row_to_row.hpp

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#ifndef TATAMI_MULT_DENSE_MATRIX_SPARSE_ROW_ROW_TO_ROW_HPP
#define TATAMI_MULT_DENSE_MATRIX_SPARSE_ROW_ROW_TO_ROW_HPP
 
#include <cstddef>
#include <vector>
#include <algorithm>
 
#include "tatami/tatami.hpp"
#include "sanisizer/sanisizer.hpp"
 
#include "../utils.hpp"
#include "../../utils.hpp"
 
namespace tatami_mult {
 
/* See https://github.com/tatami-inc/test-multiplication/tree/master/sparse_row/dense_matrix
 * for an explanation of the choice of algorithm.
 */
 
struct MultiplySparseRowWithDenseRowMatrixToRowOutputOptions {
    int num_threads = 1;
};
 
template<typename LeftValue_, typename LeftIndex_, typename RightColumns_, class GetRightRow_, typename Output_>
void multiply_sparse_row_with_dense_row_matrix_to_row_output(
    const tatami::Matrix<LeftValue_, LeftIndex_>& left,
    const RightColumns_ right_columns,
    GetRightRow_ get_right_row,
    Output_* const output,
    const MultiplySparseRowWithDenseRowMatrixToRowOutputOptions& options
) {
    const auto left_NR = left.nrow();
    const auto common_dim = left.ncol();
 
    const bool do_parallel = options.num_threads > 1;
    if (!do_parallel) {
        std::fill_n(output, sanisizer::product_unsafe<std::size_t>(left_NR, right_columns), 0);
    }
 
    tatami::parallelize([&](int, LeftIndex_ start, LeftIndex_ length) -> void {
        auto ext = tatami::consecutive_extractor<true>(left, true, start, length);
        auto vbuffer = tatami::create_container_of_Index_size<std::vector<LeftValue_> >(common_dim);
        auto ibuffer = tatami::create_container_of_Index_size<std::vector<LeftIndex_> >(common_dim);
 
        std::optional<std::vector<Output_> > tmp_output;
        if (do_parallel) {
            tmp_output.emplace(tatami::cast_Index_to_container_size<std::vector<Output_> >(right_columns));
        }
 
        for (LeftIndex_ lr = 0; lr < length; ++lr) {
            const auto range = ext->fetch(vbuffer.data(), ibuffer.data());
            const auto optr =  output + sanisizer::product_unsafe<std::size_t>(start + lr, right_columns);
            const auto tmp_optr = (do_parallel ? tmp_output->data() : optr);
 
            for (LeftIndex_ x = 0; x < range.number; ++x) {
                const auto rightrow = get_right_row(range.index[x]);
                const auto mult = range.value[x];
                for (RightColumns_ rc = 0; rc < right_columns; ++rc) {
                    tmp_optr[rc] += mult * rightrow[rc];
                }
            }
 
            if (do_parallel) {
                if (range.number == 0) {
                    // If it's empty, we would have never modified the temporary buffer,
                    // so we can proceed to directly zeroing the output array.
                    std::fill_n(optr, right_columns, 0);
                } else {
                    std::copy_n(tmp_output->data(), right_columns, optr);
                    std::fill_n(tmp_output->data(), right_columns, 0);
                }
            }
        }
    }, left_NR, options.num_threads);
}
 
template<typename LeftValue_, typename LeftIndex_, typename RightValue_, typename RightIndex_, typename Output_>
void multiply_sparse_row_with_dense_row_matrix_to_row_output(
    const tatami::Matrix<LeftValue_, LeftIndex_>& left,
    const tatami::Matrix<RightValue_, RightIndex_>& right,
    Output_* const output,
    const MultiplySparseRowWithDenseRowMatrixToRowOutputOptions& options
) {
    const auto common_dim = left.ncol();
    auto right_buffers = tatami::create_container_of_Index_size<std::vector<std::vector<RightValue_> > >(common_dim);
    auto right_ptrs = tatami::create_container_of_Index_size<std::vector<const RightValue_*> >(common_dim);
    const auto right_NC = right.ncol();
    populate_dense_buffers(true, common_dim, right_NC, right, right_buffers, right_ptrs, options.num_threads);
 
    multiply_sparse_row_with_dense_row_matrix_to_row_output(
        left,
        right_NC,
        [&](const LeftIndex_ cd) -> const RightValue_* {
            return right_ptrs[cd];
        },
        output,
        options
    );
}
 
}
 
#endif