cpu_m_extract_column.hpp

Go to the documentation of this file.

/**********************************************************************************/
/* This file is part of spla project                                              */
/* https://github.com/JetBrains-Research/spla                                     */
/**********************************************************************************/
/* MIT License                                                                    */
/*                                                                                */
/* Copyright (c) 2023 SparseLinearAlgebra                                         */
/*                                                                                */
/* Permission is hereby granted, free of charge, to any person obtaining a copy   */
/* of this software and associated documentation files (the "Software"), to deal  */
/* in the Software without restriction, including without limitation the rights   */
/* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell      */
/* copies of the Software, and to permit persons to whom the Software is          */
/* furnished to do so, subject to the following conditions:                       */
/*                                                                                */
/* The above copyright notice and this permission notice shall be included in all */
/* copies or substantial portions of the Software.                                */
/*                                                                                */
/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR     */
/* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,       */
/* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE    */
/* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER         */
/* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,  */
/* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE  */
/* SOFTWARE.                                                                      */
/**********************************************************************************/
 
#ifndef SPLA_CPU_M_EXTRACT_COLUMN_HPP
#define SPLA_CPU_M_EXTRACT_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>
#include <iterator>
#include <numeric>
 
namespace spla {
 
    template<typename T>
    class Algo_m_extract_column_cpu final : public RegistryAlgo {
    public:
        ~Algo_m_extract_column_cpu() override = default;
 
        std::string get_name() override {
            return "m_extract_column";
        }
 
        std::string get_description() override {
            return "extract matrix column on cpu sequentially";
        }
 
        Status execute(const DispatchContext& ctx) override {
            auto t = ctx.task.template cast_safe<ScheduleTask_m_extract_column>();
            auto M = t->M.template cast_safe<TMatrix<T>>();
 
            if (M->is_valid(FormatMatrix::CpuCsr)) {
                return execute_csr(ctx);
            }
            if (M->is_valid(FormatMatrix::CpuLil)) {
                return execute_lil(ctx);
            }
            if (M->is_valid(FormatMatrix::CpuDok)) {
                return execute_dok(ctx);
            }
 
            return execute_csr(ctx);
        }
 
    private:
        Status execute_dok(const DispatchContext& ctx) {
            TIME_PROFILE_SCOPE("cpu/m_extract_column_dok");
 
            auto t = ctx.task.template cast_safe<ScheduleTask_m_extract_column>();
 
            ref_ptr<TVector<T>>     r        = t->r.template cast_safe<TVector<T>>();
            ref_ptr<TMatrix<T>>     M        = t->M.template cast_safe<TMatrix<T>>();
            ref_ptr<TOpUnary<T, T>> op_apply = t->op_apply.template cast_safe<TOpUnary<T, T>>();
            uint                    index    = t->index;
 
            r->validate_wd(FormatVector::CpuCoo);
            M->validate_rw(FormatMatrix::CpuDok);
 
            CpuCooVec<T>*    p_coo_r    = r->template get<CpuCooVec<T>>();
            const CpuDok<T>* p_dok_M    = M->template get<CpuDok<T>>();
            auto&            func_apply = op_apply->function;
 
            for (const auto [key, value] : p_dok_M->Ax) {
                if (key.second == index) {
                    p_coo_r->values += 1;
                    p_coo_r->Ai.push_back(key.first);
                    p_coo_r->Ax.push_back(func_apply(value));
                }
            }
 
            cpu_coo_vec_sort(*p_coo_r);
 
            return Status::Ok;
        }
 
        Status execute_lil(const DispatchContext& ctx) {
            TIME_PROFILE_SCOPE("cpu/m_extract_column_lil");
 
            auto t = ctx.task.template cast_safe<ScheduleTask_m_extract_column>();
 
            ref_ptr<TVector<T>>     r        = t->r.template cast_safe<TVector<T>>();
            ref_ptr<TMatrix<T>>     M        = t->M.template cast_safe<TMatrix<T>>();
            ref_ptr<TOpUnary<T, T>> op_apply = t->op_apply.template cast_safe<TOpUnary<T, T>>();
            uint                    index    = t->index;
 
            r->validate_wd(FormatVector::CpuCoo);
            M->validate_rw(FormatMatrix::CpuLil);
 
            CpuCooVec<T>*    p_coo_r    = r->template get<CpuCooVec<T>>();
            const CpuLil<T>* p_lil_M    = M->template get<CpuLil<T>>();
            auto&            func_apply = op_apply->function;
 
            for (uint i = 0; i < M->get_n_rows(); i++) {
                const auto& row = p_lil_M->Ar[i];
 
                typename CpuLil<T>::Entry fake{index, T()};
                auto                      query = std::lower_bound(row.begin(), row.end(), fake, [](auto& a, auto& b) { return a.first < b.first; });
 
                if (query != row.end() && query->first == index) {
                    p_coo_r->values += 1;
                    p_coo_r->Ai.push_back(i);
                    p_coo_r->Ax.push_back(func_apply(query->second));
                }
            }
 
            return Status::Ok;
        }
 
        Status execute_csr(const DispatchContext& ctx) {
            TIME_PROFILE_SCOPE("cpu/m_extract_column_csr");
 
            auto t = ctx.task.template cast_safe<ScheduleTask_m_extract_column>();
 
            ref_ptr<TVector<T>>     r        = t->r.template cast_safe<TVector<T>>();
            ref_ptr<TMatrix<T>>     M        = t->M.template cast_safe<TMatrix<T>>();
            ref_ptr<TOpUnary<T, T>> op_apply = t->op_apply.template cast_safe<TOpUnary<T, T>>();
            uint                    index    = t->index;
 
            r->validate_wd(FormatVector::CpuCoo);
            M->validate_rw(FormatMatrix::CpuCsr);
 
            CpuCooVec<T>*    p_coo_r    = r->template get<CpuCooVec<T>>();
            const CpuCsr<T>* p_csr_M    = M->template get<CpuCsr<T>>();
            auto&            func_apply = op_apply->function;
 
            for (uint i = 0; i < M->get_n_rows(); i++) {
                const auto row_begin = p_csr_M->Aj.begin() + p_csr_M->Ap[i];
                const auto row_end   = p_csr_M->Aj.begin() + p_csr_M->Ap[i + 1];
 
                auto query = std::lower_bound(row_begin, row_end, index);
 
                if (query != row_end && *query == index) {
                    p_coo_r->values += 1;
                    p_coo_r->Ai.push_back(i);
                    p_coo_r->Ax.push_back(func_apply(p_csr_M->Ax[std::distance(p_csr_M->Aj.begin(), query)]));
                }
            }
 
            return Status::Ok;
        }
    };
 
}// namespace spla
 
#endif//SPLA_CPU_M_EXTRACT_COLUMN_HPP