cpu_m_reduce_by_column.hpp

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#ifndef SPLA_CPU_M_REDUCE_BY_COLUMN_HPP
#define SPLA_CPU_M_REDUCE_BY_COLUMN_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_m_reduce_by_column_cpu final : public RegistryAlgo {
    public:
        ~Algo_m_reduce_by_column_cpu() override = default;
 
        std::string get_name() override {
            return "m_reduce_by_column";
        }
 
        std::string get_description() override {
            return "reduce matrix by column on cpu sequentially";
        }
 
        Status execute(const DispatchContext& ctx) override {
            auto t = ctx.task.template cast_safe<ScheduleTask_m_reduce_by_column>();
            auto M = t->M.template cast_safe<TMatrix<T>>();
 
            if (M->is_valid(FormatMatrix::CpuLil)) {
                return execute_lil(ctx);
            }
            if (M->is_valid(FormatMatrix::CpuDok)) {
                return execute_dok(ctx);
            }
 
            return execute_dok(ctx);
        }
 
    private:
        Status execute_dok(const DispatchContext& ctx) {
            auto t         = ctx.task.template cast_safe<ScheduleTask_m_reduce_by_column>();
            auto r         = t->r.template cast_safe<TVector<T>>();
            auto M         = t->M.template cast_safe<TMatrix<T>>();
            auto op_reduce = t->op_reduce.template cast_safe<TOpBinary<T, T, T>>();
            auto init      = t->init.template cast_safe<TScalar<T>>();
 
            r->validate_wd(FormatVector::CpuDense);
            M->validate_rw(FormatMatrix::CpuDok);
 
            CpuDenseVec<T>*  p_dense_r = r->template get<CpuDenseVec<T>>();
            const CpuDok<T>* p_dok_M   = M->template get<CpuDok<T>>();
 
            std::fill(p_dense_r->Ax.begin(), p_dense_r->Ax.end(), init->get_value());
 
            auto& func_reduce = op_reduce->function;
 
            for (const auto& entry : p_dok_M->Ax) {
                const uint j = entry.first.second;
                const T    x = entry.second;
 
                p_dense_r->Ax[j] = func_reduce(p_dense_r->Ax[j], x);
            }
 
            return Status::Ok;
        }
        Status execute_lil(const DispatchContext& ctx) {
            auto t         = ctx.task.template cast_safe<ScheduleTask_m_reduce_by_column>();
            auto r         = t->r.template cast_safe<TVector<T>>();
            auto M         = t->M.template cast_safe<TMatrix<T>>();
            auto op_reduce = t->op_reduce.template cast_safe<TOpBinary<T, T, T>>();
            auto init      = t->init.template cast_safe<TScalar<T>>();
 
            r->validate_wd(FormatVector::CpuDense);
            M->validate_rw(FormatMatrix::CpuLil);
 
            CpuDenseVec<T>*  p_dense_r = r->template get<CpuDenseVec<T>>();
            const CpuLil<T>* p_lil_M   = M->template get<CpuLil<T>>();
 
            std::fill(p_dense_r->Ax.begin(), p_dense_r->Ax.end(), init->get_value());
 
            auto& func_reduce = op_reduce->function;
 
            for (std::size_t i = 0; i < p_lil_M->Ar.size(); i++) {
                for (const auto& entry : p_lil_M->Ar[i]) {
                    const uint j = entry.first;
                    const T    x = entry.second;
 
                    p_dense_r->Ax[j] = func_reduce(p_dense_r->Ax[j], x);
                }
            }
 
            return Status::Ok;
        }
    };
 
}// namespace spla
 
#endif//SPLA_CPU_M_REDUCE_BY_COLUMN_HPP