DenseParMat.cpp

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/****************************************************************/
/* Parallel Combinatorial BLAS Library (for Graph Computations) */
/* version 1.6 -------------------------------------------------*/
/* date: 6/15/2017 ---------------------------------------------*/
/* authors: Ariful Azad, Aydin Buluc  --------------------------*/
/****************************************************************/
/*
 Copyright (c) 2010-2017, The Regents of the University of California
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
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 The above copyright notice and this permission notice shall be included in
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 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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 */

#include <numeric>
#include "DenseParMat.h"
#include "MPIType.h"
#include "Operations.h"

namespace combblas {

template <class IT, class NT>
template <typename _BinaryOperation>
FullyDistVec< IT,NT > DenseParMat<IT,NT>::Reduce(Dim dim, _BinaryOperation __binary_op, NT identity) const
{

    switch(dim)
    {
        case Column:    // pack along the columns, result is a vector of size (global) n
        {
            // we can use parvec's grid as long as the process grid is square (currently a CombBLAS requirement)
            
            int colneighs = commGrid->GetGridRows();    // including oneself
                    int colrank = commGrid->GetRankInProcCol();

            IT * loclens = new IT[colneighs];
            IT * lensums = new IT[colneighs+1]();   // begin/end points of local lengths

                    IT n_perproc = n / colneighs;    // length on a typical processor
                    if(colrank == colneighs-1)
                        loclens[colrank] = n - (n_perproc*colrank);
                    else
                        loclens[colrank] = n_perproc;

            MPI_Allgather(MPI_IN_PLACE, 0, MPIType<IT>(), loclens, 1, MPIType<IT>(), commGrid->GetColWorld());
            std::partial_sum(loclens, loclens+colneighs, lensums+1);    // loclens and lensums are different, but both would fit in 32-bits

            std::vector<NT> trarr(loclens[colrank]);
            NT * sendbuf = new NT[n];
            for(int j=0; j < n; ++j)
            {
                sendbuf[j] = identity;
                for(int i=0; i < m; ++i)
                {
                    sendbuf[j] = __binary_op(array[i][j], sendbuf[j]); 
                }
            }
                    
            // The MPI_REDUCE_SCATTER routine is functionally equivalent to:
                    // an MPI_REDUCE collective operation with count equal to the sum of loclens[i]
                // followed by MPI_SCATTERV with sendcounts equal to loclens as well
                    MPI_Reduce_scatter(sendbuf, trarr.data(), loclens, MPIType<NT>(), MPIOp<_BinaryOperation, NT>::op(), commGrid->GetColWorld());
            
            DeleteAll(sendbuf, loclens, lensums);

            IT reallen; // Now we have to transpose the vector
            IT trlen = trarr.size();
            int diagneigh = commGrid->GetComplementRank();
            MPI_Status status;
            MPI_Sendrecv(&trlen, 1, MPIType<IT>(), diagneigh, TRNNZ, &reallen, 1, MPIType<IT>(), diagneigh, TRNNZ, commGrid->GetWorld(), &status);
            IT glncols = gcols();
                    FullyDistVec<IT,NT> parvec(commGrid, glncols, identity);            

            assert((parvec.arr.size() ==  reallen));
            MPI_Sendrecv(trarr.data(), trlen, MPIType<NT>(), diagneigh, TRX, parvec.arr.data(), reallen, MPIType<NT>(), diagneigh, TRX, commGrid->GetWorld(), &status);

            
                    return parvec;
            break;
        }
        case Row:   // pack along the rows, result is a vector of size m
        {
            IT glnrows = grows();
                    FullyDistVec<IT,NT> parvec(commGrid, glnrows, identity);

            NT * sendbuf = new NT[m];
            for(int i=0; i < m; ++i)
            {
                sendbuf[i] = std::accumulate( array[i], array[i]+n, identity, __binary_op);
            }
            NT * recvbuf = parvec.arr.data();
            
            
                    int rowneighs = commGrid->GetGridCols();
                int rowrank = commGrid->GetRankInProcRow();
                    IT * recvcounts = new IT[rowneighs];
                    recvcounts[rowrank] = parvec.MyLocLength();  // local vector lengths are the ultimate receive counts
                    MPI_Allgather(MPI_IN_PLACE, 0, MPIType<IT>(), recvcounts, 1, MPIType<IT>(), commGrid->GetRowWorld());
            
                    // The MPI_REDUCE_SCATTER routine is functionally equivalent to:
                    // an MPI_REDUCE collective operation with count equal to the sum of recvcounts[i]
                // followed by MPI_SCATTERV with sendcounts equal to recvcounts.
                    MPI_Reduce_scatter(sendbuf, recvbuf, recvcounts, MPIType<NT>(), MPIOp<_BinaryOperation, NT>::op(), commGrid->GetRowWorld());
            delete [] sendbuf;
                    delete [] recvcounts;
                return parvec;
            break;
        }
        default:
        {
            std::cout << "Unknown reduction dimension, returning empty vector" << std::endl;
                    return FullyDistVec<IT,NT>(commGrid);
            break;
        }
    }
}

template <class IT, class NT>
template <typename DER>
DenseParMat< IT,NT > & DenseParMat<IT,NT>::operator+=(const SpParMat< IT,NT,DER > & rhs)        // add a sparse matrix
{
    if(*commGrid == *rhs.commGrid)  
    {
        (rhs.spSeq)->UpdateDense(array, std::plus<double>());
    }
    else
    {
        std::cout << "Grids are not comparable elementwise addition" << std::endl; 
        MPI_Abort(MPI_COMM_WORLD,GRIDMISMATCH);
    }
    return *this;   
}


template <class IT, class NT>
DenseParMat< IT,NT > &  DenseParMat<IT,NT>::operator=(const DenseParMat< IT,NT > & rhs)     // assignment operator
{
    if(this != &rhs)        
    {
        if(array != NULL) 
            SpHelper::deallocate2D(array, m);

        m = rhs.m;
        n = rhs.n;
        if(rhs.array != NULL)   
        {
            array = SpHelper::allocate2D<NT>(m, n);
            for(int i=0; i< m; ++i)
                std::copy(array[i], array[i]+n, rhs.array[i]);
        }
        commGrid.reset(new CommGrid(*(rhs.commGrid)));      
    }
    return *this;
}

}