cpu_mxm.hpp

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#ifndef SPLA_CPU_MXM_HPP
#define SPLA_CPU_MXM_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>
 
#include <algorithm>
 
namespace spla {
 
    template<typename T>
    class Algo_mxm_cpu final : public RegistryAlgo {
    public:
        ~Algo_mxm_cpu() override = default;
 
        std::string get_name() override {
            return "mxm";
        }
 
        std::string get_description() override {
            return "sequential sparse matrix sparse matrix product on cpu";
        }
 
        Status execute(const DispatchContext& ctx) override {
            TIME_PROFILE_SCOPE("cpu/mxm");
 
            auto t = ctx.task.template cast_safe<ScheduleTask_mxm>();
 
            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>>();
            auto op_add      = t->op_add.template cast_safe<TOpBinary<T, T, T>>();
            auto init        = t->init.template cast_safe<TScalar<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& func_add      = op_add->function;
 
            auto DM = R->get_n_rows();
            auto DN = R->get_n_cols();
            auto I  = init->get_value();
 
            std::vector<T> R_tmp(DN, I);
 
            for (uint row_R = 0; row_R < DM; row_R++) {
                const auto& A_lst = p_lil_A->Ar[row_R];
                auto&       R_lst = p_lil_R->Ar[row_R];
 
                assert(R_lst.empty());
 
                std::fill(R_tmp.begin(), R_tmp.end(), I);
 
                for (const typename CpuLil<T>::Entry& entry_A : A_lst) {
                    const uint i       = entry_A.first;
                    const T    value_A = entry_A.second;
 
                    const auto& B_lst = p_lil_B->Ar[i];
 
                    for (const typename CpuLil<T>::Entry& entry_B : B_lst) {
                        const uint j       = entry_B.first;
                        const T    value_B = entry_B.second;
 
                        R_tmp[j] = func_add(R_tmp[j], func_multiply(value_A, value_B));
                    }
                }
 
                for (uint col_R = 0; col_R < DN; col_R++) {
                    if (R_tmp[col_R] != I) {
                        R_lst.emplace_back(col_R, R_tmp[col_R]);
                    }
                }
            }
 
            return Status::Ok;
        }
    };
 
}// namespace spla
 
#endif//SPLA_CPU_MXM_HPP