kk--;
                                        }
                                        else{
                                                //if cut is far away on the left of the point.
                                                //then just compare for right closest point.
                                                if(my_current_right_closest[kk] - coord > this->sEpsilon){
                                                        my_current_right_closest[kk] = coord;
                                                }
                                                break;
                                        }
                                }
 
                                is_inserted = true;
                                break;
                        }
                        else {
                                //if point is on the left of the cut.
                                if (distance_to_cut < 0) {
                                        bool _break = false;
                                        if(j > 0){
                                                //check distance to the cut on the left the current cut compared.
                                                //if point is on the right, then we find the part of the point.
                                                mj_scalar_t distance_to_next_cut = coord - temp_current_cut_coords[j - 1];
                                                if(distance_to_next_cut > this->sEpsilon){
                                                        _break = true;
                                                }
                                        }
                                        //if point is not on the right of the next cut, then
                                        //set the upper bound to this cut.
                                        upper_cut_index = j - 1;
                                        //set the last part, and mark it as on the left of the last part.
                                        is_on_left_of_cut = true;
                                        last_compared_part = j;
                                        if(_break) break;
                                }
                                else {
                                        //if point is on the right of the cut.
                                        bool _break = false;
                                        if(j < num_cuts - 1){
                                                //check distance to the cut on the left the current cut compared.
                                                //if point is on the right, then we find the part of the point.
                                                mj_scalar_t distance_to_next_cut = coord - temp_current_cut_coords[j + 1];
                                                if(distance_to_next_cut < minus_EPSILON){
                         _break = true;
                     }
                                        }
 
                                        //if point is not on the left of the next cut, then
                                        //set the upper bound to this cut.
                                        lower_cut_index = j + 1;
                                        //set the last part, and mark it as on the right of the last part.
                                        is_on_right_of_cut = true;
                                        last_compared_part = j;
                                        if(_break) break;
                                }
                        }
 
                        j = (upper_cut_index + lower_cut_index) / 2;
                }
                if(!is_inserted){
                        if(is_on_right_of_cut){
 
                                //add it to the right of the last compared part.
                                my_current_part_weights[2 * last_compared_part + 2] += w;
                                this->assigned_part_ids[i] = 2 * last_compared_part + 2;
 
                                //update the right closest point of last compared cut.
                                if(my_current_right_closest[last_compared_part] - coord > this->sEpsilon){
                                        my_current_right_closest[last_compared_part] = coord;
                                }
                                //update the left closest point of the cut on the right of the last compared cut.
                                if(last_compared_part+1 < num_cuts){
 
                                        if(coord - my_current_left_closest[last_compared_part + 1] > this->sEpsilon){
                                                my_current_left_closest[last_compared_part + 1] = coord;
                                        }
                                }
 
                        }
                        else if(is_on_left_of_cut){
 
                                //add it to the left of the last compared part.
                                my_current_part_weights[2 * last_compared_part] += w;
                                this->assigned_part_ids[i] = 2 * last_compared_part;
 
 
                                //update the left closest point of last compared cut.
                                if(coord - my_current_left_closest[last_compared_part] > this->sEpsilon){
                                        my_current_left_closest[last_compared_part] = coord;
                                }
 
                                //update the right closest point of the cut on the left of the last compared cut.
                                if(last_compared_part-1 >= 0){
                                        if(my_current_right_closest[last_compared_part -1] - coord > this->sEpsilon){
                                                my_current_right_closest[last_compared_part -1] = coord;
                                        }
                                }
                        }
                }
        }
 
        // prefix sum computation.
        //we need prefix sum for each part to determine cut positions.
        for (size_t i = 1; i < total_part_count; ++i){
                // check for cuts sharing the same position; all cuts sharing a position
                // have the same weight == total weight for all cuts sharing the position.
                // don't want to accumulate that total weight more than once.
                if(i % 2 == 0 && i > 1 && i < total_part_count - 1 &&
                                ZOLTAN2_ABS(temp_current_cut_coords[i / 2] - temp_current_cut_coords[i /2 - 1])
                < this->sEpsilon){
                        //i % 2 = 0 when part i represents the cut coordinate.
                        //if it is a cut, and if the next cut also have the same coordinate, then
                        //dont addup.
                        my_current_part_weights[i] = my_current_part_weights[i-2];
                        continue;
                }
                //otherwise do the prefix sum.
                my_current_part_weights[i] += my_current_part_weights[i-1];
        }
}
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::mj_accumulate_thread_results(
    const std::vector <mj_part_t> &num_partitioning_in_current_dim,
    mj_part_t current_work_part,
    mj_part_t current_concurrent_num_parts){
 
#ifdef HAVE_ZOLTAN2_OMP
        //needs barrier here, as it requires all threads to finish mj_1D_part_get_thread_part_weights
        //using parallel region here reduces the performance because of the cache invalidates.
#pragma omp barrier
#pragma omp single
#endif
        {
                size_t tlr_array_shift = 0;
                mj_part_t cut_shift = 0;
 
                //iterate for all concurrent parts to find the left and right closest points in the process.
                for(mj_part_t i = 0; i < current_concurrent_num_parts; ++i){
 
                        mj_part_t num_parts_in_part =  num_partitioning_in_current_dim[current_work_part + i];
                        mj_part_t num_cuts_in_part = num_parts_in_part - 1;
                        size_t num_total_part_in_part = num_parts_in_part + size_t (num_cuts_in_part) ;
 
                        //iterate for cuts in a single part.
                        for(mj_part_t ii = 0; ii < num_cuts_in_part ; ++ii){
                                mj_part_t next = tlr_array_shift + ii;
                                mj_part_t cut_index = cut_shift + ii;
                                if(this->is_cut_line_determined[cut_index]) continue;
                                mj_scalar_t left_closest_in_process = this->thread_cut_left_closest_point[0][cut_index],
                                                right_closest_in_process = this->thread_cut_right_closest_point[0][cut_index];
 
                                //find the closest points from left and right for the cut in the process.
                                for (int j = 1; j < this->num_threads; ++j){
                                        if (this->thread_cut_right_closest_point[j][cut_index] < right_closest_in_process ){
                                                right_closest_in_process = this->thread_cut_right_closest_point[j][cut_index];
                                        }
                                        if (this->thread_cut_left_closest_point[j][cut_index] > left_closest_in_process ){
                                                left_closest_in_process = this->thread_cut_left_closest_point[j][cut_index];
                                        }
                                }
                                //store the left and right closes points.
                                this->total_part_weight_left_right_closests[num_total_part_in_part +
                                                                            next] = left_closest_in_process;
                                this->total_part_weight_left_right_closests[num_total_part_in_part +
                                                                            num_cuts_in_part + next] = right_closest_in_process;
                        }
                        //set the shift position in the arrays
                        tlr_array_shift += (num_total_part_in_part + 2 * num_cuts_in_part);
                        cut_shift += num_cuts_in_part;
                }
 
                tlr_array_shift = 0;
                cut_shift = 0;
                size_t total_part_array_shift = 0;
 
                //iterate for all concurrent parts to find the total weight in the process.
                for(mj_part_t i = 0; i < current_concurrent_num_parts; ++i){
 
                        mj_part_t num_parts_in_part =  num_partitioning_in_current_dim[current_work_part + i];
                        mj_part_t num_cuts_in_part = num_parts_in_part - 1;
                        size_t num_total_part_in_part = num_parts_in_part + size_t (num_cuts_in_part) ;
 
                        for(size_t j = 0; j < num_total_part_in_part; ++j){
 
                                mj_part_t cut_ind = j / 2 + cut_shift;
 
                                //need to check j !=  num_total_part_in_part - 1
                                                // which is same as j/2 != num_cuts_in_part.
                                //we cannot check it using cut_ind, because of the concurrent part concantanetion.
                                if(j !=  num_total_part_in_part - 1 && this->is_cut_line_determined[cut_ind]) continue;
                                double pwj = 0;
                                for (int k = 0; k < this->num_threads; ++k){
                                        pwj += this->thread_part_weights[k][total_part_array_shift + j];
                                }
                                //size_t jshift = j % total_part_count + i * (total_part_count + 2 * noCuts);
                                this->total_part_weight_left_right_closests[tlr_array_shift + j] = pwj;
                        }
                        cut_shift += num_cuts_in_part;
                        tlr_array_shift += num_total_part_in_part + 2 * num_cuts_in_part;
                        total_part_array_shift += num_total_part_in_part;
                }
        }
        //the other threads needs to wait here.
        //but we don't need a pragma omp barrier.
        //as omp single has already have implicit barrier.
}
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::mj_calculate_new_cut_position (
        mj_scalar_t cut_upper_bound,
    mj_scalar_t cut_lower_bound,
    mj_scalar_t cut_upper_weight,
    mj_scalar_t cut_lower_weight,
    mj_scalar_t expected_weight,
    mj_scalar_t &new_cut_position){
 
    if(ZOLTAN2_ABS(cut_upper_bound - cut_lower_bound) < this->sEpsilon){
        new_cut_position = cut_upper_bound; //or lower bound does not matter.
    }
 
 
    if(ZOLTAN2_ABS(cut_upper_weight - cut_lower_weight) < this->sEpsilon){
        new_cut_position = cut_lower_bound;
    }
 
    mj_scalar_t coordinate_range = (cut_upper_bound - cut_lower_bound);
    mj_scalar_t weight_range = (cut_upper_weight - cut_lower_weight);
    mj_scalar_t my_weight_diff = (expected_weight - cut_lower_weight);
 
    mj_scalar_t required_shift = (my_weight_diff / weight_range);
    int scale_constant = 20;
    int shiftint= int (required_shift * scale_constant);
    if (shiftint == 0) shiftint = 1;
    required_shift = mj_scalar_t (shiftint) / scale_constant;
    new_cut_position = coordinate_range * required_shift + cut_lower_bound;
}
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::mj_create_new_partitions(
    mj_part_t num_parts,
    mj_scalar_t * /* mj_current_dim_coords */,
    mj_scalar_t *current_concurrent_cut_coordinate,
    mj_lno_t coordinate_begin,
    mj_lno_t coordinate_end,
    mj_scalar_t *used_local_cut_line_weight_to_left,
    double **used_thread_part_weight_work,
    mj_lno_t *out_part_xadj){
 
        mj_part_t num_cuts = num_parts - 1;
 
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp parallel
#endif
        {
                int me = 0;
#ifdef HAVE_ZOLTAN2_OMP
                me = omp_get_thread_num();
#endif
 
                mj_lno_t *thread_num_points_in_parts = this->thread_point_counts[me];
                mj_scalar_t *my_local_thread_cut_weights_to_put_left = NULL;
 
                //now if the rectilinear partitioning is allowed we decide how
                //much weight each thread should put to left and right.
                if (this->distribute_points_on_cut_lines){
                        my_local_thread_cut_weights_to_put_left = this->thread_cut_line_weight_to_put_left[me];
                        // this for assumes the static scheduling in mj_1D_part calculation.
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp for
#endif
                        for (mj_part_t i = 0; i < num_cuts; ++i){
                                //the left to be put on the left of the cut.
                                mj_scalar_t left_weight = used_local_cut_line_weight_to_left[i];
                                for(int ii = 0; ii < this->num_threads; ++ii){
                                        if(left_weight > this->sEpsilon){
                                                //the weight of thread ii on cut.
                                                mj_scalar_t thread_ii_weight_on_cut = used_thread_part_weight_work[ii][i * 2 + 1] - used_thread_part_weight_work[ii][i * 2 ];
                                                if(thread_ii_weight_on_cut < left_weight){
                                                        //if left weight is bigger than threads weight on cut.
                                                        this->thread_cut_line_weight_to_put_left[ii][i] = thread_ii_weight_on_cut;
                                                }
                                                else {
                                                        //if thread's weight is bigger than space, then put only a portion.
                                                        this->thread_cut_line_weight_to_put_left[ii][i] = left_weight ;
                                                }
                                                left_weight -= thread_ii_weight_on_cut;
                                        }
                                        else {
                                                this->thread_cut_line_weight_to_put_left[ii][i] = 0;
                                        }
                                }
                        }
 
                        if(num_cuts > 0){
                                //this is a special case. If cutlines share the same coordinate, their weights are equal.
                                //we need to adjust the ratio for that.
                                for (mj_part_t i = num_cuts - 1; i > 0 ; --i){
                                        if(ZOLTAN2_ABS(current_concurrent_cut_coordinate[i] - current_concurrent_cut_coordinate[i -1]) < this->sEpsilon){
                                                my_local_thread_cut_weights_to_put_left[i] -= my_local_thread_cut_weights_to_put_left[i - 1] ;
                                        }
                                        my_local_thread_cut_weights_to_put_left[i] = static_cast<long long>((my_local_thread_cut_weights_to_put_left[i] + LEAST_SIGNIFICANCE) * SIGNIFICANCE_MUL)
                                                                / mj_scalar_t(SIGNIFICANCE_MUL);
                                }
                        }
                }
 
                for(mj_part_t ii = 0; ii < num_parts; ++ii){
                        thread_num_points_in_parts[ii] = 0;
                }
 
 
#ifdef HAVE_ZOLTAN2_OMP
                //dont change static scheduler. the static partitioner used later as well.
#pragma omp for
#endif
                for (mj_lno_t ii = coordinate_begin; ii < coordinate_end; ++ii){
 
                        mj_lno_t coordinate_index = this->coordinate_permutations[ii];
                        mj_scalar_t coordinate_weight = this->mj_uniform_weights[0]? 1:this->mj_weights[0][coordinate_index];
                        mj_part_t coordinate_assigned_place = this->assigned_part_ids[coordinate_index];
                        mj_part_t coordinate_assigned_part = coordinate_assigned_place / 2;
                        if(coordinate_assigned_place % 2 == 1){
                                //if it is on the cut.
                                if(this->distribute_points_on_cut_lines
                                                && my_local_thread_cut_weights_to_put_left[coordinate_assigned_part] > this->sEpsilon){
                                        //if the rectilinear partitioning is allowed,
                                        //and the thread has still space to put on the left of the cut
                                        //then thread puts the vertex to left.
                                        my_local_thread_cut_weights_to_put_left[coordinate_assigned_part] -= coordinate_weight;
                                        //if putting the vertex to left increased the weight more than expected.
                                        //and if the next cut is on the same coordinate,
                                        //then we need to adjust how much weight next cut puts to its left as well,
                                        //in order to take care of the imbalance.
                                        if(my_local_thread_cut_weights_to_put_left[coordinate_assigned_part] < 0
                                                        && coordinate_assigned_part < num_cuts - 1
                                                        && ZOLTAN2_ABS(current_concurrent_cut_coordinate[coordinate_assigned_part+1] -
                                                                        current_concurrent_cut_coordinate[coordinate_assigned_part]) < this->sEpsilon){
                                                my_local_thread_cut_weights_to_put_left[coordinate_assigned_part + 1] += my_local_thread_cut_weights_to_put_left[coordinate_assigned_part];
                                        }
                                        ++thread_num_points_in_parts[coordinate_assigned_part];
                                        this->assigned_part_ids[coordinate_index] = coordinate_assigned_part;
                                }
                                else{
                                        //if there is no more space on the left, put the coordinate to the right of the cut.
                                        ++coordinate_assigned_part;
                                        //this while loop is necessary when a line is partitioned into more than 2 parts.
                                        while(this->distribute_points_on_cut_lines &&
                                                        coordinate_assigned_part < num_cuts){
                                                //traverse all the cut lines having the same partitiong
                                                if(ZOLTAN2_ABS(current_concurrent_cut_coordinate[coordinate_assigned_part] -
                                                                current_concurrent_cut_coordinate[coordinate_assigned_part - 1])
                                                                < this->sEpsilon){
                                                        //if line has enough space on left, put it there.
                                                        if(my_local_thread_cut_weights_to_put_left[coordinate_assigned_part] >
                                                        this->sEpsilon &&
                                                        my_local_thread_cut_weights_to_put_left[coordinate_assigned_part] >=
                                                        ZOLTAN2_ABS(my_local_thread_cut_weights_to_put_left[coordinate_assigned_part] - coordinate_weight)){
                                                                my_local_thread_cut_weights_to_put_left[coordinate_assigned_part] -= coordinate_weight;
                                                                //Again if it put too much on left of the cut,
                                                                //update how much the next cut sharing the same coordinate will put to its left.
                                                                if(my_local_thread_cut_weights_to_put_left[coordinate_assigned_part] < 0 &&
                                                                                coordinate_assigned_part < num_cuts - 1 &&
                                                                                ZOLTAN2_ABS(current_concurrent_cut_coordinate[coordinate_assigned_part+1] -
                                                                                                current_concurrent_cut_coordinate[coordinate_assigned_part]) < this->sEpsilon){
                                                                        my_local_thread_cut_weights_to_put_left[coordinate_assigned_part + 1] += my_local_thread_cut_weights_to_put_left[coordinate_assigned_part];
                                                                }
                                                                break;
                                                        }
                                                }
                                                else {
                                                        break;
                                                }
                                                ++coordinate_assigned_part;
                                        }
                                        ++thread_num_points_in_parts[coordinate_assigned_part];
                                        this->assigned_part_ids[coordinate_index] = coordinate_assigned_part;
                                }
                        }
                        else {
                                //if it is already assigned to a part, then just put it to the corresponding part.
                                ++thread_num_points_in_parts[coordinate_assigned_part];
                                this->assigned_part_ids[coordinate_index] = coordinate_assigned_part;
                        }
                }
 
 
 
                //now we calculate where each thread will write in new_coordinate_permutations array.
                //first we find the out_part_xadj, by marking the begin and end points of each part found.
                //the below loop find the number of points in each part, and writes it to out_part_xadj
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp for
#endif
                for(mj_part_t j = 0; j < num_parts; ++j){
                        mj_lno_t num_points_in_part_j_upto_thread_i = 0;
                        for (int i = 0; i < this->num_threads; ++i){
                                mj_lno_t thread_num_points_in_part_j = this->thread_point_counts[i][j];
                                //prefix sum to thread point counts, so that each will have private space to write.
                                this->thread_point_counts[i][j] = num_points_in_part_j_upto_thread_i;
                                num_points_in_part_j_upto_thread_i += thread_num_points_in_part_j;
 
                        }
                        out_part_xadj[j] = num_points_in_part_j_upto_thread_i;// + prev2; //+ coordinateBegin;
                }
 
                //now we need to do a prefix sum to out_part_xadj[j], to point begin and end of each part.
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp single
#endif
                {
                        //perform prefix sum for num_points in parts.
                        for(mj_part_t j = 1; j < num_parts; ++j){
                                out_part_xadj[j] += out_part_xadj[j - 1];
                        }
                }
 
                //shift the num points in threads thread to obtain the
                //beginning index of each thread's private space.
                for(mj_part_t j = 1; j < num_parts; ++j){
                        thread_num_points_in_parts[j] += out_part_xadj[j - 1] ;
                }
 
 
                //now thread gets the coordinate and writes the index of coordinate to the permutation array
                //using the part index we calculated.
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp for
#endif
                for (mj_lno_t ii = coordinate_begin; ii < coordinate_end; ++ii){
                        mj_lno_t i = this->coordinate_permutations[ii];
                        mj_part_t p =  this->assigned_part_ids[i];
                        this->new_coordinate_permutations[coordinate_begin +
                                                          thread_num_points_in_parts[p]++] = i;
                }
        }
}
 
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::mj_get_new_cut_coordinates(
                const size_t &/* num_total_part */,
                const mj_part_t &num_cuts,
                const mj_scalar_t &max_coordinate,
                const mj_scalar_t &min_coordinate,
                const mj_scalar_t &global_total_weight,
                const double &used_imbalance_tolerance,
                mj_scalar_t * current_global_part_weights,
                const mj_scalar_t * current_local_part_weights,
                const mj_scalar_t *current_part_target_weights,
                bool *current_cut_line_determined,
                mj_scalar_t *current_cut_coordinates,
                mj_scalar_t *current_cut_upper_bounds,
                mj_scalar_t *current_cut_lower_bounds,
                mj_scalar_t *current_global_left_closest_points,
                mj_scalar_t *current_global_right_closest_points,
                mj_scalar_t * current_cut_lower_bound_weights,
                mj_scalar_t * current_cut_upper_weights,
                mj_scalar_t *new_current_cut_coordinates,
                mj_scalar_t *current_part_cut_line_weight_to_put_left,
                mj_part_t *rectilinear_cut_count,
                mj_part_t &my_num_incomplete_cut){
 
        //seen weight in the part
        mj_scalar_t seen_weight_in_part = 0;
        //expected weight for part.
        mj_scalar_t expected_weight_in_part = 0;
        //imbalance for the left and right side of the cut.
        double imbalance_on_left = 0, imbalance_on_right = 0;
 
 
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp for
#endif
        for (mj_part_t i = 0; i < num_cuts; i++){
                //if left and right closest points are not set yet,
                //set it to the cut itself.
                if(min_coordinate - current_global_left_closest_points[i] > this->sEpsilon)
                        current_global_left_closest_points[i] = current_cut_coordinates[i];
                if(current_global_right_closest_points[i] - max_coordinate > this->sEpsilon)
                        current_global_right_closest_points[i] = current_cut_coordinates[i];
 
        }
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp for
#endif
        for (mj_part_t i = 0; i < num_cuts; i++){
 
                if(this->distribute_points_on_cut_lines){
                        //init the weight on the cut.
                        this->global_rectilinear_cut_weight[i] = 0;
                        this->process_rectilinear_cut_weight[i] = 0;
                }
                //if already determined at previous iterations,
                //then just write the coordinate to new array, and proceed.
                if(current_cut_line_determined[i]) {
                        new_current_cut_coordinates[i] = current_cut_coordinates[i];
                        continue;
                }
 
                //current weight of the part at the left of the cut line.
                seen_weight_in_part = current_global_part_weights[i * 2];
 
                /*
               std::cout << "seen_weight_in_part:" << i << " is "<< seen_weight_in_part <<std::endl;
               std::cout << "\tcut:" << current_cut_coordinates[i]
                       << " current_cut_lower_bounds:" << current_cut_lower_bounds[i]
               << " current_cut_upper_bounds:" << current_cut_upper_bounds[i] << std::endl;
               */
                //expected ratio
                expected_weight_in_part = current_part_target_weights[i];
                //leftImbalance = imbalanceOf(seenW, globalTotalWeight, expected);
                imbalance_on_left = imbalanceOf2(seen_weight_in_part, expected_weight_in_part);
                //rightImbalance = imbalanceOf(globalTotalWeight - seenW, globalTotalWeight, 1 - expected);
                imbalance_on_right = imbalanceOf2(global_total_weight - seen_weight_in_part, global_total_weight - expected_weight_in_part);
 
                bool is_left_imbalance_valid = ZOLTAN2_ABS(imbalance_on_left) - used_imbalance_tolerance < this->sEpsilon ;
                bool is_right_imbalance_valid = ZOLTAN2_ABS(imbalance_on_right) - used_imbalance_tolerance < this->sEpsilon;
 
                //if the cut line reaches to desired imbalance.
                if(is_left_imbalance_valid && is_right_imbalance_valid){
                        current_cut_line_determined[i] = true;
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp atomic
#endif
                        my_num_incomplete_cut -= 1;
                        new_current_cut_coordinates [i] = current_cut_coordinates[i];
                        continue;
                }
                else if(imbalance_on_left < 0){
                        //if left imbalance < 0 then we need to move the cut to right.
 
                        if(this->distribute_points_on_cut_lines){
                                //if it is okay to distribute the coordinate on
                                //the same coordinate to left and right.
                                //then check if we can reach to the target weight by including the
                                //coordinates in the part.
                                if (current_global_part_weights[i * 2 + 1] == expected_weight_in_part){
                                        //if it is we are done.
                                        current_cut_line_determined[i] = true;
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp atomic
#endif
                                        my_num_incomplete_cut -= 1;
 
                                        //then assign everything on the cut to the left of the cut.
                                        new_current_cut_coordinates [i] = current_cut_coordinates[i];
 
                                        //for this cut all the weight on cut will be put to left.
 
                                        current_part_cut_line_weight_to_put_left[i] = current_local_part_weights[i * 2 + 1] - current_local_part_weights[i * 2];
                                        continue;
                                }
                                else if (current_global_part_weights[i * 2 + 1] > expected_weight_in_part){
 
                                        //if the weight is larger than the expected weight,
                                        //then we need to distribute some points to left, some to right.
                                        current_cut_line_determined[i] = true;
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp atomic
#endif
                                        *rectilinear_cut_count += 1;
                                        //increase the num cuts to be determined with rectilinear partitioning.
 
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp atomic
#endif
                                        my_num_incomplete_cut -= 1;
                                        new_current_cut_coordinates [i] = current_cut_coordinates[i];
                                        this->process_rectilinear_cut_weight[i] = current_local_part_weights[i * 2 + 1] -
                                                        current_local_part_weights[i * 2];
                                        continue;
                                }
                        }
                        //we need to move further right,so set lower bound to current line, and shift it to the closes point from right.
                        current_cut_lower_bounds[i] = current_global_right_closest_points[i];
                        //set the lower bound weight to the weight we have seen.
                        current_cut_lower_bound_weights[i] = seen_weight_in_part;
 
                        //compare the upper bound with what has been found in the last iteration.
                        //we try to make more strict bounds for the cut here.
                        for (mj_part_t ii = i + 1; ii < num_cuts ; ++ii){
                                mj_scalar_t p_weight = current_global_part_weights[ii * 2];
                                mj_scalar_t line_weight = current_global_part_weights[ii * 2 + 1];
 
                                if(p_weight >= expected_weight_in_part){
                                        //if a cut on the right has the expected weight, then we found
                                        //our cut position. Set up and low coordiantes to this new cut coordinate.
                                        //but we need one more iteration to finalize the cut position,
                                        //as wee need to update the part ids.
                                        if(p_weight == expected_weight_in_part){
                                                current_cut_upper_bounds[i] = current_cut_coordinates[ii];
                                                current_cut_upper_weights[i] = p_weight;
                                                current_cut_lower_bounds[i] = current_cut_coordinates[ii];
                                                current_cut_lower_bound_weights[i] = p_weight;
                                        } else if (p_weight < current_cut_upper_weights[i]){
                                                //if a part weight is larger then my expected weight,
                                                //but lower than my upper bound weight, update upper bound.
                                                current_cut_upper_bounds[i] = current_global_left_closest_points[ii];
                                                current_cut_upper_weights[i] = p_weight;
                                        }
                                        break;
                                }
                                //if comes here then pw < ew
                                //then compare the weight against line weight.
                                if(line_weight >= expected_weight_in_part){
                                        //if the line is larger than the expected weight,
                                        //then we need to reach to the balance by distributing coordinates on this line.
                                        current_cut_upper_bounds[i] = current_cut_coordinates[ii];
                                        current_cut_upper_weights[i] = line_weight;
                                        current_cut_lower_bounds[i] = current_cut_coordinates[ii];
                                        current_cut_lower_bound_weights[i] = p_weight;
                                        break;
                                }
                                //if a stricter lower bound is found,
                                //update the lower bound.
                                if (p_weight <= expected_weight_in_part && p_weight >= current_cut_lower_bound_weights[i]){
                                        current_cut_lower_bounds[i] = current_global_right_closest_points[ii] ;
                                        current_cut_lower_bound_weights[i] = p_weight;
                                }
                        }
 
 
                        mj_scalar_t new_cut_position = 0;
                        this->mj_calculate_new_cut_position(
                                        current_cut_upper_bounds[i],
                                        current_cut_lower_bounds[i],
                                        current_cut_upper_weights[i],
                                        current_cut_lower_bound_weights[i],
                                        expected_weight_in_part, new_cut_position);
 
                        //if cut line does not move significantly.
                        //then finalize the search.
                        if (ZOLTAN2_ABS(current_cut_coordinates[i] - new_cut_position) < this->sEpsilon
                                /*|| current_cut_lower_bounds[i] - current_cut_upper_bounds[i] > this->sEpsilon*/
                                ){
                                current_cut_line_determined[i] = true;
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp atomic
#endif
                                my_num_incomplete_cut -= 1;
 
                                //set the cut coordinate and proceed.
                                new_current_cut_coordinates [i] = current_cut_coordinates[i];
                        } else {
                                new_current_cut_coordinates [i] = new_cut_position;
                        }
                } else {
 
                        //need to move the cut line to left.
                        //set upper bound to current line.
                        current_cut_upper_bounds[i] = current_global_left_closest_points[i];
                        current_cut_upper_weights[i] = seen_weight_in_part;
 
                        // compare the current cut line weights with previous upper and lower bounds.
                        for (int ii = i - 1; ii >= 0; --ii){
                                mj_scalar_t p_weight = current_global_part_weights[ii * 2];
                                mj_scalar_t line_weight = current_global_part_weights[ii * 2 + 1];
                                if(p_weight <= expected_weight_in_part){
                                        if(p_weight == expected_weight_in_part){
                                                //if the weight of the part is my expected weight
                                                //then we find the solution.
                                                current_cut_upper_bounds[i] = current_cut_coordinates[ii];
                                                current_cut_upper_weights[i] = p_weight;
                                                current_cut_lower_bounds[i] = current_cut_coordinates[ii];
                                                current_cut_lower_bound_weights[i] = p_weight;
                                        }
                                        else if (p_weight > current_cut_lower_bound_weights[i]){
                                                //if found weight is bigger than the lower bound
                                                //then update the lower bound.
                                                current_cut_lower_bounds[i] = current_global_right_closest_points[ii];
                                                current_cut_lower_bound_weights[i] = p_weight;
 
                                                //at the same time, if weight of line is bigger than the
                                                //expected weight, then update the upper bound as well.
                                                //in this case the balance will be obtained by distributing weightss
                                                //on this cut position.
                                                if(line_weight > expected_weight_in_part){
                                                        current_cut_upper_bounds[i] = current_global_right_closest_points[ii];
                                                        current_cut_upper_weights[i] = line_weight;
                                                }
                                        }
                                        break;
                                }
                                //if the weight of the cut on the left is still bigger than my weight,
                                //and also if the weight is smaller than the current upper weight,
                                //or if the weight is equal to current upper weight, but on the left of
                                // the upper weight, then update upper bound.
                                if (p_weight >= expected_weight_in_part &&
                                                (p_weight < current_cut_upper_weights[i] ||
                                                                (p_weight == current_cut_upper_weights[i] &&
                                                                                current_cut_upper_bounds[i] > current_global_left_closest_points[ii]
                                                                )
                                                )
                                        ){
                                        current_cut_upper_bounds[i] = current_global_left_closest_points[ii] ;
                                        current_cut_upper_weights[i] = p_weight;
                                }
                        }
                        mj_scalar_t new_cut_position = 0;
                        this->mj_calculate_new_cut_position(
                                        current_cut_upper_bounds[i],
                                        current_cut_lower_bounds[i],
                                        current_cut_upper_weights[i],
                                        current_cut_lower_bound_weights[i],
                                        expected_weight_in_part,
                                        new_cut_position);
 
                        //if cut line does not move significantly.
                        if (ZOLTAN2_ABS(current_cut_coordinates[i] - new_cut_position) < this->sEpsilon
                                        /*|| current_cut_lower_bounds[i] - current_cut_upper_bounds[i] > this->sEpsilon*/ ){
                                current_cut_line_determined[i] = true;
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp atomic
#endif
                                my_num_incomplete_cut -= 1;
                                //set the cut coordinate and proceed.
                                new_current_cut_coordinates [ i] = current_cut_coordinates[i];
                        } else {
                                new_current_cut_coordinates [ i] = new_cut_position;
                        }
                }
        }
 
        { // This unnecessary bracket works around a compiler bug in NVCC when enabling OpenMP as well
 
        //communication to determine the ratios of processors for the distribution
        //of coordinates on the cut lines.
#ifdef HAVE_ZOLTAN2_OMP
        //no need barrier here as it is implicit.
#pragma omp single
#endif
        {
                if(*rectilinear_cut_count > 0){
 
                        try{
                                Teuchos::scan<int,mj_scalar_t>(
                                                *comm, Teuchos::REDUCE_SUM,
                                                num_cuts,
                                                this->process_rectilinear_cut_weight,
                                                this->global_rectilinear_cut_weight
                                );
                        }
                        Z2_THROW_OUTSIDE_ERROR(*(this->mj_env))
 
                        for (mj_part_t i = 0; i < num_cuts; ++i){
                                //if cut line weight to be distributed.
                                if(this->global_rectilinear_cut_weight[i] > 0) {
                                        //expected weight to go to left of the cut.
                                        mj_scalar_t expected_part_weight = current_part_target_weights[i];
                                        //the weight that should be put to left of the cut.
                                        mj_scalar_t necessary_weight_on_line_for_left = expected_part_weight - current_global_part_weights[i * 2];
                                        //the weight of the cut in the process
                                        mj_scalar_t my_weight_on_line = this->process_rectilinear_cut_weight[i];
                                        //the sum of the cut weights upto this process, including the weight of this process.
                                        mj_scalar_t weight_on_line_upto_process_inclusive = this->global_rectilinear_cut_weight[i];
                                        //the space on the left side of the cut after all processes before this process (including this process)
                                        //puts their weights on cut to left.
                                        mj_scalar_t space_to_put_left = necessary_weight_on_line_for_left - weight_on_line_upto_process_inclusive;
                                        //add my weight to this space to find out how much space is left to me.
                                        mj_scalar_t space_left_to_me = space_to_put_left + my_weight_on_line;
 
                                        /*
                                       std::cout << "expected_part_weight:" << expected_part_weight
                                                        << " necessary_weight_on_line_for_left:" << necessary_weight_on_line_for_left
                                                        << " my_weight_on_line" << my_weight_on_line
                                                        << " weight_on_line_upto_process_inclusive:" << weight_on_line_upto_process_inclusive
                                                        << " space_to_put_left:" << space_to_put_left
                                                        << " space_left_to_me" << space_left_to_me << std::endl;
                                         */
                                        if(space_left_to_me < 0){
                                                //space_left_to_me is negative and i dont need to put anything to left.
                                                current_part_cut_line_weight_to_put_left[i] = 0;
                                        }
                                        else if(space_left_to_me >= my_weight_on_line){
                                                //space left to me is bigger than the weight of the processor on cut.
                                                //so put everything to left.
                                                current_part_cut_line_weight_to_put_left[i] = my_weight_on_line;
                                                //std::cout << "setting current_part_cut_line_weight_to_put_left to my_weight_on_line:" << my_weight_on_line << std::endl;
                                        }
                                        else {
                                                //put only the weight as much as the space.
                                                current_part_cut_line_weight_to_put_left[i] = space_left_to_me ;
 
                                                //std::cout << "setting current_part_cut_line_weight_to_put_left to space_left_to_me:" << space_left_to_me << std::endl;
                                        }
 
                                }
                        }
                        *rectilinear_cut_count = 0;
                }
        }
        }
}
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::get_processor_num_points_in_parts(
    mj_part_t num_procs,
    mj_part_t num_parts,
    mj_gno_t *&num_points_in_all_processor_parts){
 
        //initially allocation_size is num_parts
        size_t allocation_size = num_parts * (num_procs + 1);
 
        //this will be output
        //holds how many each processor has in each part.
        //last portion is the sum of all processor points in each part.
 
        //allocate memory for the local num coordinates in each part.
        mj_gno_t *num_local_points_in_each_part_to_reduce_sum = allocMemory<mj_gno_t>(allocation_size);
 
 
        //this is the portion of the memory which will be used
        //at the summation to obtain total number of processors' points in each part.
        mj_gno_t *my_local_points_to_reduce_sum = num_local_points_in_each_part_to_reduce_sum + num_procs * num_parts;
        //this is the portion of the memory where each stores its local number.
        //this information is needed by other processors.
        mj_gno_t *my_local_point_counts_in_each_art = num_local_points_in_each_part_to_reduce_sum + this->myRank * num_parts;
 
        //initialize the array with 0's.
        memset(num_local_points_in_each_part_to_reduce_sum, 0, sizeof(mj_gno_t)*allocation_size);
 
        //write the number of coordinates in each part.
        for (mj_part_t i = 0; i < num_parts; ++i){
                mj_lno_t part_begin_index = 0;
                if (i > 0){
                        part_begin_index = this->new_part_xadj[i - 1];
                }
                mj_lno_t part_end_index = this->new_part_xadj[i];
                my_local_points_to_reduce_sum[i] = part_end_index - part_begin_index;
        }
 
        //copy the local num parts to the last portion of array,
        //so that this portion will represent the global num points in each part after the reduction.
        memcpy (my_local_point_counts_in_each_art,
                        my_local_points_to_reduce_sum,
                        sizeof(mj_gno_t) * (num_parts) );
 
 
        //reduceAll operation.
        //the portion that belongs to a processor with index p
        //will start from myRank * num_parts.
        //the global number of points will be held at the index
        try{
                reduceAll<int, mj_gno_t>(
                                *(this->comm),
                                Teuchos::REDUCE_SUM,
                                allocation_size,
                                num_local_points_in_each_part_to_reduce_sum,
                                num_points_in_all_processor_parts);
        }
        Z2_THROW_OUTSIDE_ERROR(*(this->mj_env))
        freeArray<mj_gno_t>(num_local_points_in_each_part_to_reduce_sum);
}
 
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
bool AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::mj_check_to_migrate(
    size_t migration_reduce_all_population,
    mj_lno_t num_coords_for_last_dim_part,
    mj_part_t num_procs,
    mj_part_t num_parts,
    mj_gno_t *num_points_in_all_processor_parts){
 
        //if reduce all count and population in the last dim is too high
    if (migration_reduce_all_population > FUTURE_REDUCEALL_CUTOFF) return true;
    //if the work in a part per processor in the last dim is too low.
    if (num_coords_for_last_dim_part < MIN_WORK_LAST_DIM) return true;
 
        //if migration is to be checked and the imbalance is too high
    if (this->check_migrate_avoid_migration_option == 0){
        double global_imbalance = 0;
        //global shift to reach the sum of coordiante count in each part.
        size_t global_shift = num_procs * num_parts;
 
        for (mj_part_t ii = 0; ii < num_procs; ++ii){
                for (mj_part_t i = 0; i < num_parts; ++i){
                        double ideal_num = num_points_in_all_processor_parts[global_shift + i]
                                                                / double(num_procs);
 
                        global_imbalance += ZOLTAN2_ABS(ideal_num -
                                        num_points_in_all_processor_parts[ii * num_parts + i]) /  (ideal_num);
                }
        }
        global_imbalance /= num_parts;
        global_imbalance /= num_procs;
 
                /*
        if (this->myRank == 0) {
               std::cout << "imbalance for next iteration:" << global_imbalance << std::endl;
        }
        */
 
        if(global_imbalance <= this->minimum_migration_imbalance){
                return false;
        }
        else {
                return true;
        }
    }
    else {
        //if migration is forced
        return true;
    }
}
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::assign_send_destinations(
    mj_part_t num_parts,
    mj_part_t *part_assignment_proc_begin_indices,
    mj_part_t *processor_chains_in_parts,
    mj_lno_t *send_count_to_each_proc,
    int *coordinate_destinations){
 
    for (mj_part_t p = 0; p < num_parts; ++p){
        mj_lno_t part_begin = 0;
        if (p > 0) part_begin = this->new_part_xadj[p - 1];
        mj_lno_t part_end = this->new_part_xadj[p];
 
        //get the first part that current processor will send its part-p.
        mj_part_t proc_to_sent = part_assignment_proc_begin_indices[p];
        //initialize how many point I sent to this processor.
        mj_lno_t num_total_send = 0;
        for (mj_lno_t j=part_begin; j < part_end; j++){
            mj_lno_t local_ind = this->new_coordinate_permutations[j];
            while (num_total_send >= send_count_to_each_proc[proc_to_sent]){
                //then get the next processor to send the points in part p.
                num_total_send = 0;
                //assign new processor to part_assign_begin[p]
                part_assignment_proc_begin_indices[p] = processor_chains_in_parts[proc_to_sent];
                //remove the previous processor
                processor_chains_in_parts[proc_to_sent] = -1;
                //choose the next processor as the next one to send.
                proc_to_sent = part_assignment_proc_begin_indices[p];
            }
            //write the gno index to corresponding position in sendBuf.
            coordinate_destinations[local_ind] = proc_to_sent;
            ++num_total_send;
        }
    }
}
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::mj_assign_proc_to_parts(
                mj_gno_t * num_points_in_all_processor_parts,
                mj_part_t num_parts,
                mj_part_t num_procs,
                mj_lno_t *send_count_to_each_proc,
                std::vector<mj_part_t> &processor_ranks_for_subcomm,
                std::vector<mj_part_t> *next_future_num_parts_in_parts,
                mj_part_t &out_part_index,
                mj_part_t &output_part_numbering_begin_index,
                int *coordinate_destinations){
 
 
    mj_gno_t *global_num_points_in_parts = num_points_in_all_processor_parts + num_procs * num_parts;
    mj_part_t *num_procs_assigned_to_each_part = allocMemory<mj_part_t>(num_parts);
 
    //boolean variable if the process finds its part to be assigned.
    bool did_i_find_my_group = false;
 
    mj_part_t num_free_procs = num_procs;
    mj_part_t minimum_num_procs_required_for_rest_of_parts = num_parts - 1;
 
    double max_imbalance_difference = 0;
    mj_part_t max_differing_part = 0;
 
    //find how many processor each part requires.
    for (mj_part_t i=0; i < num_parts; i++){
 
        //scalar portion of the required processors
        double scalar_required_proc = num_procs *
                (double (global_num_points_in_parts[i]) / double (this->num_global_coords));
 
        //round it to closest integer; make sure have at least one proc.
        mj_part_t required_proc = static_cast<mj_part_t> (0.5 + scalar_required_proc);
        if (required_proc == 0) required_proc = 1;
 
        //if assigning the required num procs, creates problems for the rest of the parts.
        //then only assign {num_free_procs - (minimum_num_procs_required_for_rest_of_parts)} procs to this part.
        if (num_free_procs - required_proc < minimum_num_procs_required_for_rest_of_parts){
            required_proc = num_free_procs - (minimum_num_procs_required_for_rest_of_parts);
        }
 
        //reduce the free processor count
        num_free_procs -= required_proc;
        //reduce the free minimum processor count required for the rest of the part by 1.
        --minimum_num_procs_required_for_rest_of_parts;
 
        //part (i) is assigned to (required_proc) processors.
        num_procs_assigned_to_each_part[i] = required_proc;
 
        //because of the roundings some processors might be left as unassigned.
        //we want to assign those processors to the part with most imbalance.
        //find the part with the maximum imbalance here.
        double imbalance_wrt_ideal = (scalar_required_proc - required_proc) /  required_proc;
        if (imbalance_wrt_ideal > max_imbalance_difference){
            max_imbalance_difference = imbalance_wrt_ideal;
            max_differing_part = i;
        }
    }
 
    //assign extra processors to the part with maximum imbalance than the ideal.
    if (num_free_procs > 0){
        num_procs_assigned_to_each_part[max_differing_part] +=  num_free_procs;
    }
 
    //now find what are the best processors with least migration for each part.
 
    //part_assignment_proc_begin_indices ([i]) is the array that holds the beginning
    //index of a processor that processor sends its data for part - i
    mj_part_t *part_assignment_proc_begin_indices = allocMemory<mj_part_t>(num_parts);
    //the next processor send is found in processor_chains_in_parts, in linked list manner.
    mj_part_t *processor_chains_in_parts = allocMemory<mj_part_t>(num_procs);
    mj_part_t *processor_part_assignments = allocMemory<mj_part_t>(num_procs);
 
    //initialize the assignment of each processor.
    //this has a linked list implementation.
    //the beginning of processors assigned
    //to each part is hold at  part_assignment_proc_begin_indices[part].
    //then the next processor assigned to that part is located at
    //proc_part_assignments[part_assign_begins[part]], this is a chain
    //until the value of -1 is reached.
    for (int i = 0; i < num_procs; ++i ){
        processor_part_assignments[i] = -1;
        processor_chains_in_parts[i] = -1;
    }
    for (int i = 0; i < num_parts; ++i ){
        part_assignment_proc_begin_indices[i] = -1;
    }
 
 
    //std::cout << "Before migration: mig type:" << this->migration_type << std::endl;
    //Allocate memory for sorting data structure.
    uSignedSortItem<mj_part_t, mj_gno_t, char> * sort_item_num_part_points_in_procs = allocMemory <uSignedSortItem<mj_part_t, mj_gno_t, char> > (num_procs);
    for(mj_part_t i = 0; i < num_parts; ++i){
        //the algorithm tries to minimize the cost of migration,
        //by assigning the processors with highest number of coordinates on that part.
        //here we might want to implement a maximum weighted bipartite matching algorithm.
        for(mj_part_t ii = 0; ii < num_procs; ++ii){
                sort_item_num_part_points_in_procs[ii].id = ii;
                //if processor is not assigned yet.
                //add its num points to the sort data structure.
                if (processor_part_assignments[ii] == -1){
                        sort_item_num_part_points_in_procs[ii].val = num_points_in_all_processor_parts[ii * num_parts + i];
                        sort_item_num_part_points_in_procs[ii].signbit = 1; //indicate that the processor has positive weight.
                }
                else {
                        //if processor is already assigned, insert -nLocal - 1 so that it won't be selected again.
                        //would be same if we simply set it to -1,
                        //but more information with no extra cost (which is used later) is provided.
                        //sort_item_num_part_points_in_procs[ii].val = -num_points_in_all_processor_parts[ii * num_parts + i] - 1;
 
                        //UPDATE: Since above gets warning when unsigned is used to represent, we added extra bit to as sign bit to the sort item.
                        //It is 1 for positives, 0 for negatives.
                        sort_item_num_part_points_in_procs[ii].val = num_points_in_all_processor_parts[ii * num_parts + i];
                        sort_item_num_part_points_in_procs[ii].signbit = 0;
                }
        }
        //sort the processors in the part.
        uqSignsort<mj_part_t, mj_gno_t,char>(num_procs, sort_item_num_part_points_in_procs);
 
        /*
        for(mj_part_t ii = 0; ii < num_procs; ++ii){
          std::cout << "ii:" << ii << " " << sort_item_num_part_points_in_procs[ii].id <<
              " " << sort_item_num_part_points_in_procs[ii].val <<
              " " << int(sort_item_num_part_points_in_procs[ii].signbit) << std::endl;
        }
        */
 
        mj_part_t required_proc_count =  num_procs_assigned_to_each_part[i];
        mj_gno_t total_num_points_in_part = global_num_points_in_parts[i];
        mj_gno_t ideal_num_points_in_a_proc =
                Teuchos::as<mj_gno_t>(ceil (total_num_points_in_part / double (required_proc_count)));
 
        //starts sending to least heaviest part.
        mj_part_t next_proc_to_send_index = num_procs - required_proc_count;
        mj_part_t next_proc_to_send_id = sort_item_num_part_points_in_procs[next_proc_to_send_index].id;
        mj_lno_t space_left_in_sent_proc =  ideal_num_points_in_a_proc - sort_item_num_part_points_in_procs[next_proc_to_send_index].val;
 
        //find the processors that will be assigned to this part, which are the heaviest
        //non assigned processors.
        for(mj_part_t ii = num_procs - 1; ii >= num_procs - required_proc_count; --ii){
            mj_part_t proc_id = sort_item_num_part_points_in_procs[ii].id;
            //assign processor to part - i.
            processor_part_assignments[proc_id] = i;
        }
 
        bool did_change_sign = false;
        //if processor has a minus count, reverse it.
        for(mj_part_t ii = 0; ii < num_procs; ++ii){
            // TODO:  THE LINE BELOW PRODUCES A WARNING IF gno_t IS UNSIGNED
            // TODO:  SEE BUG 6194
            if (sort_item_num_part_points_in_procs[ii].signbit == 0){
                did_change_sign = true;
                sort_item_num_part_points_in_procs[ii].signbit = 1;
            }
            else {
                break;
            }
        }
        if(did_change_sign){
            //resort the processors in the part for the rest of the processors that is not assigned.
            uqSignsort<mj_part_t, mj_gno_t>(num_procs - required_proc_count, sort_item_num_part_points_in_procs);
        }
        /*
        for(mj_part_t ii = 0; ii < num_procs; ++ii){
          std::cout << "after resort ii:" << ii << " " << sort_item_num_part_points_in_procs[ii].id <<
              " " << sort_item_num_part_points_in_procs[ii].val <<
              " " << int(sort_item_num_part_points_in_procs[ii].signbit ) << std::endl;
        }
        */
 
        //check if this processors is one of the procs assigned to this part.
        //if it is, then get the group.
        if (!did_i_find_my_group){
            for(mj_part_t ii = num_procs - 1; ii >= num_procs - required_proc_count; --ii){
 
                mj_part_t proc_id_to_assign = sort_item_num_part_points_in_procs[ii].id;
                //add the proc to the group.
                processor_ranks_for_subcomm.push_back(proc_id_to_assign);
 
                if(proc_id_to_assign == this->myRank){
                        //if the assigned process is me, then I find my group.
                    did_i_find_my_group = true;
                    //set the beginning of part i to my rank.
                    part_assignment_proc_begin_indices[i] = this->myRank;
                    processor_chains_in_parts[this->myRank] = -1;
 
                    //set send count to myself to the number of points that I have in part i.
                    send_count_to_each_proc[this->myRank] = sort_item_num_part_points_in_procs[ii].val;
 
                    //calculate the shift required for the output_part_numbering_begin_index
                    for (mj_part_t in = 0; in < i; ++in){
                        output_part_numbering_begin_index += (*next_future_num_parts_in_parts)[in];
                    }
                    out_part_index = i;
                }
            }
            //if these was not my group,
            //clear the subcomminicator processor array.
            if (!did_i_find_my_group){
                processor_ranks_for_subcomm.clear();
            }
        }
 
        //send points of the nonassigned coordinates to the assigned coordinates.
        //starts from the heaviest nonassigned processor.
        //TODO we might want to play with this part, that allows more computational imbalance
        //but having better communication balance.
        for(mj_part_t ii = num_procs - required_proc_count - 1; ii >= 0; --ii){
            mj_part_t nonassigned_proc_id = sort_item_num_part_points_in_procs[ii].id;
            mj_lno_t num_points_to_sent = sort_item_num_part_points_in_procs[ii].val;
 
            //we set number of points to -to_sent - 1 for the assigned processors.
            //we reverse it here. This should not happen, as we have already reversed them above.
#ifdef MJ_DEBUG
            if (num_points_to_sent < 0) {
               std::cout << "Migration - processor assignments - for part:" << i << "from proc:" << nonassigned_proc_id << " num_points_to_sent:" << num_points_to_sent << std::endl;
                exit(1);
            }
#endif
 
      switch (migration_type){
        case 0:
        {
              //now sends the points to the assigned processors.
              while (num_points_to_sent > 0){
                //if the processor has enough space.
                if (num_points_to_sent <= space_left_in_sent_proc){
                        //reduce the space left in the processor.
                        space_left_in_sent_proc -= num_points_to_sent;
                        //if my rank is the one that is sending the coordinates.
                    if (this->myRank == nonassigned_proc_id){
                        //set my sent count to the sent processor.
                        send_count_to_each_proc[next_proc_to_send_id] = num_points_to_sent;
                        //save the processor in the list (processor_chains_in_parts and part_assignment_proc_begin_indices)
                        //that the processor will send its point in part-i.
                        mj_part_t prev_begin = part_assignment_proc_begin_indices[i];
                        part_assignment_proc_begin_indices[i] = next_proc_to_send_id;
                        processor_chains_in_parts[next_proc_to_send_id] = prev_begin;
                    }
                    num_points_to_sent = 0;
                }
                else {
                    //there might be no space left in the processor.
                    if(space_left_in_sent_proc > 0){
                        num_points_to_sent -= space_left_in_sent_proc;
 
                        //send as the space left in the processor.
                        if (this->myRank == nonassigned_proc_id){
                                //send as much as the space in this case.
                            send_count_to_each_proc[next_proc_to_send_id] = space_left_in_sent_proc;
                            mj_part_t prev_begin = part_assignment_proc_begin_indices[i];
                            part_assignment_proc_begin_indices[i] = next_proc_to_send_id;
                            processor_chains_in_parts[next_proc_to_send_id] = prev_begin;
 
                        }
                    }
                    //change the sent part
                    ++next_proc_to_send_index;
 
#ifdef MJ_DEBUG
                    if(next_part_to_send_index <  nprocs - required_proc_count ){
                       std::cout << "Migration - processor assignments - for part:"
                                        << i
                                        <<  " next_part_to_send :" << next_part_to_send_index
                                        << " nprocs:" << nprocs
                                        << " required_proc_count:" << required_proc_count
                                        << " Error: next_part_to_send_index <  nprocs - required_proc_count" << std::endl;
                        exit(1)l
 
                    }
#endif
                    //send the new id.
                    next_proc_to_send_id =  sort_item_num_part_points_in_procs[next_proc_to_send_index].id;
                    //set the new space in the processor.
                    space_left_in_sent_proc = ideal_num_points_in_a_proc - sort_item_num_part_points_in_procs[next_proc_to_send_index].val;
                }
            } 
      }
      break;
      default:
      {
    //to minimize messages, we want each processor to send its coordinates to only a single point.
    //we do not respect imbalances here, we send all points to the next processor.
    if (this->myRank == nonassigned_proc_id){
                  //set my sent count to the sent processor.
                  send_count_to_each_proc[next_proc_to_send_id] = num_points_to_sent;
                  //save the processor in the list (processor_chains_in_parts and part_assignment_proc_begin_indices)
                  //that the processor will send its point in part-i.
                  mj_part_t prev_begin = part_assignment_proc_begin_indices[i];
                  part_assignment_proc_begin_indices[i] = next_proc_to_send_id;
                  processor_chains_in_parts[next_proc_to_send_id] = prev_begin;
                }
                num_points_to_sent = 0;
                ++next_proc_to_send_index;
    
    //if we made it to the heaviest processor we round robin and go to beginning
    if (next_proc_to_send_index == num_procs){
            next_proc_to_send_index = num_procs - required_proc_count;
    }
                //send the new id.
                next_proc_to_send_id =  sort_item_num_part_points_in_procs[next_proc_to_send_index].id;
                //set the new space in the processor.
                space_left_in_sent_proc = ideal_num_points_in_a_proc - sort_item_num_part_points_in_procs[next_proc_to_send_index].val;
      } 
          }
        }
    }
    
    /*
    for (int i = 0; i < num_procs;++i){
      std::cout << "me:" << this->myRank << " to part:" << i << " sends:" <<  send_count_to_each_proc[i] << std::endl;
    } 
    */  
 
 
    this->assign_send_destinations(
                num_parts,
            part_assignment_proc_begin_indices,
            processor_chains_in_parts,
            send_count_to_each_proc,
            coordinate_destinations);
 
    freeArray<mj_part_t>(part_assignment_proc_begin_indices);
    freeArray<mj_part_t>(processor_chains_in_parts);
    freeArray<mj_part_t>(processor_part_assignments);
    freeArray<uSignedSortItem<mj_part_t, mj_gno_t, char> > (sort_item_num_part_points_in_procs);   
    freeArray<mj_part_t > (num_procs_assigned_to_each_part);
 
}
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::assign_send_destinations2(
    mj_part_t num_parts,
    uSortItem<mj_part_t, mj_part_t> * sort_item_part_to_proc_assignment, //input sorted wrt processors
    int *coordinate_destinations,
    mj_part_t &output_part_numbering_begin_index,
    std::vector<mj_part_t> *next_future_num_parts_in_parts){
 
    mj_part_t part_shift_amount = output_part_numbering_begin_index;
    mj_part_t previous_processor = -1;
    for(mj_part_t i = 0; i < num_parts; ++i){
        mj_part_t p = sort_item_part_to_proc_assignment[i].id;
        //assigned processors are sorted.
        mj_lno_t part_begin_index = 0;
        if (p > 0) part_begin_index = this->new_part_xadj[p - 1];
        mj_lno_t part_end_index = this->new_part_xadj[p];
 
        mj_part_t assigned_proc = sort_item_part_to_proc_assignment[i].val;
        if (this->myRank == assigned_proc && previous_processor != assigned_proc){
            output_part_numbering_begin_index =  part_shift_amount;
        }
        previous_processor = assigned_proc;
        part_shift_amount += (*next_future_num_parts_in_parts)[p];
 
        for (mj_lno_t j=part_begin_index; j < part_end_index; j++){
            mj_lno_t localInd = this->new_coordinate_permutations[j];
            coordinate_destinations[localInd] = assigned_proc;
        }
    }
}
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::mj_assign_parts_to_procs(
    mj_gno_t * num_points_in_all_processor_parts,
    mj_part_t num_parts,
    mj_part_t num_procs,
    mj_lno_t *send_count_to_each_proc, //output: sized nprocs, show the number of send point counts to each proc.
    std::vector<mj_part_t> *next_future_num_parts_in_parts,//input how many more partitions the part will be partitioned into.
    mj_part_t &out_num_part, //output, how many parts the processor will have. this is always 1 for this function.
    std::vector<mj_part_t> &out_part_indices, //output: the part indices which the processor is assigned to.
    mj_part_t &output_part_numbering_begin_index, //output: how much the part number should be shifted when setting the solution
    int *coordinate_destinations){
    out_num_part = 0;
 
    mj_gno_t *global_num_points_in_parts = num_points_in_all_processor_parts + num_procs * num_parts;
    out_part_indices.clear();
 
    //to sort the parts that is assigned to the processors.
    //id is the part number, sort value is the assigned processor id.
    uSortItem<mj_part_t, mj_part_t> * sort_item_part_to_proc_assignment  = allocMemory <uSortItem<mj_part_t, mj_part_t> >(num_parts);
    uSortItem<mj_part_t, mj_gno_t> * sort_item_num_points_of_proc_in_part_i = allocMemory <uSortItem<mj_part_t, mj_gno_t> >(num_procs);
 
 
    //calculate the optimal number of coordinates that should be assigned to each processor.
    mj_lno_t work_each = mj_lno_t (this->num_global_coords / (double (num_procs)) + 0.5f);
    //to hold the left space as the number of coordinates to the optimal number in each proc.
    mj_lno_t *space_in_each_processor = allocMemory <mj_lno_t>(num_procs);
    //initialize left space in each.
    for (mj_part_t i = 0; i < num_procs; ++i){
        space_in_each_processor[i] = work_each;
    }
 
    //we keep track of how many parts each processor is assigned to.
    //because in some weird inputs, it might be possible that some
    //processors is not assigned to any part. Using these variables,
    //we force each processor to have at least one part.
    mj_part_t *num_parts_proc_assigned = allocMemory <mj_part_t>(num_procs);
    memset(num_parts_proc_assigned, 0, sizeof(mj_part_t) * num_procs);
    int empty_proc_count = num_procs;
 
    //to sort the parts with decreasing order of their coordiantes.
    //id are the part numbers, sort value is the number of points in each.
    uSortItem<mj_part_t, mj_gno_t> * sort_item_point_counts_in_parts  = allocMemory <uSortItem<mj_part_t, mj_gno_t> >(num_parts);
 
    //initially we will sort the parts according to the number of coordinates they have.
    //so that we will start assigning with the part that has the most number of coordinates.
    for (mj_part_t i = 0; i < num_parts; ++i){
        sort_item_point_counts_in_parts[i].id = i;
        sort_item_point_counts_in_parts[i].val = global_num_points_in_parts[i];
    }
    //sort parts with increasing order of loads.
    uqsort<mj_part_t, mj_gno_t>(num_parts, sort_item_point_counts_in_parts);
 
 
    //assigning parts to the processors
    //traverse the part win decreasing order of load.
    //first assign the heaviest part.
    for (mj_part_t j = 0; j < num_parts; ++j){
        //sorted with increasing order, traverse inverse.
        mj_part_t i = sort_item_point_counts_in_parts[num_parts - 1 - j].id;
        //load of the part
        mj_gno_t load = global_num_points_in_parts[i];
 
        //assigned processors
        mj_part_t assigned_proc = -1;
        //if not fit best processor.
        mj_part_t best_proc_to_assign = 0;
 
 
        //sort processors with increasing number of points in this part.
        for (mj_part_t ii = 0; ii < num_procs; ++ii){
            sort_item_num_points_of_proc_in_part_i[ii].id = ii;
 
            //if there are still enough parts to fill empty processors, than proceed normally.
            //but if empty processor count is equal to the number of part, then
            //we force to part assignments only to empty processors.
            if (empty_proc_count < num_parts - j || num_parts_proc_assigned[ii] == 0){
                //how many points processor ii has in part i?
                sort_item_num_points_of_proc_in_part_i[ii].val =  num_points_in_all_processor_parts[ii * num_parts + i];
            }
            else {
                sort_item_num_points_of_proc_in_part_i[ii].val  = -1;
            }
        }
        uqsort<mj_part_t, mj_gno_t>(num_procs, sort_item_num_points_of_proc_in_part_i);
 
        //traverse all processors with decreasing load.
        for (mj_part_t iii = num_procs - 1; iii >= 0; --iii){
            mj_part_t ii = sort_item_num_points_of_proc_in_part_i[iii].id;
            mj_lno_t left_space = space_in_each_processor[ii] - load;
            //if enought space, assign to this part.
            if(left_space >= 0 ){
                assigned_proc = ii;
                break;
            }
            //if space is not enough, store the best candidate part.
            if (space_in_each_processor[best_proc_to_assign] < space_in_each_processor[ii]){
                best_proc_to_assign = ii;
            }
        }
 
        //if none had enough space, then assign it to best part.
        if (assigned_proc == -1){
            assigned_proc = best_proc_to_assign;
        }
 
        if (num_parts_proc_assigned[assigned_proc]++ == 0){
                --empty_proc_count;
        }
        space_in_each_processor[assigned_proc] -= load;
        //to sort later, part-i is assigned to the proccessor - assignment.
        sort_item_part_to_proc_assignment[j].id = i; //part i
        sort_item_part_to_proc_assignment[j].val = assigned_proc; //assigned to processor - assignment.
 
 
        //if assigned processor is me, increase the number.
        if (assigned_proc == this->myRank){
            out_num_part++;//assigned_part_count;
            out_part_indices.push_back(i);
        }
        //increase the send to that processor by the number of points in that part.
        //as everyone send their coordiantes in this part to the processor assigned to this part.
        send_count_to_each_proc[assigned_proc] += num_points_in_all_processor_parts[this->myRank * num_parts + i];
    }
    freeArray<mj_part_t>(num_parts_proc_assigned);
    freeArray< uSortItem<mj_part_t, mj_gno_t> > (sort_item_num_points_of_proc_in_part_i);
    freeArray<uSortItem<mj_part_t, mj_gno_t> >(sort_item_point_counts_in_parts);
    freeArray<mj_lno_t >(space_in_each_processor);
 
 
    //sort assignments with respect to the assigned processors.
    uqsort<mj_part_t, mj_part_t>(num_parts, sort_item_part_to_proc_assignment);
    //fill sendBuf.
 
 
    this->assign_send_destinations2(
            num_parts,
            sort_item_part_to_proc_assignment,
            coordinate_destinations,
            output_part_numbering_begin_index,
            next_future_num_parts_in_parts);
 
    freeArray<uSortItem<mj_part_t, mj_part_t> >(sort_item_part_to_proc_assignment);
}
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::mj_migration_part_proc_assignment(
    mj_gno_t * num_points_in_all_processor_parts,
    mj_part_t num_parts,
    mj_part_t num_procs,
    mj_lno_t *send_count_to_each_proc,
    std::vector<mj_part_t> &processor_ranks_for_subcomm,
    std::vector<mj_part_t> *next_future_num_parts_in_parts,
    mj_part_t &out_num_part,
    std::vector<mj_part_t> &out_part_indices,
    mj_part_t &output_part_numbering_begin_index,
    int *coordinate_destinations){
 
 
 
        processor_ranks_for_subcomm.clear();
        // if (this->num_local_coords > 0)
        if (num_procs > num_parts){
                //if there are more processors than the number of current part
                //then processors share the existing parts.
                //at the end each processor will have a single part,
                //but a part will be shared by a group of processors.
                mj_part_t out_part_index = 0;
                this->mj_assign_proc_to_parts(
                                num_points_in_all_processor_parts,
                                num_parts,
                                num_procs,
                                send_count_to_each_proc,
                                processor_ranks_for_subcomm,
                                next_future_num_parts_in_parts,
                                out_part_index,
                                output_part_numbering_begin_index,
                                coordinate_destinations
                );
 
                out_num_part = 1;
                out_part_indices.clear();
                out_part_indices.push_back(out_part_index);
        }
        else {
 
                //there are more parts than the processors.
                //therefore a processor will be assigned multiple parts,
                //the subcommunicators will only have a single processor.
                processor_ranks_for_subcomm.push_back(this->myRank);
 
                //since there are more parts then procs,
                //assign multiple parts to processors.
                this->mj_assign_parts_to_procs(
                                num_points_in_all_processor_parts,
                                num_parts,
                                num_procs,
                                send_count_to_each_proc,
                                next_future_num_parts_in_parts,
                                out_num_part,
                                out_part_indices,
                                output_part_numbering_begin_index,
                                coordinate_destinations);
        }
}
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::mj_migrate_coords(
    mj_part_t num_procs,
    mj_lno_t &num_new_local_points,
    std::string iteration,
    int *coordinate_destinations,
    mj_part_t num_parts)
{
#ifdef ENABLE_ZOLTAN_MIGRATION
    if (sizeof(mj_lno_t) <= sizeof(int)) {
 
        // Cannot use Zoltan_Comm with local ordinals larger than ints.
        // In Zoltan_Comm_Create, the cast int(this->num_local_coords)
        // may overflow.
 
        ZOLTAN_COMM_OBJ *plan = NULL;
        MPI_Comm mpi_comm = Teuchos::getRawMpiComm(*(this->comm));
        int num_incoming_gnos = 0;
        int message_tag = 7859;
 
        this->mj_env->timerStart(MACRO_TIMERS, "MultiJagged - Migration Z1PlanCreating-" + iteration);
        int ierr = Zoltan_Comm_Create(
                        &plan,
                        int(this->num_local_coords),
                        coordinate_destinations,
                        mpi_comm,
                        message_tag,
                        &num_incoming_gnos);
        Z2_ASSERT_VALUE(ierr, ZOLTAN_OK);
        this->mj_env->timerStop(MACRO_TIMERS, "MultiJagged - Migration Z1PlanCreating-" + iteration);
 
        this->mj_env->timerStart(MACRO_TIMERS, "MultiJagged - Migration Z1Migration-" + iteration);
        mj_gno_t *incoming_gnos = allocMemory< mj_gno_t>(num_incoming_gnos);
 
        //migrate gnos.
        message_tag++;
        ierr = Zoltan_Comm_Do(
                        plan,
                        message_tag,
                        (char *) this->current_mj_gnos,
                        sizeof(mj_gno_t),
                        (char *) incoming_gnos);
        Z2_ASSERT_VALUE(ierr, ZOLTAN_OK);
 
        freeArray<mj_gno_t>(this->current_mj_gnos);
        this->current_mj_gnos = incoming_gnos;
 
 
        //migrate coordinates
        for (int i = 0; i < this->coord_dim; ++i){
                message_tag++;
                mj_scalar_t *coord = this->mj_coordinates[i];
 
                this->mj_coordinates[i] = allocMemory<mj_scalar_t>(num_incoming_gnos);
                ierr = Zoltan_Comm_Do(
                                plan,
                                message_tag,
                                (char *) coord,
                                sizeof(mj_scalar_t),
                                (char *) this->mj_coordinates[i]);
                Z2_ASSERT_VALUE(ierr, ZOLTAN_OK);
                freeArray<mj_scalar_t>(coord);
        }
 
        //migrate weights.
        for (int i = 0; i < this->num_weights_per_coord; ++i){
                message_tag++;
                mj_scalar_t *weight = this->mj_weights[i];
 
                this->mj_weights[i] = allocMemory<mj_scalar_t>(num_incoming_gnos);
                ierr = Zoltan_Comm_Do(
                                plan,
                                message_tag,
                                (char *) weight,
                                sizeof(mj_scalar_t),
                                (char *) this->mj_weights[i]);
                Z2_ASSERT_VALUE(ierr, ZOLTAN_OK);
                freeArray<mj_scalar_t>(weight);
        }
 
 
        //migrate owners.
        int *coord_own = allocMemory<int>(num_incoming_gnos);
        message_tag++;
        ierr = Zoltan_Comm_Do(
                        plan,
                        message_tag,
                        (char *) this->owner_of_coordinate,
                        sizeof(int), (char *) coord_own);
        Z2_ASSERT_VALUE(ierr, ZOLTAN_OK);
        freeArray<int>(this->owner_of_coordinate);
        this->owner_of_coordinate = coord_own;
 
 
        //if num procs is less than num parts,
        //we need the part assigment arrays as well, since
        //there will be multiple parts in processor.
        mj_part_t *new_parts = allocMemory<mj_part_t>(num_incoming_gnos);
        if(num_procs < num_parts){
                message_tag++;
                ierr = Zoltan_Comm_Do(
                                plan,
                                message_tag,
                                (char *) this->assigned_part_ids,
                                sizeof(mj_part_t),
                                (char *) new_parts);
                Z2_ASSERT_VALUE(ierr, ZOLTAN_OK);
        }
        freeArray<mj_part_t>(this->assigned_part_ids);
        this->assigned_part_ids = new_parts;
 
        ierr = Zoltan_Comm_Destroy(&plan);
        Z2_ASSERT_VALUE(ierr, ZOLTAN_OK);
        num_new_local_points = num_incoming_gnos;
        this->mj_env->timerStop(MACRO_TIMERS, "MultiJagged - Migration Z1Migration-" + iteration);
    }
 
    else
 
#endif  // ENABLE_ZOLTAN_MIGRATION
    {
        this->mj_env->timerStart(MACRO_TIMERS, "MultiJagged - Migration DistributorPlanCreating-" + iteration);
        Tpetra::Distributor distributor(this->comm);
        ArrayView<const mj_part_t> destinations( coordinate_destinations, this->num_local_coords);
        mj_lno_t num_incoming_gnos = distributor.createFromSends(destinations);
        this->mj_env->timerStop(MACRO_TIMERS, "MultiJagged - Migration DistributorPlanCreating-" + iteration);
 
        this->mj_env->timerStart(MACRO_TIMERS, "MultiJagged - Migration DistributorMigration-" + iteration);
        {
                //migrate gnos.
                ArrayRCP<mj_gno_t> received_gnos(num_incoming_gnos);
                ArrayView<mj_gno_t> sent_gnos(this->current_mj_gnos, this->num_local_coords);
                distributor.doPostsAndWaits<mj_gno_t>(sent_gnos, 1, received_gnos());
                freeArray<mj_gno_t>(this->current_mj_gnos);
                this->current_mj_gnos = allocMemory<mj_gno_t>(num_incoming_gnos);
                memcpy(
                                this->current_mj_gnos,
                                received_gnos.getRawPtr(),
                                num_incoming_gnos * sizeof(mj_gno_t));
        }
        //migrate coordinates
        for (int i = 0; i < this->coord_dim; ++i){
 
                ArrayView<mj_scalar_t> sent_coord(this->mj_coordinates[i], this->num_local_coords);
                ArrayRCP<mj_scalar_t> received_coord(num_incoming_gnos);
                distributor.doPostsAndWaits<mj_scalar_t>(sent_coord, 1, received_coord());
                freeArray<mj_scalar_t>(this->mj_coordinates[i]);
                this->mj_coordinates[i] = allocMemory<mj_scalar_t>(num_incoming_gnos);
                memcpy(
                                this->mj_coordinates[i],
                                received_coord.getRawPtr(),
                                num_incoming_gnos * sizeof(mj_scalar_t));
        }
 
        //migrate weights.
        for (int i = 0; i < this->num_weights_per_coord; ++i){
 
                ArrayView<mj_scalar_t> sent_weight(this->mj_weights[i], this->num_local_coords);
                ArrayRCP<mj_scalar_t> received_weight(num_incoming_gnos);
                distributor.doPostsAndWaits<mj_scalar_t>(sent_weight, 1, received_weight());
                freeArray<mj_scalar_t>(this->mj_weights[i]);
                this->mj_weights[i] = allocMemory<mj_scalar_t>(num_incoming_gnos);
                memcpy(
                                this->mj_weights[i],
                                received_weight.getRawPtr(),
                                num_incoming_gnos * sizeof(mj_scalar_t));
        }
 
        {
                //migrate the owners of the coordinates
                ArrayView<int> sent_owners(this->owner_of_coordinate, this->num_local_coords);
                ArrayRCP<int> received_owners(num_incoming_gnos);
                distributor.doPostsAndWaits<int>(sent_owners, 1, received_owners());
                freeArray<int>(this->owner_of_coordinate);
                this->owner_of_coordinate = allocMemory<int>(num_incoming_gnos);
                memcpy(
                                                this->owner_of_coordinate,
                                                received_owners.getRawPtr(),
                                                num_incoming_gnos * sizeof(int));
        }
 
        //if num procs is less than num parts,
        //we need the part assigment arrays as well, since
        //there will be multiple parts in processor.
        if(num_procs < num_parts){
                ArrayView<mj_part_t> sent_partids(this->assigned_part_ids, this->num_local_coords);
                ArrayRCP<mj_part_t> received_partids(num_incoming_gnos);
                distributor.doPostsAndWaits<mj_part_t>(sent_partids, 1, received_partids());
                freeArray<mj_part_t>(this->assigned_part_ids);
                this->assigned_part_ids = allocMemory<mj_part_t>(num_incoming_gnos);
                memcpy(
                                this->assigned_part_ids,
                                received_partids.getRawPtr(),
                                num_incoming_gnos * sizeof(mj_part_t));
        }
        else {
                mj_part_t *new_parts = allocMemory<int>(num_incoming_gnos);
                freeArray<mj_part_t>(this->assigned_part_ids);
                this->assigned_part_ids = new_parts;
        }
        this->mj_env->timerStop(MACRO_TIMERS, "MultiJagged - Migration DistributorMigration-" + iteration);
        num_new_local_points = num_incoming_gnos;
 
    }
}
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::create_sub_communicator(std::vector<mj_part_t> &processor_ranks_for_subcomm){
    mj_part_t group_size = processor_ranks_for_subcomm.size();
    mj_part_t *ids = allocMemory<mj_part_t>(group_size);
    for(mj_part_t i = 0; i < group_size; ++i) {
        ids[i] = processor_ranks_for_subcomm[i];
    }
    ArrayView<const mj_part_t> idView(ids, group_size);
    this->comm = this->comm->createSubcommunicator(idView);
    freeArray<mj_part_t>(ids);
}
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::fill_permutation_array(
    mj_part_t output_num_parts,
    mj_part_t num_parts){
        //if there is single output part, then simply fill the permutation array.
    if (output_num_parts == 1){
        for(mj_lno_t i = 0; i < this->num_local_coords; ++i){
            this->new_coordinate_permutations[i] = i;
        }
        this->new_part_xadj[0] = this->num_local_coords;
    }
    else {
 
        //otherwise we need to count how many points are there in each part.
        //we allocate here as num_parts, because the sent partids are up to num_parts,
        //although there are outout_num_parts different part.
        mj_lno_t *num_points_in_parts = allocMemory<mj_lno_t>(num_parts);
        //part shift holds the which part number an old part number corresponds to.
        mj_part_t *part_shifts = allocMemory<mj_part_t>(num_parts);
 
        memset(num_points_in_parts, 0, sizeof(mj_lno_t) * num_parts);
 
        for(mj_lno_t i = 0; i < this->num_local_coords; ++i){
            mj_part_t ii = this->assigned_part_ids[i];
            ++num_points_in_parts[ii];
        }
 
        //write the end points of the parts.
        mj_part_t p = 0;
        mj_lno_t prev_index = 0;
        for(mj_part_t i = 0; i < num_parts; ++i){
            if(num_points_in_parts[i] > 0)  {
                this->new_part_xadj[p] =  prev_index + num_points_in_parts[i];
                prev_index += num_points_in_parts[i];
                part_shifts[i] = p++;
            }
        }
 
        //for the rest of the parts write the end index as end point.
        mj_part_t assigned_num_parts = p - 1;
        for (;p < num_parts; ++p){
            this->new_part_xadj[p] =  this->new_part_xadj[assigned_num_parts];
        }
        for(mj_part_t i = 0; i < output_num_parts; ++i){
            num_points_in_parts[i] = this->new_part_xadj[i];
        }
 
        //write the permutation array here.
        //get the part of the coordinate i, shift it to obtain the new part number.
        //assign it to the end of the new part numbers pointer.
        for(mj_lno_t i = this->num_local_coords - 1; i >= 0; --i){
            mj_part_t part = part_shifts[mj_part_t(this->assigned_part_ids[i])];
            this->new_coordinate_permutations[--num_points_in_parts[part]] = i;
        }
 
        freeArray<mj_lno_t>(num_points_in_parts);
        freeArray<mj_part_t>(part_shifts);
    }
}
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
bool AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::mj_perform_migration(
    mj_part_t input_num_parts, //current number of parts
    mj_part_t &output_num_parts, //output number of parts.
    std::vector<mj_part_t> *next_future_num_parts_in_parts,
    mj_part_t &output_part_begin_index,
    size_t migration_reduce_all_population,
    mj_lno_t num_coords_for_last_dim_part,
    std::string iteration,
    RCP<mj_partBoxVector_t> &input_part_boxes,
    RCP<mj_partBoxVector_t> &output_part_boxes
)
{
        mj_part_t num_procs = this->comm->getSize();
        this->myRank = this->comm->getRank();
 
 
        //this array holds how many points each processor has in each part.
        //to access how many points processor i has on part j,
        //num_points_in_all_processor_parts[i * num_parts + j]
        mj_gno_t *num_points_in_all_processor_parts = allocMemory<mj_gno_t>(input_num_parts * (num_procs + 1));
 
        //get the number of coordinates in each part in each processor.
        this->get_processor_num_points_in_parts(
                        num_procs,
                        input_num_parts,
                        num_points_in_all_processor_parts);
 
 
        //check if migration will be performed or not.
        if (!this->mj_check_to_migrate(
                        migration_reduce_all_population,
                        num_coords_for_last_dim_part,
                        num_procs,
                        input_num_parts,
                        num_points_in_all_processor_parts)){
                freeArray<mj_gno_t>(num_points_in_all_processor_parts);
                return false;
        }
 
 
        mj_lno_t *send_count_to_each_proc = NULL;
        int *coordinate_destinations = allocMemory<int>(this->num_local_coords);
        send_count_to_each_proc = allocMemory<mj_lno_t>(num_procs);
        for (int i = 0; i < num_procs; ++i) send_count_to_each_proc[i] = 0;
 
        std::vector<mj_part_t> processor_ranks_for_subcomm;
        std::vector<mj_part_t> out_part_indices;
 
        //determine which processors are assigned to which parts
        this->mj_migration_part_proc_assignment(
                        num_points_in_all_processor_parts,
                        input_num_parts,
                        num_procs,
                        send_count_to_each_proc,
                        processor_ranks_for_subcomm,
                        next_future_num_parts_in_parts,
                        output_num_parts,
                        out_part_indices,
                        output_part_begin_index,
                        coordinate_destinations);
 
 
 
 
        freeArray<mj_lno_t>(send_count_to_each_proc);
        std::vector <mj_part_t> tmpv;
 
        std::sort (out_part_indices.begin(), out_part_indices.end());
        mj_part_t outP = out_part_indices.size();
 
        mj_gno_t new_global_num_points = 0;
        mj_gno_t *global_num_points_in_parts = num_points_in_all_processor_parts + num_procs * input_num_parts;
 
        if (this->mj_keep_part_boxes){
                input_part_boxes->clear();
        }
 
        //now we calculate the new values for next_future_num_parts_in_parts.
        //same for the part boxes.
        for (mj_part_t i = 0; i < outP; ++i){
                mj_part_t ind = out_part_indices[i];
                new_global_num_points += global_num_points_in_parts[ind];
                tmpv.push_back((*next_future_num_parts_in_parts)[ind]);
                if (this->mj_keep_part_boxes){
                        input_part_boxes->push_back((*output_part_boxes)[ind]);
                }
        }
        //swap the input and output part boxes.
        if (this->mj_keep_part_boxes){
                RCP<mj_partBoxVector_t> tmpPartBoxes = input_part_boxes;
                input_part_boxes = output_part_boxes;
                output_part_boxes = tmpPartBoxes;
        }
        next_future_num_parts_in_parts->clear();
        for (mj_part_t i = 0; i < outP; ++i){
                mj_part_t p = tmpv[i];
                next_future_num_parts_in_parts->push_back(p);
        }
 
        freeArray<mj_gno_t>(num_points_in_all_processor_parts);
 
        mj_lno_t num_new_local_points = 0;
 
 
        //perform the actual migration operation here.
        this->mj_migrate_coords(
                        num_procs,
                        num_new_local_points,
                        iteration,
                        coordinate_destinations,
                        input_num_parts);
 
 
        freeArray<int>(coordinate_destinations);
 
        if(this->num_local_coords != num_new_local_points){
                freeArray<mj_lno_t>(this->new_coordinate_permutations);
                freeArray<mj_lno_t>(this->coordinate_permutations);
 
                this->new_coordinate_permutations = allocMemory<mj_lno_t>(num_new_local_points);
                this->coordinate_permutations = allocMemory<mj_lno_t>(num_new_local_points);
        }
        this->num_local_coords = num_new_local_points;
        this->num_global_coords = new_global_num_points;
 
 
 
        //create subcommunicator.
        this->create_sub_communicator(processor_ranks_for_subcomm);
        processor_ranks_for_subcomm.clear();
 
        //fill the new permutation arrays.
        this->fill_permutation_array(
                        output_num_parts,
                        input_num_parts);
        return true;
}
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::create_consistent_chunks(
    mj_part_t num_parts,
    mj_scalar_t *mj_current_dim_coords,
    mj_scalar_t *current_concurrent_cut_coordinate,
    mj_lno_t coordinate_begin,
    mj_lno_t coordinate_end,
    mj_scalar_t *used_local_cut_line_weight_to_left,
    mj_lno_t *out_part_xadj,
    int coordInd, bool longest_dim_part, uSignedSortItem<int, mj_scalar_t, char> *p_coord_dimension_range_sorted){
 
        //mj_lno_t numCoordsInPart =  coordinateEnd - coordinateBegin;
        mj_part_t no_cuts = num_parts - 1;
 
 
 
        int me = 0;
        mj_lno_t *thread_num_points_in_parts = this->thread_point_counts[me];
        mj_scalar_t *my_local_thread_cut_weights_to_put_left = NULL;
 
 
        //now if the rectilinear partitioning is allowed we decide how
        //much weight each thread should put to left and right.
        if (this->distribute_points_on_cut_lines){
 
                my_local_thread_cut_weights_to_put_left = this->thread_cut_line_weight_to_put_left[me];
                for (mj_part_t i = 0; i < no_cuts; ++i){
                        //the left to be put on the left of the cut.
                        mj_scalar_t left_weight = used_local_cut_line_weight_to_left[i];
                        //std::cout << "i:" << i << " left_weight:" << left_weight << std::endl;
                        for(int ii = 0; ii < this->num_threads; ++ii){
                                if(left_weight > this->sEpsilon){
                                        //the weight of thread ii on cut.
                                        mj_scalar_t thread_ii_weight_on_cut = this->thread_part_weight_work[ii][i * 2 + 1] - this->thread_part_weight_work[ii][i * 2 ];
                                        if(thread_ii_weight_on_cut < left_weight){
                                                this->thread_cut_line_weight_to_put_left[ii][i] = thread_ii_weight_on_cut;
                                        }
                                        else {
                                                this->thread_cut_line_weight_to_put_left[ii][i] = left_weight ;
                                        }
                                        left_weight -= thread_ii_weight_on_cut;
                                }
                                else {
                                        this->thread_cut_line_weight_to_put_left[ii][i] = 0;
                                }
                        }
                }
 
                if(no_cuts > 0){
                        //this is a special case. If cutlines share the same coordinate, their weights are equal.
                        //we need to adjust the ratio for that.
                        for (mj_part_t i = no_cuts - 1; i > 0 ; --i){
                                if(ZOLTAN2_ABS(current_concurrent_cut_coordinate[i] - current_concurrent_cut_coordinate[i -1]) < this->sEpsilon){
                                        my_local_thread_cut_weights_to_put_left[i] -= my_local_thread_cut_weights_to_put_left[i - 1] ;
                                }
                                my_local_thread_cut_weights_to_put_left[i] = static_cast<long long>((my_local_thread_cut_weights_to_put_left[i] + LEAST_SIGNIFICANCE) * SIGNIFICANCE_MUL)
                                                                                                / mj_scalar_t(SIGNIFICANCE_MUL);
                        }
                }
        }
 
        for(mj_part_t ii = 0; ii < num_parts; ++ii){
                thread_num_points_in_parts[ii] = 0;
        }
 
        //for this specific case we dont want to distribute the points along the cut position
        //randomly, as we need a specific ordering of them. Instead,
        //we put the coordinates into a sort item, where we sort those
        //using the coordinates of points on other dimensions and the index.
 
 
        //some of the cuts might share the same position.
        //in this case, if cut i and cut j share the same position
        //cut_map[i] = cut_map[j] = sort item index.
        mj_part_t *cut_map = allocMemory<mj_part_t> (no_cuts);
 
 
        typedef uMultiSortItem<mj_lno_t, int, mj_scalar_t> multiSItem;
        typedef std::vector< multiSItem > multiSVector;
        typedef std::vector<multiSVector> multiS2Vector;
 
        //to keep track of the memory allocated.
        std::vector<mj_scalar_t *>allocated_memory;
 
        //vector for which the coordinates will be sorted.
        multiS2Vector sort_vector_points_on_cut;
 
        //the number of cuts that have different coordinates.
        mj_part_t different_cut_count = 1;
        cut_map[0] = 0;
 
        //now we insert 1 sort vector for all cuts on the different
        //positins.if multiple cuts are on the same position, they share sort vectors.
        multiSVector tmpMultiSVector;
        sort_vector_points_on_cut.push_back(tmpMultiSVector);
 
        for (mj_part_t i = 1; i < no_cuts ; ++i){
                //if cuts share the same cut coordinates
                //set the cutmap accordingly.
                if(ZOLTAN2_ABS(current_concurrent_cut_coordinate[i] - current_concurrent_cut_coordinate[i -1]) < this->sEpsilon){
                        cut_map[i] = cut_map[i-1];
                }
                else {
                        cut_map[i] = different_cut_count++;
                        multiSVector tmp2MultiSVector;
                        sort_vector_points_on_cut.push_back(tmp2MultiSVector);
                }
        }
 
 
        //now the actual part assigment.
        for (mj_lno_t ii = coordinate_begin; ii < coordinate_end; ++ii){
 
                mj_lno_t i = this->coordinate_permutations[ii];
 
                mj_part_t pp = this->assigned_part_ids[i];
                mj_part_t p = pp / 2;
                //if the coordinate is on a cut.
                if(pp % 2 == 1 ){
                        mj_scalar_t *vals = allocMemory<mj_scalar_t>(this->coord_dim -1);
                        allocated_memory.push_back(vals);
 
                        //we insert the coordinates to the sort item here.
                        int val_ind = 0;
 
                        if (longest_dim_part){
                          //std::cout << std::endl << std::endl;
                          for(int dim = this->coord_dim - 2; dim >= 0; --dim){
                            //uSignedSortItem<int, mj_scalar_t, char> *p_coord_dimension_range_sorted
                            int next_largest_coord_dim = p_coord_dimension_range_sorted[dim].id;
                            //std::cout << "next_largest_coord_dim: " << next_largest_coord_dim << " ";
                            vals[val_ind++] = this->mj_coordinates[next_largest_coord_dim][i];
                          }
                        }
                        else {
                          for(int dim = coordInd + 1; dim < this->coord_dim; ++dim){
                            vals[val_ind++] = this->mj_coordinates[dim][i];
                          }
                          for(int dim = 0; dim < coordInd; ++dim){
                            vals[val_ind++] = this->mj_coordinates[dim][i];
                          }
                        }
                        multiSItem tempSortItem(i, this->coord_dim -1, vals);
                        //inser the point to the sort vector pointed by the cut_map[p].
                        mj_part_t cmap = cut_map[p];
                        sort_vector_points_on_cut[cmap].push_back(tempSortItem);
                }
                else {
                        //if it is not on the cut, simple sorting.
                        ++thread_num_points_in_parts[p];
                        this->assigned_part_ids[i] = p;
                }
        }
 
        //sort all the sort vectors.
        for (mj_part_t i = 0; i < different_cut_count; ++i){
                std::sort (sort_vector_points_on_cut[i].begin(), sort_vector_points_on_cut[i].end());
        }
 
        //we do the part assignment for the points on cuts here.
        mj_part_t previous_cut_map = cut_map[0];
 
        //this is how much previous part owns the weight of the current part.
        //when target part weight is 1.6, and the part on the left is given 2,
        //the left has an extra 0.4, while the right has missing 0.4 from the previous cut.
        //this parameter is used to balance this issues.
        //in the above example weight_stolen_from_previous_part will be 0.4.
        //if the left part target is 2.2 but it is given 2,
        //then weight_stolen_from_previous_part will be -0.2.
        mj_scalar_t weight_stolen_from_previous_part = 0;
        for (mj_part_t p = 0; p < no_cuts; ++p){
 
                mj_part_t mapped_cut = cut_map[p];
 
                //if previous cut map is done, and it does not have the same index,
                //then assign all points left on that cut to its right.
                if (previous_cut_map != mapped_cut){
                        mj_lno_t sort_vector_end = (mj_lno_t)sort_vector_points_on_cut[previous_cut_map].size() - 1;
                        for (; sort_vector_end >= 0; --sort_vector_end){
                                multiSItem t = sort_vector_points_on_cut[previous_cut_map][sort_vector_end];
                                mj_lno_t i = t.index;
                                ++thread_num_points_in_parts[p];
                                this->assigned_part_ids[i] = p;
                        }
                        sort_vector_points_on_cut[previous_cut_map].clear();
                }
 
                //TODO: MD: I dont remember why I have it reverse order here.
                mj_lno_t sort_vector_end = (mj_lno_t)sort_vector_points_on_cut[mapped_cut].size() - 1;
                //mj_lno_t sort_vector_begin= 0;
                //mj_lno_t sort_vector_size = (mj_lno_t)sort_vector_points_on_cut[mapped_cut].size();
 
                //TODO commented for reverse order
                for (; sort_vector_end >= 0; --sort_vector_end){
                //for (; sort_vector_begin < sort_vector_size; ++sort_vector_begin){
                        //TODO COMMENTED FOR REVERSE ORDER
                        multiSItem t = sort_vector_points_on_cut[mapped_cut][sort_vector_end];
                        //multiSItem t = sort_vector_points_on_cut[mapped_cut][sort_vector_begin];
                        mj_lno_t i = t.index;
                        mj_scalar_t w = this->mj_uniform_weights[0]? 1:this->mj_weights[0][i];
 
 
                        //part p has enough space for point i, then put it to point i.
                        if(     my_local_thread_cut_weights_to_put_left[p] + weight_stolen_from_previous_part> this->sEpsilon &&
                                my_local_thread_cut_weights_to_put_left[p] + weight_stolen_from_previous_part - ZOLTAN2_ABS(my_local_thread_cut_weights_to_put_left[p] + weight_stolen_from_previous_part - w)
                                        > this->sEpsilon){
 
                                my_local_thread_cut_weights_to_put_left[p] -= w;
                                sort_vector_points_on_cut[mapped_cut].pop_back();
                                ++thread_num_points_in_parts[p];
                                this->assigned_part_ids[i] = p;
                                //if putting this weight to left overweights the left cut, then
                                //increase the space for the next cut using weight_stolen_from_previous_part.
                                if(p < no_cuts - 1 && my_local_thread_cut_weights_to_put_left[p] < this->sEpsilon){
                                        if(mapped_cut == cut_map[p + 1] ){
                                                //if the cut before the cut indexed at p was also at the same position
                                                //special case, as we handle the weight differently here.
                                                if (previous_cut_map != mapped_cut){
                                                        weight_stolen_from_previous_part = my_local_thread_cut_weights_to_put_left[p];
                                                }
                                                else {
                                                        //if the cut before the cut indexed at p was also at the same position
                                                        //we assign extra weights cumulatively in this case.
                                                        weight_stolen_from_previous_part += my_local_thread_cut_weights_to_put_left[p];
                                                }
                                        }
                                        else{
                                                weight_stolen_from_previous_part = -my_local_thread_cut_weights_to_put_left[p];
                                        }
                                        //end assignment for part p
                                        break;
                                }
                        } else {
                                //if part p does not have enough space for this point
                                //and if there is another cut sharing the same positon,
                                //again increase the space for the next
                                if(p < no_cuts - 1 && mapped_cut == cut_map[p + 1]){
                                        if (previous_cut_map != mapped_cut){
                                                weight_stolen_from_previous_part = my_local_thread_cut_weights_to_put_left[p];
                                        }
                                        else {
                                                weight_stolen_from_previous_part += my_local_thread_cut_weights_to_put_left[p];
                                        }
                                }
                                else{
                                        weight_stolen_from_previous_part = -my_local_thread_cut_weights_to_put_left[p];
                                }
                                //end assignment for part p
                                break;
                        }
                }
                previous_cut_map = mapped_cut;
        }
 
        //TODO commented for reverse order
        //put everything left on the last cut to the last part.
        mj_lno_t sort_vector_end = (mj_lno_t)sort_vector_points_on_cut[previous_cut_map].size() - 1;
 
        //mj_lno_t sort_vector_begin= 0;
        //mj_lno_t sort_vector_size = (mj_lno_t)sort_vector_points_on_cut[previous_cut_map].size();
        //TODO commented for reverse order
        for (; sort_vector_end >= 0; --sort_vector_end){
        //for (; sort_vector_begin < sort_vector_size; ++sort_vector_begin){
                //TODO commented for reverse order
                multiSItem t = sort_vector_points_on_cut[previous_cut_map][sort_vector_end];
                //multiSItem t = sort_vector_points_on_cut[previous_cut_map][sort_vector_begin];
                mj_lno_t i = t.index;
                ++thread_num_points_in_parts[no_cuts];
                this->assigned_part_ids[i] = no_cuts;
        }
        sort_vector_points_on_cut[previous_cut_map].clear();
        freeArray<mj_part_t> (cut_map);
 
        //free the memory allocated for vertex sort items .
        mj_lno_t vSize = (mj_lno_t) allocated_memory.size();
        for(mj_lno_t i = 0; i < vSize; ++i){
                freeArray<mj_scalar_t> (allocated_memory[i]);
        }
 
        //creation of part_xadj as in usual case.
        for(mj_part_t j = 0; j < num_parts; ++j){
                mj_lno_t num_points_in_part_j_upto_thread_i = 0;
                for (int i = 0; i < this->num_threads; ++i){
                        mj_lno_t thread_num_points_in_part_j = this->thread_point_counts[i][j];
                        this->thread_point_counts[i][j] = num_points_in_part_j_upto_thread_i;
                        num_points_in_part_j_upto_thread_i += thread_num_points_in_part_j;
 
                }
                out_part_xadj[j] = num_points_in_part_j_upto_thread_i;// + prev2; //+ coordinateBegin;
        }
 
        //perform prefix sum for num_points in parts.
        for(mj_part_t j = 1; j < num_parts; ++j){
                out_part_xadj[j] += out_part_xadj[j - 1];
        }
 
 
        //shift the num points in threads thread to obtain the
        //beginning index of each thread's private space.
        for(mj_part_t j = 1; j < num_parts; ++j){
                thread_num_points_in_parts[j] += out_part_xadj[j - 1] ;
        }
 
        //now thread gets the coordinate and writes the index of coordinate to the permutation array
        //using the part index we calculated.
        for (mj_lno_t ii = coordinate_begin; ii < coordinate_end; ++ii){
                mj_lno_t i = this->coordinate_permutations[ii];
                mj_part_t p =  this->assigned_part_ids[i];
                this->new_coordinate_permutations[coordinate_begin +
                                                  thread_num_points_in_parts[p]++] = i;
        }
}
 
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::set_final_parts(
                mj_part_t current_num_parts,
                mj_part_t output_part_begin_index,
                RCP<mj_partBoxVector_t> &output_part_boxes,
                bool is_data_ever_migrated)
{
    this->mj_env->timerStart(MACRO_TIMERS, "MultiJagged - Part_Assignment");
 
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp parallel for
#endif
    for(mj_part_t i = 0; i < current_num_parts;++i){
 
        mj_lno_t begin = 0;
        mj_lno_t end = this->part_xadj[i];
 
        if(i > 0) begin = this->part_xadj[i -1];
        mj_part_t part_to_set_index = i + output_part_begin_index;
        if (this->mj_keep_part_boxes){
                (*output_part_boxes)[i].setpId(part_to_set_index);
        }
        for (mj_lno_t ii = begin; ii < end; ++ii){
                mj_lno_t k = this->coordinate_permutations[ii];
                this->assigned_part_ids[k] = part_to_set_index;
        }
    }
 
    //ArrayRCP<const mj_gno_t> gnoList;
    if(!is_data_ever_migrated){
        //freeArray<mj_gno_t>(this->current_mj_gnos);
        //if(this->num_local_coords > 0){
        //    gnoList = arcpFromArrayView(this->mj_gnos);
        //}
    }
    else {
#ifdef ENABLE_ZOLTAN_MIGRATION
      if (sizeof(mj_lno_t) <=  sizeof(int)) {
 
        // Cannot use Zoltan_Comm with local ordinals larger than ints.
        // In Zoltan_Comm_Create, the cast int(this->num_local_coords)
        // may overflow.
 
        //if data is migrated, then send part numbers to the original owners.
        ZOLTAN_COMM_OBJ *plan = NULL;
        MPI_Comm mpi_comm = Teuchos::getRawMpiComm(*(this->mj_problemComm));
 
        int incoming = 0;
        int message_tag = 7856;
 
        this->mj_env->timerStart(MACRO_TIMERS, "MultiJagged - Final Z1PlanCreating");
        int ierr = Zoltan_Comm_Create( &plan, int(this->num_local_coords),
                        this->owner_of_coordinate, mpi_comm, message_tag,
                        &incoming);
        Z2_ASSERT_VALUE(ierr, ZOLTAN_OK);
        this->mj_env->timerStop(MACRO_TIMERS, "MultiJagged - Final Z1PlanCreating" );
 
        mj_gno_t *incoming_gnos = allocMemory< mj_gno_t>(incoming);
 
        message_tag++;
        this->mj_env->timerStart(MACRO_TIMERS, "MultiJagged - Final Z1PlanComm");
        ierr = Zoltan_Comm_Do( plan, message_tag, (char *) this->current_mj_gnos,
                        sizeof(mj_gno_t), (char *) incoming_gnos);
        Z2_ASSERT_VALUE(ierr, ZOLTAN_OK);
 
        freeArray<mj_gno_t>(this->current_mj_gnos);
        this->current_mj_gnos = incoming_gnos;
 
        mj_part_t *incoming_partIds = allocMemory< mj_part_t>(incoming);
 
        message_tag++;
        ierr = Zoltan_Comm_Do( plan, message_tag, (char *) this->assigned_part_ids,
                        sizeof(mj_part_t), (char *) incoming_partIds);
        Z2_ASSERT_VALUE(ierr, ZOLTAN_OK);
        freeArray<mj_part_t>(this->assigned_part_ids);
        this->assigned_part_ids = incoming_partIds;
 
        this->mj_env->timerStop(MACRO_TIMERS, "MultiJagged - Final Z1PlanComm");
        ierr = Zoltan_Comm_Destroy(&plan);
        Z2_ASSERT_VALUE(ierr, ZOLTAN_OK);
 
        this->num_local_coords = incoming;
        //gnoList = arcp(this->current_mj_gnos, 0, this->num_local_coords, true);
      }
      else
 
#endif  // !ENABLE_ZOLTAN_MIGRATION
      {
        //if data is migrated, then send part numbers to the original owners.
        this->mj_env->timerStart(MACRO_TIMERS, "MultiJagged - Final DistributorPlanCreating");
        Tpetra::Distributor distributor(this->mj_problemComm);
        ArrayView<const mj_part_t> owners_of_coords(this->owner_of_coordinate, this->num_local_coords);
        mj_lno_t incoming = distributor.createFromSends(owners_of_coords);
        this->mj_env->timerStop(MACRO_TIMERS, "MultiJagged - Final DistributorPlanCreating" );
 
        this->mj_env->timerStart(MACRO_TIMERS, "MultiJagged - Final DistributorPlanComm");
        //migrate gnos to actual owners.
        ArrayRCP<mj_gno_t> received_gnos(incoming);
        ArrayView<mj_gno_t> sent_gnos(this->current_mj_gnos, this->num_local_coords);
        distributor.doPostsAndWaits<mj_gno_t>(sent_gnos, 1, received_gnos());
        freeArray<mj_gno_t>(this->current_mj_gnos);
        this->current_mj_gnos = allocMemory<mj_gno_t>(incoming);
        memcpy( this->current_mj_gnos,
                received_gnos.getRawPtr(),
                incoming * sizeof(mj_gno_t));
 
                //migrate part ids to actual owners.
        ArrayView<mj_part_t> sent_partids(this->assigned_part_ids, this->num_local_coords);
        ArrayRCP<mj_part_t> received_partids(incoming);
        distributor.doPostsAndWaits<mj_part_t>(sent_partids, 1, received_partids());
        freeArray<mj_part_t>(this->assigned_part_ids);
        this->assigned_part_ids = allocMemory<mj_part_t>(incoming);
        memcpy( this->assigned_part_ids,
                received_partids.getRawPtr(),
                incoming * sizeof(mj_part_t));
 
        this->num_local_coords = incoming;
        this->mj_env->timerStop(MACRO_TIMERS, "MultiJagged - Final DistributorPlanComm");
 
      }
    }
 
    this->mj_env->timerStop(MACRO_TIMERS, "MultiJagged - Part_Assignment");
 
    this->mj_env->timerStart(MACRO_TIMERS, "MultiJagged - Solution_Part_Assignment");
 
    //ArrayRCP<mj_part_t> partId;
    //partId = arcp(this->assigned_part_ids, 0, this->num_local_coords, true);
 
    if (this->mj_keep_part_boxes){
        this->kept_boxes = compute_global_box_boundaries(output_part_boxes);
 
    }
 
    this->mj_env->timerStop(MACRO_TIMERS, "MultiJagged - Solution_Part_Assignment");
}
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::free_work_memory(){
        this->mj_env->timerStart(MACRO_TIMERS, "MultiJagged - Problem_Free");
 
        for (int i=0; i < this->coord_dim; i++){
                freeArray<mj_scalar_t>(this->mj_coordinates[i]);
        }
        freeArray<mj_scalar_t *>(this->mj_coordinates);
 
        for (int i=0; i < this->num_weights_per_coord; i++){
                freeArray<mj_scalar_t>(this->mj_weights[i]);
        }
        freeArray<mj_scalar_t *>(this->mj_weights);
 
        freeArray<int>(this->owner_of_coordinate);
 
        for(int i = 0; i < this->num_threads; ++i){
                freeArray<mj_lno_t>(this->thread_point_counts[i]);
        }
 
        freeArray<mj_lno_t *>(this->thread_point_counts);
        freeArray<double *> (this->thread_part_weight_work);
 
        if(this->distribute_points_on_cut_lines){
                freeArray<mj_scalar_t>(this->process_cut_line_weight_to_put_left);
                for(int i = 0; i < this->num_threads; ++i){
                        freeArray<mj_scalar_t>(this->thread_cut_line_weight_to_put_left[i]);
                }
                freeArray<mj_scalar_t *>(this->thread_cut_line_weight_to_put_left);
                freeArray<mj_scalar_t>(this->process_rectilinear_cut_weight);
                freeArray<mj_scalar_t>(this->global_rectilinear_cut_weight);
        }
 
        freeArray<mj_part_t>(this->my_incomplete_cut_count);
 
        freeArray<mj_scalar_t>(this->max_min_coords);
 
        freeArray<mj_lno_t>(this->part_xadj);
 
        freeArray<mj_lno_t>(this->coordinate_permutations);
 
        freeArray<mj_lno_t>(this->new_coordinate_permutations);
 
        freeArray<mj_scalar_t>(this->all_cut_coordinates);
 
        freeArray<mj_scalar_t> (this->process_local_min_max_coord_total_weight);
 
        freeArray<mj_scalar_t> (this->global_min_max_coord_total_weight);
 
        freeArray<mj_scalar_t>(this->cut_coordinates_work_array);
 
        freeArray<mj_scalar_t>(this->target_part_weights);
 
        freeArray<mj_scalar_t>(this->cut_upper_bound_coordinates);
 
        freeArray<mj_scalar_t>(this->cut_lower_bound_coordinates);
 
        freeArray<mj_scalar_t>(this->cut_lower_bound_weights);
        freeArray<mj_scalar_t>(this->cut_upper_bound_weights);
        freeArray<bool>(this->is_cut_line_determined);
        freeArray<mj_scalar_t>(this->total_part_weight_left_right_closests);
        freeArray<mj_scalar_t>(this->global_total_part_weight_left_right_closests);
 
        for(int i = 0; i < this->num_threads; ++i){
                freeArray<double>(this->thread_part_weights[i]);
                freeArray<mj_scalar_t>(this->thread_cut_right_closest_point[i]);
                freeArray<mj_scalar_t>(this->thread_cut_left_closest_point[i]);
        }
 
        freeArray<double *>(this->thread_part_weights);
        freeArray<mj_scalar_t *>(this->thread_cut_left_closest_point);
        freeArray<mj_scalar_t *>(this->thread_cut_right_closest_point);
 
        this->mj_env->timerStop(MACRO_TIMERS, "MultiJagged - Problem_Free");
}
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::set_partitioning_parameters(
                bool distribute_points_on_cut_lines_,
                int max_concurrent_part_calculation_,
                int check_migrate_avoid_migration_option_,
                double minimum_migration_imbalance_,
    int migration_type_ ){
        this->distribute_points_on_cut_lines = distribute_points_on_cut_lines_;
        this->max_concurrent_part_calculation = max_concurrent_part_calculation_;
        this->check_migrate_avoid_migration_option = check_migrate_avoid_migration_option_;
        this->minimum_migration_imbalance = minimum_migration_imbalance_;
  this->migration_type = migration_type_;
 
}
 
 
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
void AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t>::multi_jagged_part(
 
        const RCP<const Environment> &env,
        RCP<const Comm<int> > &problemComm,
 
        double imbalance_tolerance_,
        size_t num_global_parts_,
        mj_part_t *part_no_array_,
        int recursion_depth_,
 
        int coord_dim_,
        mj_lno_t num_local_coords_,
        mj_gno_t num_global_coords_,
        const mj_gno_t *initial_mj_gnos_,
        mj_scalar_t **mj_coordinates_,
 
        int num_weights_per_coord_,
        bool *mj_uniform_weights_,
        mj_scalar_t **mj_weights_,
        bool *mj_uniform_parts_,
        mj_scalar_t **mj_part_sizes_,
 
        mj_part_t *&result_assigned_part_ids_,
        mj_gno_t *&result_mj_gnos_
)
{
 
 
 
#ifdef print_debug
    if(comm->getRank() == 0){
        std::cout << "size of gno:" << sizeof(mj_gno_t) << std::endl;
        std::cout << "size of lno:" << sizeof(mj_lno_t) << std::endl;
        std::cout << "size of mj_scalar_t:" << sizeof(mj_scalar_t) << std::endl;
    }
#endif
    this->mj_env = env;
    this->mj_problemComm = problemComm;
    this->myActualRank = this->myRank = this->mj_problemComm->getRank();
 
    /*
    if (0)
    { 
      int a = rand();
      this->mj_problemComm->broadcast(0, sizeof(int), (char *) (&a));
      std::string istring = "output_" + Teuchos::toString<int>(a) + "_" + Teuchos::toString<int>(myRank) + ".mtx";
 
      std::ofstream output(istring.c_str());
      output << num_local_coords_ << " " << coord_dim_ << std::endl;
      for (int j = 0; j < coord_dim_ ; ++j){
        for (size_t i = 0; i < num_local_coords_; ++i){
          output << mj_coordinates_[j][i] << std::endl;
        }
    
      }
      output.close();
    }
    */
    
 
    this->mj_env->timerStart(MACRO_TIMERS, "MultiJagged - Total");
    this->mj_env->debug(3, "In MultiJagged Jagged");
 
    {
        this->imbalance_tolerance = imbalance_tolerance_;
        this->num_global_parts = num_global_parts_;
        this->part_no_array =  part_no_array_;
        this->recursion_depth = recursion_depth_;
 
        this->coord_dim = coord_dim_;
        this->num_local_coords = num_local_coords_;
        this->num_global_coords = num_global_coords_;
        this->mj_coordinates = mj_coordinates_; //will copy the memory to this->mj_coordinates.
        this->initial_mj_gnos = (mj_gno_t *) initial_mj_gnos_; //will copy the memory to this->current_mj_gnos[j].
 
        this->num_weights_per_coord = num_weights_per_coord_;
        this->mj_uniform_weights = mj_uniform_weights_;
        this->mj_weights = mj_weights_; //will copy the memory to this->mj_weights
        this->mj_uniform_parts = mj_uniform_parts_;
        this->mj_part_sizes = mj_part_sizes_;
 
        this->num_threads = 1;
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp parallel
 
        {
                this->num_threads = omp_get_num_threads();
        }
#endif
    }
    //this->set_input_data();
    this->set_part_specifications();
 
    this->allocate_set_work_memory();
 
    //We duplicate the comm as we create subcommunicators during migration.
    //We keep the problemComm as it is, while comm changes after each migration.
    this->comm = this->mj_problemComm->duplicate();
 
    //initially there is a single partition
    mj_part_t current_num_parts = 1;
    mj_scalar_t *current_cut_coordinates =  this->all_cut_coordinates;
 
    this->mj_env->timerStart(MACRO_TIMERS, "MultiJagged - Problem_Partitioning");
 
    mj_part_t output_part_begin_index = 0;
    mj_part_t future_num_parts = this->total_num_part;
    bool is_data_ever_migrated = false;
 
    std::vector<mj_part_t> *future_num_part_in_parts = new std::vector<mj_part_t> ();
    std::vector<mj_part_t> *next_future_num_parts_in_parts = new std::vector<mj_part_t> ();
    next_future_num_parts_in_parts->push_back(this->num_global_parts);
 
    RCP<mj_partBoxVector_t> input_part_boxes(new mj_partBoxVector_t(), true) ;
    RCP<mj_partBoxVector_t> output_part_boxes(new mj_partBoxVector_t(), true);
 
    compute_global_box();
    if(this->mj_keep_part_boxes){
        this->init_part_boxes(output_part_boxes);
    }
 
    for (int i = 0; i < this->recursion_depth; ++i){
        //partitioning array. size will be as the number of current partitions and this
        //holds how many parts that each part will be in the current dimension partitioning.
        std::vector <mj_part_t> num_partitioning_in_current_dim;
 
        //number of parts that will be obtained at the end of this partitioning.
        //future_num_part_in_parts is as the size of current number of parts.
        //holds how many more parts each should be divided in the further
        //iterations. this will be used to calculate num_partitioning_in_current_dim,
        //as the number of parts that the part will be partitioned
        //in the current dimension partitioning.
 
        //next_future_num_parts_in_parts will be as the size of outnumParts,
        //and this will hold how many more parts that each output part
        //should be divided. this array will also be used to determine the weight ratios
        //of the parts.
        //swap the arrays to use iteratively..
        std::vector<mj_part_t> *tmpPartVect= future_num_part_in_parts;
        future_num_part_in_parts = next_future_num_parts_in_parts;
        next_future_num_parts_in_parts = tmpPartVect;
 
        //clear next_future_num_parts_in_parts array as
        //getPartitionArrays expects it to be empty.
        //it also expects num_partitioning_in_current_dim to be empty as well.
        next_future_num_parts_in_parts->clear();
 
        if(this->mj_keep_part_boxes){
            RCP<mj_partBoxVector_t> tmpPartBoxes = input_part_boxes;
            input_part_boxes = output_part_boxes;
            output_part_boxes = tmpPartBoxes;
            output_part_boxes->clear();
        }
 
        //returns the total no. of output parts for this dimension partitioning.
        mj_part_t output_part_count_in_dimension =
                        this->update_part_num_arrays(
                                        num_partitioning_in_current_dim,
                                        future_num_part_in_parts,
                                        next_future_num_parts_in_parts,
                                        future_num_parts,
                                        current_num_parts,
                                        i,
                                        input_part_boxes,
                                        output_part_boxes, 1);
 
        //if the number of obtained parts equal to current number of parts,
        //skip this dimension. For example, this happens when 1 is given in the input
        //part array is given. P=4,5,1,2
        if(output_part_count_in_dimension == current_num_parts) {
            //still need to swap the input output arrays.
            tmpPartVect= future_num_part_in_parts;
            future_num_part_in_parts = next_future_num_parts_in_parts;
            next_future_num_parts_in_parts = tmpPartVect;
 
            if(this->mj_keep_part_boxes){
                RCP<mj_partBoxVector_t> tmpPartBoxes = input_part_boxes;
                input_part_boxes = output_part_boxes;
                output_part_boxes = tmpPartBoxes;
            }
            continue;
        }
 
 
        //get the coordinate axis along which the partitioning will be done.
        int coordInd = i % this->coord_dim;
        mj_scalar_t * mj_current_dim_coords = this->mj_coordinates[coordInd];
 
        //convert i to string to be used for debugging purposes.
        std::string istring = Teuchos::toString<int>(i);
        this->mj_env->timerStart(MACRO_TIMERS, "MultiJagged - Problem_Partitioning_" + istring);
 
        //alloc Memory to point the indices
        //of the parts in the permutation array.
        this->new_part_xadj = allocMemory<mj_lno_t>(output_part_count_in_dimension);
 
        //the index where in the new_part_xadj will be written.
        mj_part_t output_part_index = 0;
        //whatever is written to output_part_index will be added with putput_coordinate_end_index
        //so that the points will be shifted.
        mj_part_t output_coordinate_end_index = 0;
 
        mj_part_t current_work_part = 0;
        mj_part_t current_concurrent_num_parts =
                        std::min(current_num_parts - current_work_part, this->max_concurrent_part_calculation);
 
        mj_part_t obtained_part_index = 0;
 
        //run for all available parts.
        for (; current_work_part < current_num_parts;
                 current_work_part += current_concurrent_num_parts){
 
            current_concurrent_num_parts = std::min(current_num_parts - current_work_part,
                                 this->max_concurrent_part_calculation);
 
            mj_part_t actual_work_part_count = 0;
            //initialization for 1D partitioning.
            //get the min and max coordinates of each part
            //together with the part weights of each part.
            for(int kk = 0; kk < current_concurrent_num_parts; ++kk){
                mj_part_t current_work_part_in_concurrent_parts = current_work_part + kk;
 
                //if this part wont be partitioned any further
                //dont do any work for this part.
                if (num_partitioning_in_current_dim[current_work_part_in_concurrent_parts] == 1){
                    continue;
                }
                ++actual_work_part_count;
                mj_lno_t coordinate_end_index= this->part_xadj[current_work_part_in_concurrent_parts];
                mj_lno_t coordinate_begin_index = current_work_part_in_concurrent_parts == 0 ? 
                  0 : this->part_xadj[current_work_part_in_concurrent_parts - 1];
 
/*
               std::cout << "i:" << i << " j:" << current_work_part + kk
                                << " coordinate_begin_index:" << coordinate_begin_index
                                << " coordinate_end_index:" << coordinate_end_index
                                << " total:" << coordinate_end_index - coordinate_begin_index<< std::endl;
                                */
                this->mj_get_local_min_max_coord_totW(
                                coordinate_begin_index,
                                coordinate_end_index,
                                this->coordinate_permutations,
                                mj_current_dim_coords,
                            this->process_local_min_max_coord_total_weight[kk], //min_coordinate
                            this->process_local_min_max_coord_total_weight[kk + current_concurrent_num_parts], //max_coordinate
                            this->process_local_min_max_coord_total_weight[kk + 2*current_concurrent_num_parts]); //total_weight
 
            }
 
            //1D partitioning
            if (actual_work_part_count > 0){
                //obtain global Min max of the part.
                this->mj_get_global_min_max_coord_totW(
                                current_concurrent_num_parts,
                                this->process_local_min_max_coord_total_weight,
                                this->global_min_max_coord_total_weight);
 
                //represents the total number of cutlines
                //whose coordinate should be determined.
                mj_part_t total_incomplete_cut_count = 0;
 
                //Compute weight ratios for parts & cuts:
                //e.g., 0.25  0.25  0.5    0.5  0.75 0.75  1
                //part0  cut0  part1 cut1 part2 cut2 part3
                mj_part_t concurrent_part_cut_shift = 0;
                mj_part_t concurrent_part_part_shift = 0;
                for(int kk = 0; kk < current_concurrent_num_parts; ++kk){
                    mj_scalar_t min_coordinate = this->global_min_max_coord_total_weight[kk];
                    mj_scalar_t max_coordinate = this->global_min_max_coord_total_weight[kk +
                                                     current_concurrent_num_parts];
 
                    mj_scalar_t global_total_weight = this->global_min_max_coord_total_weight[kk +
                                                        2 * current_concurrent_num_parts];
 
                    mj_part_t concurrent_current_part_index = current_work_part + kk;
 
                    mj_part_t partition_count = num_partitioning_in_current_dim[concurrent_current_part_index];
 
                    mj_scalar_t *usedCutCoordinate = current_cut_coordinates + concurrent_part_cut_shift;
                    mj_scalar_t *current_target_part_weights = this->target_part_weights +
                                                        concurrent_part_part_shift;
                    //shift the usedCutCoordinate array as noCuts.
                    concurrent_part_cut_shift += partition_count - 1;
                    //shift the partRatio array as noParts.
                    concurrent_part_part_shift += partition_count;
 
 
                    //calculate only if part is not empty,
                    //and part will be further partitioned.
                    if(partition_count > 1 && min_coordinate <= max_coordinate){
 
                        //increase num_cuts_do_be_determined by the number of cuts of the current
                        //part's cut line number.
                        total_incomplete_cut_count += partition_count - 1;
                        //set the number of cut lines that should be determined
                        //for this part.
                        this->my_incomplete_cut_count[kk] = partition_count - 1;
 
                        //get the target weights of the parts.
                        this->mj_get_initial_cut_coords_target_weights(
                                        min_coordinate,
                                        max_coordinate,
                                        partition_count - 1,
                                        global_total_weight,
                                        usedCutCoordinate,
                                        current_target_part_weights,
                                        future_num_part_in_parts,
                                        next_future_num_parts_in_parts,
                                        concurrent_current_part_index,
                                        obtained_part_index);
 
                        mj_lno_t coordinate_end_index= this->part_xadj[concurrent_current_part_index];
                        mj_lno_t coordinate_begin_index = concurrent_current_part_index == 0 ? 
                          0 : this->part_xadj[concurrent_current_part_index - 1];
 
                        //get the initial estimated part assignments of the
                        //coordinates.
                        this->set_initial_coordinate_parts(
                            max_coordinate,
                            min_coordinate,
                            concurrent_current_part_index,
                            coordinate_begin_index, coordinate_end_index,
                            this->coordinate_permutations,
                            mj_current_dim_coords,
                            this->assigned_part_ids,
                            partition_count);
                    }
                    else {
                        // e.g., if have fewer coordinates than parts, don't need to do next dim.
                        this->my_incomplete_cut_count[kk] = 0;
                    }
                    obtained_part_index += partition_count;
                }
 
 
 
                //used imbalance, it is always 0, as it is difficult to
                //estimate a range.
                double used_imbalance = 0;
 
 
                // Determine cut lines for all concurrent parts parts here.
                this->mj_1D_part(
                    mj_current_dim_coords,
                    used_imbalance,
                    current_work_part,
                    current_concurrent_num_parts,
                    current_cut_coordinates,
                    total_incomplete_cut_count,
                    num_partitioning_in_current_dim);
            }
 
            //create new part chunks
            {
                mj_part_t output_array_shift = 0;
                mj_part_t cut_shift = 0;
                size_t tlr_shift = 0;
                size_t partweight_array_shift = 0;
 
                for(int kk = 0; kk < current_concurrent_num_parts; ++kk){
                    mj_part_t current_concurrent_work_part = current_work_part + kk;
                    mj_part_t num_parts = num_partitioning_in_current_dim[current_concurrent_work_part];
 
                    //if the part is empty, skip the part.
                    if((num_parts != 1  )
                                &&
                                this->global_min_max_coord_total_weight[kk] >
                             this->global_min_max_coord_total_weight[kk + current_concurrent_num_parts]) {
 
                        //we still need to write the begin and end point of the
                        //empty part. simply set it zero, the array indices will be shifted later.
                        for(mj_part_t jj = 0; jj < num_parts; ++jj){
                                this->new_part_xadj[output_part_index + output_array_shift + jj] = 0;
                        }
                        cut_shift += num_parts - 1;
                        tlr_shift += (4 *(num_parts - 1) + 1);
                        output_array_shift += num_parts;
                        partweight_array_shift += (2 * (num_parts - 1) + 1);
                        continue;
                    }
 
                    mj_lno_t coordinate_end= this->part_xadj[current_concurrent_work_part];
                    mj_lno_t coordinate_begin = current_concurrent_work_part==0 ? 0: this->part_xadj[
                                                                current_concurrent_work_part -1];
                    mj_scalar_t *current_concurrent_cut_coordinate = current_cut_coordinates + cut_shift;
                    mj_scalar_t *used_local_cut_line_weight_to_left = this->process_cut_line_weight_to_put_left +
                                                            cut_shift;
 
                    //mj_scalar_t *used_tlr_array =  this->total_part_weight_left_right_closests + tlr_shift;
 
                    for(int ii = 0; ii < this->num_threads; ++ii){
                        this->thread_part_weight_work[ii] = this->thread_part_weights[ii] +  partweight_array_shift;
                    }
 
                    if(num_parts > 1){
                        if(this->mj_keep_part_boxes){
                                //if part boxes are to be stored update the boundaries.
                            for (mj_part_t j = 0; j < num_parts - 1; ++j){
                                (*output_part_boxes)[output_array_shift + output_part_index +
                                 j].updateMinMax(current_concurrent_cut_coordinate[j], 1
                                  /*update max*/, coordInd);
 
                                (*output_part_boxes)[output_array_shift + output_part_index + j +
                                 1].updateMinMax(current_concurrent_cut_coordinate[j], 0
                                  /*update min*/, coordInd);
                            }
                        }
 
                        // Rewrite the indices based on the computed cuts.
                        this->mj_create_new_partitions(
                            num_parts,
                            mj_current_dim_coords,
                            current_concurrent_cut_coordinate,
                            coordinate_begin,
                            coordinate_end,
                            used_local_cut_line_weight_to_left,
                            this->thread_part_weight_work,
                            this->new_part_xadj + output_part_index + output_array_shift
                            );
 
                    }
                    else {
                        //if this part is partitioned into 1 then just copy
                        //the old values.
                        mj_lno_t part_size = coordinate_end - coordinate_begin;
                        *(this->new_part_xadj + output_part_index + output_array_shift) = part_size;
                        memcpy(
                                this->new_coordinate_permutations + coordinate_begin,
                            this->coordinate_permutations + coordinate_begin,
                            part_size * sizeof(mj_lno_t));
                    }
                    cut_shift += num_parts - 1;
                    tlr_shift += (4 *(num_parts - 1) + 1);
                    output_array_shift += num_parts;
                    partweight_array_shift += (2 * (num_parts - 1) + 1);
                }
 
                //shift cut coordinates so that all cut coordinates are stored.
                //no shift now because we dont keep the cuts.
                //current_cut_coordinates += cut_shift;
 
                //mj_create_new_partitions from coordinates partitioned the parts and
                //write the indices as if there were a single part.
                //now we need to shift the beginning indices.
                for(mj_part_t kk = 0; kk < current_concurrent_num_parts; ++kk){
                    mj_part_t num_parts = num_partitioning_in_current_dim[ current_work_part + kk];
                    for (mj_part_t ii = 0;ii < num_parts ; ++ii){
                        //shift it by previousCount
                        this->new_part_xadj[output_part_index+ii] += output_coordinate_end_index;
                    }
                    //increase the previous count by current end.
                    output_coordinate_end_index = this->new_part_xadj[output_part_index + num_parts - 1];
                    //increase the current out.
                    output_part_index += num_parts ;
                }
            }
        }
        // end of this partitioning dimension
 
 
        int current_world_size = this->comm->getSize();
        long migration_reduce_all_population = this->total_dim_num_reduce_all * current_world_size;
 
 
        bool is_migrated_in_current_dimension = false;
 
        //we migrate if there are more partitionings to be done after this step
        //and if the migration is not forced to be avoided.
        //and the operation is not sequential.
        if (future_num_parts > 1 &&
            this->check_migrate_avoid_migration_option >= 0 &&
            current_world_size > 1){
 
                this->mj_env->timerStart(MACRO_TIMERS, "MultiJagged - Problem_Migration-" + istring);
                mj_part_t num_parts = output_part_count_in_dimension;
                if ( this->mj_perform_migration(
                                                num_parts,
                                                current_num_parts, //output
                                                next_future_num_parts_in_parts, //output
                                                output_part_begin_index,
                                                migration_reduce_all_population,
                                                this->num_global_coords / (future_num_parts * current_num_parts),
                                                istring,
                                                input_part_boxes, output_part_boxes) ) {
                        is_migrated_in_current_dimension = true;
                        is_data_ever_migrated = true;
                        this->mj_env->timerStop(MACRO_TIMERS, "MultiJagged - Problem_Migration-" +
                                        istring);
                        //since data is migrated, we reduce the number of reduceAll operations for the last part.
                        this->total_dim_num_reduce_all /= num_parts;
                }
                else {
                        is_migrated_in_current_dimension = false;
                        this->mj_env->timerStop(MACRO_TIMERS, "MultiJagged - Problem_Migration-" + istring);
                }
        }
 
        //swap the coordinate permutations for the next dimension.
        mj_lno_t * tmp = this->coordinate_permutations;
        this->coordinate_permutations = this->new_coordinate_permutations;
        this->new_coordinate_permutations = tmp;
 
        if(!is_migrated_in_current_dimension){
            this->total_dim_num_reduce_all -= current_num_parts;
            current_num_parts = output_part_count_in_dimension;
        }
        freeArray<mj_lno_t>(this->part_xadj);
        this->part_xadj = this->new_part_xadj;
        this->new_part_xadj = NULL;
        this->mj_env->timerStop(MACRO_TIMERS, "MultiJagged - Problem_Partitioning_" + istring);
    }
 
    // Partitioning is done
    delete future_num_part_in_parts;
    delete next_future_num_parts_in_parts;
 
    this->mj_env->timerStop(MACRO_TIMERS, "MultiJagged - Problem_Partitioning");
 
 
    //get the final parts of each initial coordinate
    //the results will be written to
    //this->assigned_part_ids for gnos given in this->current_mj_gnos
    this->set_final_parts(
                current_num_parts,
                output_part_begin_index,
                output_part_boxes,
                is_data_ever_migrated);
 
    result_assigned_part_ids_ = this->assigned_part_ids;
    result_mj_gnos_ = this->current_mj_gnos;
 
    this->free_work_memory();
    this->mj_env->timerStop(MACRO_TIMERS, "MultiJagged - Total");
    this->mj_env->debug(3, "Out of MultiJagged");
 
}
 
 
template <typename Adapter>
class Zoltan2_AlgMJ : public Algorithm<Adapter>
{
private:
 
#ifndef DOXYGEN_SHOULD_SKIP_THIS
 
    typedef CoordinateModel<typename Adapter::base_adapter_t> coordinateModel_t;
 
    // For coordinates and weights, MJ needs floats or doubles
    // But Adapter can provide other scalars, e.g., ints.
    // So have separate scalar_t for MJ and adapter.
    typedef typename Adapter::scalar_t adapter_scalar_t;
 
    // Provide a default type for mj_scalar_t;
    typedef float default_mj_scalar_t;
 
    // If Adapter provided float or double scalar_t, use it (prevents copies).
    // Otherwise, use the default type of mj_scalar_t;
    typedef typename 
            std::conditional<
                 (std::is_same<adapter_scalar_t, float>::value || 
                  std::is_same<adapter_scalar_t, double>::value),
                 adapter_scalar_t, default_mj_scalar_t>::type   mj_scalar_t;
 
    typedef typename Adapter::gno_t mj_gno_t;
    typedef typename Adapter::lno_t mj_lno_t;
    typedef typename Adapter::node_t mj_node_t;
    typedef typename Adapter::part_t mj_part_t;
    typedef coordinateModelPartBox mj_partBox_t;
    typedef std::vector<mj_partBox_t> mj_partBoxVector_t;
#endif
    AlgMJ<mj_scalar_t, mj_lno_t, mj_gno_t, mj_part_t> mj_partitioner;
 
    RCP<const Environment> mj_env; //the environment object
    RCP<const Comm<int> > mj_problemComm; //initial comm object
    RCP<const coordinateModel_t> mj_coords; //coordinate adapter
 
    //PARAMETERS
    double imbalance_tolerance; //input imbalance tolerance.
    size_t num_global_parts; //the targeted number of parts
    mj_part_t *part_no_array; //input part array specifying num part to divide along each dim.
    int recursion_depth; //the number of steps that partitioning will be solved in.
 
    int coord_dim; // coordinate dimension.
    mj_lno_t num_local_coords; //number of local coords.
    mj_gno_t num_global_coords; //number of global coords.
    const mj_gno_t *initial_mj_gnos; //initial global ids of the coordinates.
    mj_scalar_t **mj_coordinates; //two dimension coordinate array
 
    int num_weights_per_coord; // number of weights per coordinate
    bool *mj_uniform_weights; //if the coordinates have uniform weights.
    mj_scalar_t **mj_weights; //two dimensional weight array
    bool *mj_uniform_parts; //if the target parts are uniform
    mj_scalar_t **mj_part_sizes; //target part weight sizes.
 
    // Nonuniform first level partitioning
    // Currently used for Dragonfly task mapping by partitioning Dragonfly RCA 
    // machine coordinates and application coordinates.
    // An optimization that completely partitions the most important machine dimension 
    // first (i.e. the Dragonfly group coordinate, or RCA's x coordinate). The standard 
    // MJ alg follows after the nonuniform first level partitioning.   
    mj_part_t num_first_level_parts; // If used, number of parts for the first level partitioing
    const mj_part_t *first_level_distribution; // If used, the distribution of parts for the nonuniform first level partitioning
 
    bool distribute_points_on_cut_lines; //if partitioning can distribute points on same coordiante to different parts.
    mj_part_t max_concurrent_part_calculation; // how many parts we can calculate concurrently.
    int check_migrate_avoid_migration_option; //whether to migrate=1, avoid migrate=2, or leave decision to MJ=0
    int migration_type; // when doing the migration, 0 will aim for perfect load-imbalance, 
      //1 for minimized messages
    double minimum_migration_imbalance; //when MJ decides whether to migrate, the minimum imbalance for migration.
    bool mj_keep_part_boxes; //if the boxes need to be kept.
 
    int num_threads;
 
    bool mj_run_as_rcb; //if this is set, then recursion depth is adjusted to its maximum value.
    int mj_premigration_option;
    int min_coord_per_rank_for_premigration;
 
    ArrayRCP<mj_part_t> comXAdj_; //communication graph xadj
    ArrayRCP<mj_part_t> comAdj_; //communication graph adj.
 
 
    //when we have strided data, it returns a unstrided data in RCP form.
    //we need to hold on to that data, during the execution of mj, so that the data is not released.
    //coordinate_rcp_holder will hold that data, and release it when MJ is deleted.
    ArrayRCP<const mj_scalar_t> * coordinate_ArrayRCP_holder;
 
    void set_up_partitioning_data(
      const RCP<PartitioningSolution<Adapter> >&solution);
 
    void set_input_parameters(const Teuchos::ParameterList &p);
 
    void free_work_memory();
 
    RCP<mj_partBoxVector_t> getGlobalBoxBoundaries() const;
    
    bool mj_premigrate_to_subset(int used_num_ranks, int migration_selection_option,
        RCP<const Environment> mj_env_,
        RCP<const Comm<int> > mj_problemComm_,
        int coord_dim_,
        mj_lno_t num_local_coords_,
        mj_gno_t num_global_coords_,  size_t num_global_parts_,
        const mj_gno_t *initial_mj_gnos_,
        mj_scalar_t **mj_coordinates_,
        int num_weights_per_coord_,
        mj_scalar_t **mj_weights_,
        //results
        RCP<const Comm<int> > &result_problemComm_,
        mj_lno_t & result_num_local_coords_,
        mj_gno_t * &result_initial_mj_gnos_,
        mj_scalar_t ** &result_mj_coordinates_,
        mj_scalar_t ** &result_mj_weights_,
        int * &result_actual_owner_rank_);
 
public:
 
    Zoltan2_AlgMJ(const RCP<const Environment> &env,
                  RCP<const Comm<int> > &problemComm,
                  const RCP<const coordinateModel_t> &coords) :
                        mj_partitioner(), mj_env(env),
                        mj_problemComm(problemComm),
                        mj_coords(coords),
                        imbalance_tolerance(0),
                        num_global_parts(1), 
                        part_no_array(NULL),
                        recursion_depth(0),
                        coord_dim(0),
                        num_local_coords(0), 
                        num_global_coords(0),
                        initial_mj_gnos(NULL), 
                        mj_coordinates(NULL),
                        num_weights_per_coord(0),
                        mj_uniform_weights(NULL), 
                        mj_weights(NULL),
                        mj_uniform_parts(NULL),
                        mj_part_sizes(NULL),
                        num_first_level_parts(1),
                        first_level_distribution(NULL),
                        distribute_points_on_cut_lines(true),
                        max_concurrent_part_calculation(1),
                        check_migrate_avoid_migration_option(0), 
                        migration_type(0),
                        minimum_migration_imbalance(0.30),
                        mj_keep_part_boxes(false), 
                        num_threads(1), 
                        mj_run_as_rcb(false),
                        mj_premigration_option(0), 
                        min_coord_per_rank_for_premigration(32000),
                        comXAdj_(), comAdj_(), 
                        coordinate_ArrayRCP_holder(NULL)
    {}
 
    ~Zoltan2_AlgMJ(){
      if (coordinate_ArrayRCP_holder != NULL){
        delete [] this->coordinate_ArrayRCP_holder;
        this->coordinate_ArrayRCP_holder = NULL;
      }
    }
 
    static void getValidParameters(ParameterList & pl)
    {
      const bool bUnsorted = true; // this clarifies the flag is for unsrorted
      RCP<Zoltan2::IntegerRangeListValidator<int>> mj_parts_Validator =
      Teuchos::rcp( new Zoltan2::IntegerRangeListValidator<int>(bUnsorted) );
      pl.set("mj_parts", "0", "list of parts for multiJagged partitioning "
        "algorithm. As many as the dimension count.", mj_parts_Validator);
 
      pl.set("mj_concurrent_part_count", 1, "The number of parts whose cut "
        "coordinates will be calculated concurently.", Environment::getAnyIntValidator());
 
      pl.set("mj_minimum_migration_imbalance", 1.1,
        "mj_minimum_migration_imbalance, the minimum imbalance of the "
        "processors to avoid migration",
        Environment::getAnyDoubleValidator());
 
      RCP<Teuchos::EnhancedNumberValidator<int>> mj_migration_option_validator =
        Teuchos::rcp( new Teuchos::EnhancedNumberValidator<int>(0, 2) );
      pl.set("mj_migration_option", 1, "Migration option, 0 for decision "
        "depending on the imbalance, 1 for forcing migration, 2 for "
        "avoiding migration", mj_migration_option_validator);
 
            
 
 
      RCP<Teuchos::EnhancedNumberValidator<int>> mj_migration_type_validator =
        Teuchos::rcp( new Teuchos::EnhancedNumberValidator<int>(0, 1) );
      pl.set("mj_migration_type", 0, "Migration type, 0 for migration to minimize the imbalance "
        "1 for migration to minimize messages exchanged the migration." ,
  mj_migration_option_validator);
 
      // bool parameter
      pl.set("mj_keep_part_boxes", false, "Keep the part boundaries of the "
        "geometric partitioning.", Environment::getBoolValidator());
 
      // bool parameter
      pl.set("mj_enable_rcb", false, "Use MJ as RCB.",
        Environment::getBoolValidator());
 
      pl.set("mj_recursion_depth", -1, "Recursion depth for MJ: Must be "
        "greater than 0.", Environment::getAnyIntValidator());
 
      RCP<Teuchos::EnhancedNumberValidator<int>> mj_premigration_option_validator =
        Teuchos::rcp( new Teuchos::EnhancedNumberValidator<int>(0, 1024) );
 
      pl.set("mj_premigration_option", 0, "Whether to do premigration or not. 0 for no migration "
        "x > 0 for migration to consecutive processors, the subset will be 0,x,2x,3x,...subset ranks."
        , mj_premigration_option_validator);
 
      pl.set("mj_premigration_coordinate_count", 32000, "How many coordinate to assign each rank in multijagged after premigration"
        , Environment::getAnyIntValidator());
 
    }
 
    void partition(const RCP<PartitioningSolution<Adapter> > &solution);
 
    mj_partBoxVector_t &getPartBoxesView() const
    {
      RCP<mj_partBoxVector_t> pBoxes = this->getGlobalBoxBoundaries();
      return *pBoxes;
    }
 
    mj_part_t pointAssign(int dim, adapter_scalar_t *point) const;
 
    void boxAssign(int dim, adapter_scalar_t *lower, adapter_scalar_t *upper,
                   size_t &nPartsFound, mj_part_t **partsFound) const;
 
 
    void getCommunicationGraph(
                         const PartitioningSolution<Adapter> *solution,
                         ArrayRCP<mj_part_t> &comXAdj,
                         ArrayRCP<mj_part_t> &comAdj);
};
 
 
 
 
template <typename Adapter>
bool Zoltan2_AlgMJ<Adapter>::mj_premigrate_to_subset( int used_num_ranks, 
         int /* migration_selection_option */,
         RCP<const Environment> mj_env_,
                                 RCP<const Comm<int> > mj_problemComm_,
                                 int coord_dim_,
                                 mj_lno_t num_local_coords_,
                                 mj_gno_t /* num_global_coords_ */, size_t /* num_global_parts_ */,
                                 const mj_gno_t *initial_mj_gnos_,
                                 mj_scalar_t **mj_coordinates_,
                                 int num_weights_per_coord_,
                                 mj_scalar_t **mj_weights_,
                                 //results
                                 RCP<const Comm<int> > &result_problemComm_,
                                 mj_lno_t &result_num_local_coords_,
                                 mj_gno_t * &result_initial_mj_gnos_,
                                 mj_scalar_t ** &result_mj_coordinates_,
                                 mj_scalar_t ** &result_mj_weights_,
                                 int * &result_actual_owner_rank_){
  mj_env_->timerStart(MACRO_TIMERS, "MultiJagged - PreMigration DistributorPlanCreating");
 
  
  int myRank = mj_problemComm_->getRank();
  int worldSize = mj_problemComm_->getSize();
  
  mj_part_t groupsize = worldSize / used_num_ranks;
 
  //std::cout << "used_num_ranks:" << used_num_ranks << " groupsize:" << groupsize << std::endl;
  
  std::vector<mj_part_t> group_begins(used_num_ranks + 1, 0);
 
  mj_part_t i_am_sending_to = 0;
  bool am_i_a_receiver = false;
 
  for(int i = 0; i < used_num_ranks; ++i){
    group_begins[i+ 1]  = group_begins[i] + groupsize;
    if (worldSize % used_num_ranks > i) group_begins[i+ 1] += 1;
    if (i == used_num_ranks) group_begins[i+ 1] = worldSize;
    if (myRank >= group_begins[i] && myRank < group_begins[i + 1]) i_am_sending_to = group_begins[i];
    if (myRank == group_begins[i])  am_i_a_receiver= true;
  }
  
  ArrayView<const mj_part_t> idView(&(group_begins[0]), used_num_ranks );
  result_problemComm_ = mj_problemComm_->createSubcommunicator(idView);
 
   
  Tpetra::Distributor distributor(mj_problemComm_);
 
  std::vector<mj_part_t> coordinate_destinations(num_local_coords_, i_am_sending_to);
  ArrayView<const mj_part_t> destinations( &(coordinate_destinations[0]), num_local_coords_);
  mj_lno_t num_incoming_gnos = distributor.createFromSends(destinations);
  result_num_local_coords_ = num_incoming_gnos;
  mj_env_->timerStop(MACRO_TIMERS, "MultiJagged - PreMigration DistributorPlanCreating");
 
  mj_env_->timerStart(MACRO_TIMERS, "MultiJagged - PreMigration DistributorMigration");
   
  //migrate gnos.
  {
    ArrayRCP<mj_gno_t> received_gnos(num_incoming_gnos);
 
    ArrayView<const mj_gno_t> sent_gnos(initial_mj_gnos_, num_local_coords_);
    distributor.doPostsAndWaits<mj_gno_t>(sent_gnos, 1, received_gnos());
 
    result_initial_mj_gnos_ = allocMemory<mj_gno_t>(num_incoming_gnos);
    memcpy(
    result_initial_mj_gnos_,
    received_gnos.getRawPtr(),
    num_incoming_gnos * sizeof(mj_gno_t));
  }
      
  //migrate coordinates
  result_mj_coordinates_ = allocMemory<mj_scalar_t *>(coord_dim_);
  for (int i = 0; i < coord_dim_; ++i){
    ArrayView<const mj_scalar_t> sent_coord(mj_coordinates_[i], num_local_coords_);
    ArrayRCP<mj_scalar_t> received_coord(num_incoming_gnos);
    distributor.doPostsAndWaits<mj_scalar_t>(sent_coord, 1, received_coord());
    result_mj_coordinates_[i] = allocMemory<mj_scalar_t>(num_incoming_gnos);
    memcpy(
    result_mj_coordinates_[i],
    received_coord.getRawPtr(),
    num_incoming_gnos * sizeof(mj_scalar_t));
  }
        
  result_mj_weights_ = allocMemory<mj_scalar_t *>(num_weights_per_coord_);
  //migrate weights.
  for (int i = 0; i < num_weights_per_coord_; ++i){
    ArrayView<const mj_scalar_t> sent_weight(mj_weights_[i], num_local_coords_);
    ArrayRCP<mj_scalar_t> received_weight(num_incoming_gnos);
    distributor.doPostsAndWaits<mj_scalar_t>(sent_weight, 1, received_weight());
    result_mj_weights_[i] = allocMemory<mj_scalar_t>(num_incoming_gnos);
    memcpy(
    result_mj_weights_[i],
    received_weight.getRawPtr(),
    num_incoming_gnos * sizeof(mj_scalar_t));
  }
                
  //migrate the owners of the coordinates
  { 
    std::vector<int> owner_of_coordinate(num_local_coords_, myRank);
    ArrayView<int> sent_owners(&(owner_of_coordinate[0]), num_local_coords_);
    ArrayRCP<int> received_owners(num_incoming_gnos);
    distributor.doPostsAndWaits<int>(sent_owners, 1, received_owners());
    result_actual_owner_rank_ = allocMemory<int>(num_incoming_gnos);
    memcpy(
    result_actual_owner_rank_,
    received_owners.getRawPtr(),
    num_incoming_gnos * sizeof(int));
  }
  mj_env_->timerStop(MACRO_TIMERS, "MultiJagged - PreMigration DistributorMigration");
  return am_i_a_receiver;
}
 
 
 
 
 
 
 
template <typename Adapter>
void Zoltan2_AlgMJ<Adapter>::partition(
  const RCP<PartitioningSolution<Adapter> > &solution
)
{
    this->set_up_partitioning_data(solution);
    this->set_input_parameters(this->mj_env->getParameters());
    if (this->mj_keep_part_boxes){
        this->mj_partitioner.set_to_keep_part_boxes();
    }
    this->mj_partitioner.set_partitioning_parameters(
                this->distribute_points_on_cut_lines,
                this->max_concurrent_part_calculation,
                this->check_migrate_avoid_migration_option,
                this->minimum_migration_imbalance, this->migration_type);
 
 
   RCP<const Comm<int> > result_problemComm = this->mj_problemComm;
   mj_lno_t result_num_local_coords = this->num_local_coords;
   mj_gno_t * result_initial_mj_gnos = NULL;
   mj_scalar_t **result_mj_coordinates = this->mj_coordinates;
   mj_scalar_t **result_mj_weights = this->mj_weights;
   int *result_actual_owner_rank = NULL;
   const mj_gno_t * result_initial_mj_gnos_ = this->initial_mj_gnos;
 
   //TODO: MD 08/2017: Further discussion is required.
   //MueLu calls MJ when it has very few coordinates per processors, such as 10. 
   //For example, it begins with 1K processor with 1K coordinate in each. 
   //Then with coarsening this reduces to 10 coordinate per procesor.
   //It calls MJ to repartition these to 10 coordinates.
   //MJ runs with 1K processor, 10 coordinate in each, and partitions to 10 parts. 
   //As expected strong scaling is problem here, because computation is almost 0, and
   //communication cost of MJ linearly increases. 
   //Premigration option gathers the coordinates to 10 parts before MJ starts
   //therefore MJ will run with a smalller subset of the problem. 
   //Below, I am migrating the coordinates if mj_premigration_option is set,
   //and the result parts are less than the current part count, and the average number of 
   //local coordinates is less than some threshold.
   //For example, premigration may not help if 1000 processors are partitioning data to 10,
   //but each of them already have 1M coordinate. In that case, we premigration would not help.
   int current_world_size = this->mj_problemComm->getSize();
   mj_lno_t threshold_num_local_coords = this->min_coord_per_rank_for_premigration;
   bool is_pre_migrated = false;
   bool am_i_in_subset = true;
   if ( mj_premigration_option > 0 &&
       size_t (current_world_size) > this->num_global_parts &&
       this->num_global_coords < mj_gno_t (current_world_size * threshold_num_local_coords)){
     if (this->mj_keep_part_boxes){
       throw std::logic_error("Multijagged: mj_keep_part_boxes and mj_premigration_option are not supported together yet.");
     }
     is_pre_migrated =true;
     int migration_selection_option = mj_premigration_option;
     if(migration_selection_option * this->num_global_parts > (size_t) (current_world_size)){
       migration_selection_option = current_world_size / this->num_global_parts;
     }
     int used_num_ranks = int (this->num_global_coords / float (threshold_num_local_coords) + 0.5);
     if (used_num_ranks == 0) used_num_ranks = 1;
 
     am_i_in_subset = this->mj_premigrate_to_subset(
     used_num_ranks,
         migration_selection_option,
         this->mj_env,
         this->mj_problemComm,
         this->coord_dim,
         this->num_local_coords,
         this->num_global_coords,
         this->num_global_parts,
         this->initial_mj_gnos,
         this->mj_coordinates,
         this->num_weights_per_coord,
         this->mj_weights,
         //results
         result_problemComm,
         result_num_local_coords,
         result_initial_mj_gnos,
         result_mj_coordinates,
         result_mj_weights,
         result_actual_owner_rank);
     result_initial_mj_gnos_ = result_initial_mj_gnos;
   }
   
 
 
   mj_part_t *result_assigned_part_ids = NULL;
   mj_gno_t *result_mj_gnos = NULL;
 
    if (am_i_in_subset){
      this->mj_partitioner.multi_jagged_part(
          this->mj_env,
          result_problemComm, //this->mj_problemComm,
 
          this->imbalance_tolerance,
          this->num_global_parts,
          this->part_no_array,
          this->recursion_depth,
 
          this->coord_dim,
          result_num_local_coords, //this->num_local_coords,
          this->num_global_coords,
          result_initial_mj_gnos_, //this->initial_mj_gnos,
          result_mj_coordinates, //this->mj_coordinates,
 
          this->num_weights_per_coord,
          this->mj_uniform_weights,
          result_mj_weights, //this->mj_weights,
          this->mj_uniform_parts,
          this->mj_part_sizes,
 
          result_assigned_part_ids,
          result_mj_gnos
      );
 
    }
 
    // Reorder results so that they match the order of the input
 
#if defined(__cplusplus) && __cplusplus >= 201103L
    std::unordered_map<mj_gno_t, mj_lno_t> localGidToLid;
    localGidToLid.reserve(result_num_local_coords);
    for (mj_lno_t i = 0; i < result_num_local_coords; i++)
      localGidToLid[result_initial_mj_gnos_[i]] = i;
    ArrayRCP<mj_part_t> partId = arcp(new mj_part_t[result_num_local_coords],
        0, result_num_local_coords, true);
 
    for (mj_lno_t i = 0; i < result_num_local_coords; i++) {
      mj_lno_t origLID = localGidToLid[result_mj_gnos[i]];
      partId[origLID] = result_assigned_part_ids[i];
    }
 
#else
    Teuchos::Hashtable<mj_gno_t, mj_lno_t>
    localGidToLid(result_num_local_coords);
    for (mj_lno_t i = 0; i < result_num_local_coords; i++)
      localGidToLid.put(result_initial_mj_gnos_[i], i);
 
    ArrayRCP<mj_part_t> partId = arcp(new mj_part_t[result_num_local_coords],
        0, result_num_local_coords, true);
 
    for (mj_lno_t i = 0; i < result_num_local_coords; i++) {
      mj_lno_t origLID = localGidToLid.get(result_mj_gnos[i]);
      partId[origLID] = result_assigned_part_ids[i];
    }
 
#endif // C++11 is enabled
 
    delete [] result_mj_gnos;
    delete [] result_assigned_part_ids;
 
 
    //now the results are reordered. but if premigration occured,
    //then we need to send these ids to actual owners again. 
    if (is_pre_migrated){
      this->mj_env->timerStart(MACRO_TIMERS, "MultiJagged - PostMigration DistributorPlanCreating");
      Tpetra::Distributor distributor(this->mj_problemComm);
 
      ArrayView<const mj_part_t> actual_owner_destinations( result_actual_owner_rank , result_num_local_coords);
      mj_lno_t num_incoming_gnos = distributor.createFromSends(actual_owner_destinations);
      if (num_incoming_gnos != this->num_local_coords){
        throw std::logic_error("Zoltan2 - Multijagged Post Migration - num incoming is not equal to num local coords");
      }
      mj_env->timerStop(MACRO_TIMERS, "MultiJagged - PostMigration DistributorPlanCreating");
      mj_env->timerStart(MACRO_TIMERS, "MultiJagged - PostMigration DistributorMigration");
      ArrayRCP<mj_gno_t> received_gnos(num_incoming_gnos);
      ArrayRCP<mj_part_t> received_partids(num_incoming_gnos);
      {
        ArrayView<const mj_gno_t> sent_gnos(result_initial_mj_gnos_, result_num_local_coords);
        distributor.doPostsAndWaits<mj_gno_t>(sent_gnos, 1, received_gnos());
      }
      {
        ArrayView<mj_part_t> sent_partnos(partId());
        distributor.doPostsAndWaits<mj_part_t>(sent_partnos, 1, received_partids());
      }
      partId = arcp(new mj_part_t[this->num_local_coords],
                      0, this->num_local_coords, true);
 
      {
#if defined(__cplusplus) && __cplusplus >= 201103L
      std::unordered_map<mj_gno_t, mj_lno_t> localGidToLid2;
      localGidToLid2.reserve(this->num_local_coords);
      for (mj_lno_t i = 0; i < this->num_local_coords; i++)
        localGidToLid2[this->initial_mj_gnos[i]] = i;
 
 
      for (mj_lno_t i = 0; i < this->num_local_coords; i++) {
        mj_lno_t origLID = localGidToLid2[received_gnos[i]];
        partId[origLID] = received_partids[i];
      }
 
#else
      Teuchos::Hashtable<mj_gno_t, mj_lno_t>
        localGidToLid2(this->num_local_coords);
      for (mj_lno_t i = 0; i < this->num_local_coords; i++)
        localGidToLid2.put(this->initial_mj_gnos[i], i);
 
 
      for (mj_lno_t i = 0; i < this->num_local_coords; i++) {
        mj_lno_t origLID = localGidToLid2.get(received_gnos[i]);
        partId[origLID] = received_partids[i];
      }
 
#endif // C++11 is enabled
 
      }
 
      {
        freeArray<mj_gno_t> (result_initial_mj_gnos);
        for (int i = 0; i < this->coord_dim; ++i){
          freeArray<mj_scalar_t> (result_mj_coordinates[i]);
        }
        freeArray<mj_scalar_t *> (result_mj_coordinates);
 
        for (int i = 0; i < this->num_weights_per_coord; ++i){
          freeArray<mj_scalar_t> (result_mj_weights[i]);
        }
        freeArray<mj_scalar_t *> (result_mj_weights);
        freeArray<int> (result_actual_owner_rank);
      }
      mj_env->timerStop(MACRO_TIMERS, "MultiJagged - PostMigration DistributorMigration");
 
    }
 
    solution->setParts(partId);
    this->free_work_memory();
}
 
/* \brief Freeing the memory allocated.
 * */
template <typename Adapter>
void Zoltan2_AlgMJ<Adapter>::free_work_memory(){
        freeArray<mj_scalar_t *>(this->mj_coordinates);
        freeArray<mj_scalar_t *>(this->mj_weights);
        freeArray<bool>(this->mj_uniform_parts);
        freeArray<mj_scalar_t *>(this->mj_part_sizes);
        freeArray<bool>(this->mj_uniform_weights);
 
}
 
/* \brief Sets the partitioning data for multijagged algorithm.
 * */
template <typename Adapter>
void Zoltan2_AlgMJ<Adapter>::set_up_partitioning_data(
  const RCP<PartitioningSolution<Adapter> > &solution
)
{
        this->coord_dim = this->mj_coords->getCoordinateDim();
        this->num_weights_per_coord = this->mj_coords->getNumWeightsPerCoordinate();
        this->num_local_coords = this->mj_coords->getLocalNumCoordinates();
        this->num_global_coords = this->mj_coords->getGlobalNumCoordinates();
        int criteria_dim = (this->num_weights_per_coord ? this->num_weights_per_coord : 1);
 
        // From the Solution we get part information.
        // If the part sizes for a given criteria are not uniform,
        // then they are values that sum to 1.0.
        this->num_global_parts = solution->getTargetGlobalNumberOfParts();
        //allocate only two dimensional pointer.
        //raw pointer addresess will be obtained from multivector.
        this->mj_coordinates = allocMemory<mj_scalar_t *>(this->coord_dim);
        this->mj_weights = allocMemory<mj_scalar_t *>(criteria_dim);
 
        //if the partitioning results are to be uniform.
        this->mj_uniform_parts = allocMemory< bool >(criteria_dim);
        //if in a criteria dimension, uniform part is false this shows ratios of
        //the target part weights.
        this->mj_part_sizes =  allocMemory<mj_scalar_t *>(criteria_dim);
        //if the weights of coordinates are uniform in a criteria dimension.
        this->mj_uniform_weights = allocMemory< bool >(criteria_dim);
 
        typedef StridedData<mj_lno_t, adapter_scalar_t> input_t;
        ArrayView<const mj_gno_t> gnos;
        ArrayView<input_t> xyz;
        ArrayView<input_t> wgts;
 
 
        this->coordinate_ArrayRCP_holder = new ArrayRCP<const mj_scalar_t> [this->coord_dim + this->num_weights_per_coord];
 
        this->mj_coords->getCoordinates(gnos, xyz, wgts);
        //obtain global ids.
        ArrayView<const mj_gno_t> mj_gnos = gnos;
        this->initial_mj_gnos = mj_gnos.getRawPtr();
 
        //extract coordinates from multivector.
        for (int dim=0; dim < this->coord_dim; dim++){
                ArrayRCP<const mj_scalar_t> ar;
                xyz[dim].getInputArray(ar); // will copy if stride != 1 or 
                                            // adapter_scalar_t != mj_scalar_t
                this->coordinate_ArrayRCP_holder[dim] = ar;
 
                //multiJagged coordinate values assignment
                this->mj_coordinates[dim] =  (mj_scalar_t *)ar.getRawPtr();
        }
 
        //if no weights are provided set uniform weight.
        if (this->num_weights_per_coord == 0){
                this->mj_uniform_weights[0] = true;
                this->mj_weights[0] = NULL;
        }
        else{
                //if weights are provided get weights for all weight indices
                for (int wdim = 0; wdim < this->num_weights_per_coord; wdim++){
                        ArrayRCP<const mj_scalar_t> ar;
                        wgts[wdim].getInputArray(ar); // will copy if stride!=1
                                                      // or adapter_scalar_t !=
                                                      // mj_scalar_t
                        this->coordinate_ArrayRCP_holder[this->coord_dim + wdim] = ar;
                        this->mj_uniform_weights[wdim] = false;
                        this->mj_weights[wdim] = (mj_scalar_t *) ar.getRawPtr();
                }
        }
 
        for (int wdim = 0; wdim < criteria_dim; wdim++){
                if (solution->criteriaHasUniformPartSizes(wdim)){
                        this->mj_uniform_parts[wdim] = true;
                        this->mj_part_sizes[wdim] = NULL;
                }
                else{
                        std::cerr << "MJ does not support non uniform target part weights" << std::endl;
                        exit(1);
                }
        }
}
 
/* \brief Sets the partitioning parameters for multijagged algorithm.
 * \param pl: is the parameter list provided to zoltan2 call
 * */
template <typename Adapter>
void Zoltan2_AlgMJ<Adapter>::set_input_parameters(const Teuchos::ParameterList &pl){
 
        const Teuchos::ParameterEntry *pe = pl.getEntryPtr("imbalance_tolerance");
        if (pe){
                double tol;
                tol = pe->getValue(&tol);
                this->imbalance_tolerance = tol - 1.0;
        }
 
    // TODO: May be a more relaxed tolerance is needed. RCB uses 10%
        if (this->imbalance_tolerance <= 0)
                this->imbalance_tolerance= 10e-4;
 
        //if an input partitioning array is provided.
        this->part_no_array = NULL;
        //the length of the input partitioning array.
        this->recursion_depth = 0;
 
        if (pl.getPtr<Array <mj_part_t> >("mj_parts")){
                this->part_no_array = (mj_part_t *) pl.getPtr<Array <mj_part_t> >("mj_parts")->getRawPtr();
                this->recursion_depth = pl.getPtr<Array <mj_part_t> >("mj_parts")->size() - 1;
                this->mj_env->debug(2, "mj_parts provided by user");
        }
 
        //get mj specific parameters.
        this->distribute_points_on_cut_lines = true;
        this->max_concurrent_part_calculation = 1;
 
        this->mj_run_as_rcb = false;
        this->mj_premigration_option = 0;
  this->min_coord_per_rank_for_premigration = 32000;
 
        int mj_user_recursion_depth = -1;
        this->mj_keep_part_boxes = false;
        this->check_migrate_avoid_migration_option = 0;
        this->migration_type = 0;
  this->minimum_migration_imbalance = 0.35;
 
        pe = pl.getEntryPtr("mj_minimum_migration_imbalance");
        if (pe){
                double imb;
                imb = pe->getValue(&imb);
                this->minimum_migration_imbalance = imb - 1.0;
        }
 
        pe = pl.getEntryPtr("mj_migration_option");
        if (pe){
                this->check_migrate_avoid_migration_option = pe->getValue(&this->check_migrate_avoid_migration_option);
        }else {
                this->check_migrate_avoid_migration_option = 0;
        }
        if (this->check_migrate_avoid_migration_option > 1) this->check_migrate_avoid_migration_option = -1;
 
        pe = pl.getEntryPtr("mj_migration_type");
        if (pe){
                this->migration_type = pe->getValue(&this->migration_type);
        }else {
                this->migration_type = 0;
        }
  //std::cout << " this->migration_type:" <<  this->migration_type << std::endl;
 
        pe = pl.getEntryPtr("mj_concurrent_part_count");
        if (pe){
                this->max_concurrent_part_calculation = pe->getValue(&this->max_concurrent_part_calculation);
        }else {
                this->max_concurrent_part_calculation = 1; // Set to 1 if not provided.
        }
 
        pe = pl.getEntryPtr("mj_keep_part_boxes");
        if (pe){
                this->mj_keep_part_boxes = pe->getValue(&this->mj_keep_part_boxes);
        }else {
                this->mj_keep_part_boxes = false; // Set to invalid value
        }
 
 
        // For now, need keep_part_boxes to do pointAssign and boxAssign.
        // pe = pl.getEntryPtr("keep_cuts");
        // if (pe){
        //      int tmp = pe->getValue(&tmp);
        //      if (tmp) this->mj_keep_part_boxes = true;
        // }
 
        //need to keep part boxes if mapping type is geometric.
        if (this->mj_keep_part_boxes == false){
                pe = pl.getEntryPtr("mapping_type");
                if (pe){
                        int mapping_type = -1;
                        mapping_type = pe->getValue(&mapping_type);
                        if (mapping_type == 0){
                                mj_keep_part_boxes  = true;
                        }
                }
        }
 
        //need to keep part boxes if mapping type is geometric.
        pe = pl.getEntryPtr("mj_enable_rcb");
        if (pe){
                this->mj_run_as_rcb = pe->getValue(&this->mj_run_as_rcb);
        }else {
                this->mj_run_as_rcb = false; // Set to invalid value
        }
 
        pe = pl.getEntryPtr("mj_premigration_option");
        if (pe){
    mj_premigration_option = pe->getValue(&mj_premigration_option);
        }else {
    mj_premigration_option = 0;
        }
 
        pe = pl.getEntryPtr("mj_premigration_coordinate_count");
        if (pe){
          min_coord_per_rank_for_premigration = pe->getValue(&min_coord_per_rank_for_premigration);
        }else {
                min_coord_per_rank_for_premigration = 32000;
        }
 
        pe = pl.getEntryPtr("mj_recursion_depth");
        if (pe){
                mj_user_recursion_depth = pe->getValue(&mj_user_recursion_depth);
        }else {
                mj_user_recursion_depth = -1; // Set to invalid value
        }
 
        bool val = false;
        pe = pl.getEntryPtr("rectilinear");
        if (pe) val = pe->getValue(&val);
        if (val){
                this->distribute_points_on_cut_lines = false;
        } else {
                this->distribute_points_on_cut_lines = true;
        }
 
        if (this->mj_run_as_rcb){
                mj_user_recursion_depth = (int)(ceil(log ((this->num_global_parts)) / log (2.0)));
        }
        if (this->recursion_depth < 1){
                if (mj_user_recursion_depth > 0){
                        this->recursion_depth = mj_user_recursion_depth;
                }
                else {
                        this->recursion_depth = this->coord_dim;
                }
        }
 
        this->num_threads = 1;
#ifdef HAVE_ZOLTAN2_OMP
#pragma omp parallel
        {
                this->num_threads = omp_get_num_threads();
        }
#endif
 
}
 
template <typename Adapter>
void Zoltan2_AlgMJ<Adapter>::boxAssign(
  int dim,
  adapter_scalar_t *lower,
  adapter_scalar_t *upper,
  size_t &nPartsFound,
  typename Adapter::part_t **partsFound) const
{
  // TODO:  Implement with cuts rather than boxes to reduce algorithmic
  // TODO:  complexity.  Or at least do a search through the boxes, using
  // TODO:  p x q x r x ... if possible.
 
  nPartsFound = 0;
  *partsFound = NULL;
 
  if (this->mj_keep_part_boxes) {
 
    // Get vector of part boxes
    RCP<mj_partBoxVector_t> partBoxes = this->getGlobalBoxBoundaries();
 
    size_t nBoxes = (*partBoxes).size();
    if (nBoxes == 0) {
      throw std::logic_error("no part boxes exist");
    }
 
    // Determine whether the box overlaps the globalBox at all
    RCP<mj_partBox_t> globalBox = this->mj_partitioner.get_global_box();
 
    if (globalBox->boxesOverlap(dim, lower, upper)) {
 
      std::vector<typename Adapter::part_t> partlist;
 
      // box overlaps the global box; find specific overlapping boxes
      for (size_t i = 0; i < nBoxes; i++) {
        try {
          if ((*partBoxes)[i].boxesOverlap(dim, lower, upper)) {
            nPartsFound++;
            partlist.push_back((*partBoxes)[i].getpId());
 
//            std::cout << "Given box (";
//            for (int j = 0; j < dim; j++)
//              std::cout << lower[j] << " ";
//            std::cout << ") x (";
//            for (int j = 0; j < dim; j++)
//              std::cout << upper[j] << " ";
//            std::cout << ") overlaps PartBox "
//                      << (*partBoxes)[i].getpId() << " (";
//            for (int j = 0; j < dim; j++)
//              std::cout << (*partBoxes)[i].getlmins()[j] << " ";
//            std::cout << ") x (";
//            for (int j = 0; j < dim; j++)
//              std::cout << (*partBoxes)[i].getlmaxs()[j] << " ";
//            std::cout << ")" << std::endl;
          }
        }
        Z2_FORWARD_EXCEPTIONS;
      }
      if (nPartsFound) {
        *partsFound = new mj_part_t[nPartsFound];
        for (size_t i = 0; i < nPartsFound; i++)
          (*partsFound)[i] = partlist[i];
      }
    }
    else {
      // Box does not overlap the domain at all.  Find the closest part
      // Not sure how to perform this operation for MJ without having the
      // cuts.  With the RCB cuts, the concept of a part extending to
      // infinity was natural.  With the boxes, it is much more difficult.
      // TODO:  For now, return information indicating NO OVERLAP.
 
    }
  }
  else {
    throw std::logic_error("need to use keep_cuts parameter for boxAssign");
  }
}
 
template <typename Adapter>
typename Adapter::part_t Zoltan2_AlgMJ<Adapter>::pointAssign(
  int dim,
  adapter_scalar_t *point) const
{
 
  // TODO:  Implement with cuts rather than boxes to reduce algorithmic
  // TODO:  complexity.  Or at least do a search through the boxes, using
  // TODO:  p x q x r x ... if possible.
 
  if (this->mj_keep_part_boxes) {
    typename Adapter::part_t foundPart = -1;
 
    // Get vector of part boxes
    RCP<mj_partBoxVector_t> partBoxes = this->getGlobalBoxBoundaries();
 
    size_t nBoxes = (*partBoxes).size();
    if (nBoxes == 0) {
      throw std::logic_error("no part boxes exist");
    }
 
    // Determine whether the point is within the global domain
    RCP<mj_partBox_t> globalBox = this->mj_partitioner.get_global_box();
 
    if (globalBox->pointInBox(dim, point)) {
 
      // point is in the global domain; determine in which part it is.
      size_t i;
      for (i = 0; i < nBoxes; i++) {
        try {
          if ((*partBoxes)[i].pointInBox(dim, point)) {
            foundPart = (*partBoxes)[i].getpId();
//            std::cout << "Point (";
//            for (int j = 0; j < dim; j++) std::cout << point[j] << " ";
//            std::cout << ") found in box " << i << " part " << foundPart
//                      << std::endl;
//            (*partBoxes)[i].print();
            break;
          }
        }
        Z2_FORWARD_EXCEPTIONS;
      }
 
      if (i == nBoxes) {
        // This error should never occur
        std::ostringstream oss;
        oss << "Point (";
        for (int j = 0; j < dim; j++) oss << point[j] << " ";
        oss << ") not found in domain";
        throw std::logic_error(oss.str());
      }
    }
 
    else {
      // Point is outside the global domain.
      // Determine to which part it is closest.
      // TODO:  with cuts, would not need this special case
 
      typedef typename Zoltan2::coordinateModelPartBox::coord_t coord_t;
      size_t closestBox = 0;
      coord_t minDistance = std::numeric_limits<coord_t>::max();
      coord_t *centroid = new coord_t[dim];
      for (size_t i = 0; i < nBoxes; i++) {
        (*partBoxes)[i].computeCentroid(centroid);
        coord_t sum = 0.;
        coord_t diff;
        for (int j = 0; j < dim; j++) {
          diff = centroid[j] - point[j];
          sum += diff * diff;
        }
        if (sum < minDistance) {
          minDistance = sum;
          closestBox = i;
        }
      }
      foundPart = (*partBoxes)[closestBox].getpId();
      delete [] centroid;
    }
 
    return foundPart;
  }
  else {
    throw std::logic_error("need to use keep_cuts parameter for pointAssign");
  }
}
 
template <typename Adapter>
void Zoltan2_AlgMJ<Adapter>::getCommunicationGraph(
  const PartitioningSolution<Adapter> * /* solution */,
  ArrayRCP<typename Zoltan2_AlgMJ<Adapter>::mj_part_t> &comXAdj,
  ArrayRCP<typename Zoltan2_AlgMJ<Adapter>::mj_part_t> &comAdj)
{
  if(comXAdj_.getRawPtr() == NULL && comAdj_.getRawPtr() == NULL){
    RCP<mj_partBoxVector_t> pBoxes = this->getGlobalBoxBoundaries();
    mj_part_t ntasks =  (*pBoxes).size();
    int dim = (*pBoxes)[0].getDim();
    GridHash grid(pBoxes, ntasks, dim);
    grid.getAdjArrays(comXAdj_, comAdj_);
  }
  comAdj = comAdj_;
  comXAdj = comXAdj_;
}
 
 
template <typename Adapter>
RCP<typename Zoltan2_AlgMJ<Adapter>::mj_partBoxVector_t>
Zoltan2_AlgMJ<Adapter>::getGlobalBoxBoundaries() const
{
  return this->mj_partitioner.get_kept_boxes();
}
 
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
RCP<typename AlgMJ<mj_scalar_t,mj_lno_t,mj_gno_t,mj_part_t>::mj_partBoxVector_t>
AlgMJ<mj_scalar_t,mj_lno_t,mj_gno_t,mj_part_t>::get_kept_boxes() const
{
  if (this->mj_keep_part_boxes)
    return this->kept_boxes;
  else
    throw std::logic_error("Error: part boxes are not stored.");
}
 
template <typename mj_scalar_t, typename mj_lno_t, typename mj_gno_t,
          typename mj_part_t>
RCP<typename AlgMJ<mj_scalar_t,mj_lno_t,mj_gno_t,mj_part_t>::mj_partBoxVector_t>
AlgMJ<mj_scalar_t,mj_lno_t,mj_gno_t,mj_part_t>::compute_global_box_boundaries(
  RCP<mj_partBoxVector_t> &localPartBoxes
) const
{
  typedef typename Zoltan2::coordinateModelPartBox::coord_t coord_t;
  mj_part_t ntasks = this->num_global_parts;
  int dim = (*localPartBoxes)[0].getDim();
  coord_t *localPartBoundaries = new coord_t[ntasks * 2 *dim];
 
  memset(localPartBoundaries, 0, sizeof(coord_t) * ntasks * 2 *dim);
 
  coord_t *globalPartBoundaries = new coord_t[ntasks * 2 *dim];
  memset(globalPartBoundaries, 0, sizeof(coord_t) * ntasks * 2 *dim);
 
  coord_t *localPartMins = localPartBoundaries;
  coord_t *localPartMaxs = localPartBoundaries + ntasks * dim;
 
  coord_t *globalPartMins = globalPartBoundaries;
  coord_t *globalPartMaxs = globalPartBoundaries + ntasks * dim;
 
  mj_part_t boxCount = localPartBoxes->size();
  for (mj_part_t i = 0; i < boxCount; ++i){
    mj_part_t pId = (*localPartBoxes)[i].getpId();
      //std::cout << "me:" << comm->getRank() << " has:" << pId << std::endl;
 
    coord_t *lmins = (*localPartBoxes)[i].getlmins();
    coord_t *lmaxs = (*localPartBoxes)[i].getlmaxs();
 
    for (int j = 0; j < dim; ++j){
      localPartMins[dim * pId + j] = lmins[j];
      localPartMaxs[dim * pId + j] = lmaxs[j];
      /*
      std::cout << "me:" << comm->getRank()  <<
              " dim * pId + j:"<< dim * pId + j <<
              " localMin:" << localPartMins[dim * pId + j] <<
              " localMax:" << localPartMaxs[dim * pId + j] << std::endl;
      */
    }
  }
 
  Teuchos::Zoltan2_BoxBoundaries<int, coord_t> reductionOp(ntasks * 2 *dim);
 
  reduceAll<int, coord_t>(*mj_problemComm, reductionOp,
            ntasks * 2 *dim, localPartBoundaries, globalPartBoundaries);
  RCP<mj_partBoxVector_t> pB(new mj_partBoxVector_t(),true);
  for (mj_part_t i = 0; i < ntasks; ++i){
    Zoltan2::coordinateModelPartBox tpb(i, dim, globalPartMins + dim * i,
                                                globalPartMaxs + dim * i);
 
    /*
    for (int j = 0; j < dim; ++j){
       std::cout << "me:" << comm->getRank()  <<
                " dim * pId + j:"<< dim * i + j <<
                " globalMin:" << globalPartMins[dim * i + j] <<
                " globalMax:" << globalPartMaxs[dim * i + j] << std::endl;
    }
    */
    pB->push_back(tpb);
  }
  delete []localPartBoundaries;
  delete []globalPartBoundaries;
  //RCP <mj_partBoxVector_t> tmpRCPBox(pB, true);
  return pB;
}
} // namespace Zoltan2
 
#endif