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)); } } } }
IT Height(const SpParMat<IT, VT, SpDCCols<IT, VT> >& A) { return A.getnrow(); }