cl_v_reduce.hpp

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#ifndef SPLA_CL_V_REDUCE_HPP
#define SPLA_CL_V_REDUCE_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_reduce.hpp>
 
#include <sstream>
 
namespace spla {
 
    template<typename T>
    class Algo_v_reduce_cl final : public RegistryAlgo {
    public:
        ~Algo_v_reduce_cl() override = default;
 
        std::string get_name() override {
            return "v_reduce";
        }
 
        std::string get_description() override {
            return "parallel vector reduction on opencl device";
        }
 
        Status execute(const DispatchContext& ctx) override {
            auto                t = ctx.task.template cast_safe<ScheduleTask_v_reduce>();
            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_reduce_dense");
 
            auto t = ctx.task.template cast_safe<ScheduleTask_v_reduce>();
 
            auto r         = t->r.template cast_safe<TScalar<T>>();
            auto s         = t->s.template cast_safe<TScalar<T>>();
            auto v         = t->v.template cast_safe<TVector<T>>();
            auto op_reduce = t->op_reduce.template cast_safe<TOpBinary<T, T, T>>();
 
            v->validate_rw(FormatVector::AccDense);
 
            const auto* p_cl_dense_vec = v->template get<CLDenseVec<T>>();
            auto*       p_cl_acc       = get_acc_cl();
            auto&       queue          = p_cl_acc->get_queue_default();
 
            cl_reduce<T>(queue, p_cl_dense_vec->Ax, v->get_n_rows(), s->get_value(), op_reduce, r->get_value());
 
            return Status::Ok;
        }
 
        Status execute_sp(const DispatchContext& ctx) {
            TIME_PROFILE_SCOPE("opencl/vector_reduce_sparse");
 
            auto t = ctx.task.template cast_safe<ScheduleTask_v_reduce>();
 
            auto r         = t->r.template cast_safe<TScalar<T>>();
            auto s         = t->s.template cast_safe<TScalar<T>>();
            auto v         = t->v.template cast_safe<TVector<T>>();
            auto op_reduce = t->op_reduce.template cast_safe<TOpBinary<T, T, T>>();
 
            v->validate_rw(FormatVector::AccCoo);
 
            const auto* p_cl_coo_vec = v->template get<CLCooVec<T>>();
            auto*       p_cl_acc     = get_acc_cl();
            auto&       queue        = p_cl_acc->get_queue_default();
 
            cl_reduce<T>(queue, p_cl_coo_vec->Ax, p_cl_coo_vec->values, s->get_value(), op_reduce, r->get_value());
 
            return Status::Ok;
        }
    };
 
}// namespace spla
 
#endif//SPLA_CL_V_REDUCE_HPP