cpu_kron.hpp

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#ifndef SPLA_CPU_KRON_HPP
#define SPLA_CPU_KRON_HPP
 
#include <schedule/schedule_tasks.hpp>
 
#include <core/dispatcher.hpp>
#include <core/registry.hpp>
#include <core/tmatrix.hpp>
#include <core/top.hpp>
#include <core/tscalar.hpp>
#include <core/ttype.hpp>
#include <core/tvector.hpp>
 
namespace spla {
 
    template<typename T>
    class Algo_kron_cpu final : public RegistryAlgo {
    public:
        ~Algo_kron_cpu() override = default;
 
        std::string get_name() override {
            return "kron";
        }
 
        std::string get_description() override {
            return "sequential sparse matrix kronecker product on cpu";
        }
 
        Status execute(const DispatchContext& ctx) override {
            TIME_PROFILE_SCOPE("cpu/kron");
 
            auto t = ctx.task.template cast_safe<ScheduleTask_kron>();
 
            auto R           = t->R.template cast_safe<TMatrix<T>>();
            auto A           = t->A.template cast_safe<TMatrix<T>>();
            auto B           = t->B.template cast_safe<TMatrix<T>>();
            auto op_multiply = t->op_multiply.template cast_safe<TOpBinary<T, T, T>>();
 
            R->validate_wd(FormatMatrix::CpuLil);
            A->validate_rw(FormatMatrix::CpuLil);
            B->validate_rw(FormatMatrix::CpuLil);
 
            CpuLil<T>*       p_lil_R = R->template get<CpuLil<T>>();
            const CpuLil<T>* p_lil_A = A->template get<CpuLil<T>>();
            const CpuLil<T>* p_lil_B = B->template get<CpuLil<T>>();
 
            auto& func_multiply = op_multiply->function;
 
            auto D_AM = A->get_n_rows();
            auto D_AN = A->get_n_cols();
            auto D_BM = B->get_n_rows();
            auto D_BN = B->get_n_cols();
 
            assert(R->get_n_rows() == D_AM * D_BM);
            assert(R->get_n_cols() == D_AN * D_BN);
 
            assert(p_lil_R->Ar.size() == D_AM * D_BM);
 
            for (uint i_A = 0; i_A < D_AM; i_A++) {
                const auto& row_A = p_lil_A->Ar[i_A];
 
                for (const auto [j_A, x_A] : row_A) {
 
                    for (uint i_B = 0; i_B < D_BM; i_B++) {
                        const auto& row_B = p_lil_B->Ar[i_B];
 
                        auto& row_R = p_lil_R->Ar[i_A * D_BM + i_B];
                        row_R.reserve(row_A.size() * row_B.size());
 
                        for (const auto [j_B, x_B] : row_B) {
                            row_R.emplace_back(j_A * D_BN + j_B, func_multiply(x_A, x_B));
                        }
                    }
                }
            }
 
            p_lil_R->values = p_lil_A->values * p_lil_B->values;
 
            return Status::Ok;
        }
    };
 
}// namespace spla
 
#endif//SPLA_CPU_KRON_HPP