cl_v_map.hpp

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/* This file is part of spla project                                              */
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/* Copyright (c) 2023 SparseLinearAlgebra                                         */
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#ifndef SPLA_CL_V_MAP_HPP
#define SPLA_CL_V_MAP_HPP
 
#include <schedule/schedule_tasks.hpp>
 
#include <core/dispatcher.hpp>
#include <core/registry.hpp>
#include <core/top.hpp>
#include <core/tscalar.hpp>
#include <core/ttype.hpp>
#include <core/tvector.hpp>
 
#include <opencl/cl_formats.hpp>
#include <opencl/cl_map.hpp>
 
#include <sstream>
 
namespace spla {
 
    template<typename T>
    class Algo_v_map_cl final : public RegistryAlgo {
    public:
        ~Algo_v_map_cl() override = default;
 
        std::string get_name() override {
            return "v_map";
        }
 
        std::string get_description() override {
            return "parallel vector map on opencl device";
        }
 
        Status execute(const DispatchContext& ctx) override {
            auto                t = ctx.task.template cast_safe<ScheduleTask_v_map>();
            ref_ptr<TVector<T>> v = t->v.template cast_safe<TVector<T>>();
 
            if (v->is_valid(FormatVector::AccCoo)) {
                return execute_sp(ctx);
            }
            if (v->is_valid(FormatVector::AccDense)) {
                return execute_dn(ctx);
            }
            if (v->is_valid(FormatVector::CpuCoo)) {
                return execute_sp(ctx);
            }
            if (v->is_valid(FormatVector::CpuDense)) {
                return execute_dn(ctx);
            }
 
            return execute_sp(ctx);
        }
 
    private:
        Status execute_dn(const DispatchContext& ctx) {
            TIME_PROFILE_SCOPE("opencl/vector_map_dense");
 
            auto t = ctx.task.template cast_safe<ScheduleTask_v_map>();
 
            auto r  = t->r.template cast_safe<TVector<T>>();
            auto v  = t->v.template cast_safe<TVector<T>>();
            auto op = t->op.template cast_safe<TOpUnary<T, T>>();
 
            r->validate_wd(FormatVector::AccDense);
            v->validate_rw(FormatVector::AccDense);
 
            auto*       p_cl_dense_r = r->template get<CLDenseVec<T>>();
            const auto* p_cl_dense_v = v->template get<CLDenseVec<T>>();
            auto*       p_cl_acc     = get_acc_cl();
            auto&       queue        = p_cl_acc->get_queue_default();
 
            cl_map(queue, p_cl_dense_v->Ax, p_cl_dense_r->Ax, r->get_n_rows(), op);
 
            return Status::Ok;
        }
 
        Status execute_sp(const DispatchContext& ctx) {
            TIME_PROFILE_SCOPE("opencl/vector_map_sparse");
 
            auto t = ctx.task.template cast_safe<ScheduleTask_v_map>();
 
            auto r  = t->r.template cast_safe<TVector<T>>();
            auto v  = t->v.template cast_safe<TVector<T>>();
            auto op = t->op.template cast_safe<TOpUnary<T, T>>();
 
            r->validate_wd(FormatVector::AccCoo);
            v->validate_rw(FormatVector::AccCoo);
 
            auto*       p_cl_coo_r = r->template get<CLCooVec<T>>();
            const auto* p_cl_coo_v = v->template get<CLCooVec<T>>();
            auto*       p_cl_acc   = get_acc_cl();
            auto&       queue      = p_cl_acc->get_queue_default();
 
            const uint N = p_cl_coo_v->values;
 
            cl_coo_vec_resize(N, *p_cl_coo_r);
 
            queue.enqueueCopyBuffer(p_cl_coo_v->Ai, p_cl_coo_r->Ai, 0, 0, sizeof(uint) * N);
            cl_map(queue, p_cl_coo_v->Ax, p_cl_coo_r->Ax, N, op);
 
            return Status::Ok;
        }
    };
 
}// namespace spla
 
#endif//SPLA_CL_V_MAP_HPP