C++ DistMatrix::SetLocal方法代码示例

inline void inner_panel_mixed_gemm_impl_nn(
        const double alpha,
        const SpParMat<index_type, value_type, SpDCCols<index_type, value_type> > &A,
        const El::DistMatrix<value_type, El::STAR, El::STAR> &S,
        const double beta,
        El::DistMatrix<value_type, col_d, El::STAR> &C) {

    int n_proc_side   = A.getcommgrid()->GetGridRows();
    int output_width  = S.Width();
    int output_height = A.getnrow();

    size_t rank = A.getcommgrid()->GetRank();
    size_t cb_row_offset = utility::cb_my_row_offset(A);

    typedef SpDCCols< index_type, value_type > col_t;
    typedef SpParMat< index_type, value_type, col_t > matrix_type;
    matrix_type &_A = const_cast<matrix_type&>(A);
    col_t &data = _A.seq();

    // 1) compute the local values still using the CombBLAS distribution (2D
    //    processor grid). We assume the result is dense.
    std::vector<double> local_matrix;
    mixed_gemm_local_part_nn(alpha, A, S, 0.0, local_matrix);

    // 2) reduce first along rows so that each processor owns the values in
    //    the output row of the SOMETHING/* matrix and values for processors in
    //    the same processor column.
    boost::mpi::communicator my_row_comm(
            A.getcommgrid()->GetRowWorld(), boost::mpi::comm_duplicate);

    // storage for other procs in same row communicator: rank -> (row, values)
    typedef std::vector<std::pair<int, std::vector<double> > > for_rank_t;
    std::vector<for_rank_t> for_rank(n_proc_side);

    for(size_t local_row = 0; local_row < data.getnrow(); ++local_row) {

        size_t row = local_row + cb_row_offset;

        // the owner for VR/* and VC/* matrices is independent of the column
        size_t target_proc = utility::owner(C, row, static_cast<size_t>(0));

        // if the target processor is not in the current row communicator, get
        // the value in the processor grid sharing the same row.
        if(!A.getcommgrid()->OnSameProcRow(target_proc))
            target_proc = static_cast<int>(rank / n_proc_side) *
                            n_proc_side + target_proc % n_proc_side;

        size_t target_row_rank = A.getcommgrid()->GetRankInProcRow(target_proc);

        // reduce partial row (FIXME: if the resulting matrix is still
        // expected to be sparse, change this to communicate only nnz).
        // Working on local_width columns concurrently per column processing
        // group.
        size_t local_width = S.Width();
        const value_type* buffer = &local_matrix[local_row * local_width];
        std::vector<value_type> new_values(local_width);
        boost::mpi::reduce(my_row_comm, buffer, local_width,
                &new_values[0], std::plus<value_type>(), target_row_rank);

        // processor stores result directly if it is the owning rank of that
        // row, save for subsequent communication along rows otherwise
        if(rank == utility::owner(C, row, static_cast<size_t>(0))) {
            int elem_lrow = C.LocalRow(row);
            for(size_t idx = 0; idx < local_width; ++idx) {
                int elem_lcol = C.LocalCol(idx);
                C.SetLocal(elem_lrow, elem_lcol,
                    new_values[idx] + beta * C.GetLocal(elem_lrow, elem_lcol));
            }
        } else if (rank == target_proc) {
            // store for later comm across rows
            for_rank[utility::owner(C, row, static_cast<size_t>(0)) / n_proc_side].push_back(
                    std::make_pair(row, new_values));
        }
    }

    // 3) gather remaining values along rows: we exchange all the values with
    //    other processors in the same communicator row and then add them to
    //    our local part.
    boost::mpi::communicator my_col_comm(
            A.getcommgrid()->GetColWorld(), boost::mpi::comm_duplicate);

    std::vector<for_rank_t> new_values;
    for(int i = 0; i < n_proc_side; ++i)
        boost::mpi::gather(my_col_comm, for_rank[i], new_values, i);

    // insert new values
    for(size_t proc = 0; proc < new_values.size(); ++proc) {
        const for_rank_t &cur  = new_values[proc];

        for(size_t i = 0; i < cur.size(); ++i) {
            int elem_lrow = C.LocalRow(cur[i].first);
            for(size_t j = 0; j < cur[i].second.size(); ++j) {
                size_t elem_lcol = C.LocalCol(j);
                C.SetLocal(elem_lrow, elem_lcol,
                        cur[i].second[j] + beta *
                        C.GetLocal(elem_lrow, elem_lcol));
            }
        }
    }
}