tsgGpuWrappers.hpp

/*
 * Copyright (c) 2017, Miroslav Stoyanov
 *
 * This file is part of
 * Toolkit for Adaptive Stochastic Modeling And Non-Intrusive ApproximatioN: TASMANIAN
 *
 * Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
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#ifndef __TASMANIAN_GPU_WRAPPERS_HPP
#define __TASMANIAN_GPU_WRAPPERS_HPP

 
#include "tsgAcceleratedDataStructures.hpp"
 
namespace TasGrid{
namespace TasGpu{

template<typename scalar_type>
void solveLSmultiGPU(AccelerationContext const *acceleration, int n, int m, scalar_type A[], int nrhs, scalar_type B[]);

template<typename scalar_type>
void solveLSmultiOOC(AccelerationContext const *acceleration, int n, int m, scalar_type A[], int nrhs, scalar_type B[]);

template<typename scalar_type>
void solveLSmulti(AccelerationContext const *acceleration, int n, int m, scalar_type A[], int nrhs, scalar_type B[]){
    GpuVector<scalar_type> gpuA(acceleration, m, n, A);
    GpuVector<scalar_type> gpuB(acceleration, nrhs, n, B);
    solveLSmultiGPU(acceleration, n, m, gpuA.data(), nrhs, gpuB.data());
    gpuB.unload(acceleration, B);
}

void factorizePLU(AccelerationContext const *acceleration, int n, double A[], int_gpu_lapack ipiv[]);
void solvePLU(AccelerationContext const *acceleration, char trans, int n, double const A[], int_gpu_lapack const ipiv[], double b[]);
void solvePLU(AccelerationContext const *acceleration, char trans, int n, double const A[], int_gpu_lapack const ipiv[], int nrhs, double B[]);

template<typename scalar_type>
void denseMultiply(AccelerationContext const *acceleration, int M, int N, int K,
                   typename GpuVector<scalar_type>::value_type alpha, GpuVector<scalar_type> const &A,
                   GpuVector<scalar_type> const &B, typename GpuVector<scalar_type>::value_type beta, scalar_type C[]);

template<typename scalar_type>
void denseMultiplyMixed(AccelerationContext const *acceleration, int M, int N, int K, typename GpuVector<scalar_type>::value_type alpha,
                        GpuVector<scalar_type> const &A, scalar_type const B[],
                        typename GpuVector<scalar_type>::value_type beta, scalar_type C[]){
    GpuVector<scalar_type> gpuB(acceleration, K, N, B), gpuC(acceleration, M, N);
    denseMultiply(acceleration, M, N, K, alpha, A, gpuB, beta, gpuC.data());
    gpuC.unload(acceleration, C);
}

template<typename scalar_type>
void sparseMultiply(AccelerationContext const *acceleration, int M, int N, int K, typename GpuVector<scalar_type>::value_type alpha,
                    const GpuVector<scalar_type> &A, const GpuVector<int> &pntr, const GpuVector<int> &indx,
                    const GpuVector<scalar_type> &vals, scalar_type C[]);

template<typename T>
void sparseMultiplyMixed(AccelerationContext const *acceleration, int M, int N, int K, typename GpuVector<T>::value_type alpha, const GpuVector<T> &A,
                         const std::vector<int> &pntr, const std::vector<int> &indx, const std::vector<T> &vals, T C[]){
    GpuVector<int> gpu_pntr(acceleration, pntr), gpu_indx(acceleration, indx);
    GpuVector<T> gpu_vals(acceleration, vals), gpu_c(acceleration, M, N);
    sparseMultiply(acceleration, M, N, K, alpha, A, gpu_pntr, gpu_indx, gpu_vals, gpu_c.data());
    gpu_c.unload(acceleration, C);
}
 
}
}
 
#endif