row_to_column.hpp

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#ifndef TATAMI_MULT_SPARSE_MATRIX_DENSE_COLUMN_ROW_TO_COLUMN_HPP
#define TATAMI_MULT_SPARSE_MATRIX_DENSE_COLUMN_ROW_TO_COLUMN_HPP
 
#include <cstddef>
#include <vector>
#include <optional>
 
#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/dense_column/sparse_matrix
 * for an explanation of the choice of algorithm.
 */
 
struct MultiplyDenseColumnWithSparseRowMatrixToColumnOutputOptions {
    int num_threads = 1;
};
 
template<typename LeftValue_, typename LeftIndex_, typename RightValue_, typename RightIndex_, typename Output_>
void multiply_dense_column_with_sparse_row_matrix_to_column_output(
    const tatami::Matrix<LeftValue_, LeftIndex_>& left,
    const tatami::Matrix<RightValue_, RightIndex_>& right,
    Output_* const output,
    const MultiplyDenseColumnWithSparseRowMatrixToColumnOutputOptions& options
) {
    const auto left_NR = left.nrow();
    const auto common_dim = left.ncol();
    const auto right_NC = right.ncol();
 
    const bool do_parallel = options.num_threads > 1;
    std::optional<std::vector<std::optional<std::vector<Output_> > > > tmp_results;
    if (do_parallel) {
        tmp_results.emplace(sanisizer::cast<I<decltype(tmp_results->size())> >(options.num_threads - 1));
    }
 
    std::fill_n(output, sanisizer::product_unsafe<std::size_t>(left_NR, right_NC), 0);
 
    const int num_used = tatami::parallelize([&](int t, LeftIndex_ start, LeftIndex_ length) -> void {
        auto left_ext = tatami::consecutive_extractor<false>(left, false, start, length);
        auto right_ext = tatami::consecutive_extractor<true>(right, true, start, length);
 
        std::optional<std::vector<Output_> > tmp_output;
        Output_* outptr; 
        if (!do_parallel || t == 0) {
            outptr = output;
        } else {
            tmp_output.emplace(sanisizer::product<I<decltype(tmp_output->size())> >(left_NR, right_NC));
            outptr = tmp_output->data();
        }
 
        auto dbuffer = tatami::create_container_of_Index_size<std::vector<LeftValue_> >(left_NR);
        auto vbuffer = tatami::create_container_of_Index_size<std::vector<RightValue_> >(right_NC);
        auto ibuffer = tatami::create_container_of_Index_size<std::vector<RightIndex_> >(right_NC);
 
        for (LeftIndex_ cd = 0; cd < length; ++cd) {
            const auto lptr = left_ext->fetch(dbuffer.data());
            const auto rrange = right_ext->fetch(vbuffer.data(), ibuffer.data());
 
            // Do this after all fetch() calls, to ensure that they both remain in sync with iteration through common_dim.
            if (rrange.number == 0) {
                continue;
            }
 
            for (RightIndex_ x = 0; x < rrange.number; ++x) {
                const Output_ mult = rrange.value[x];
                const auto idx = rrange.index[x];
                for (LeftIndex_ lr = 0; lr < left_NR; ++lr) {
                    outptr[sanisizer::nd_offset<std::size_t>(lr, left_NR, idx)] += mult * static_cast<Output_>(lptr[lr]);
                }
            }
        }
 
        if (do_parallel && t > 0) {
            (*tmp_results)[t - 1] = std::move(tmp_output);
        }
    }, common_dim, options.num_threads);
 
    if (do_parallel) {
        for (int u = 1; u < num_used; ++u) {
            const auto& tmp = *((*tmp_results)[u - 1]);
            const auto N = tmp.size();
            for (I<decltype(N)> x = 0; x < N; ++x) {
                output[x] += tmp[x];
            }
        }
    }
}
 
}
 
#endif