cuSPARSE

简介

cuSPARSE 库为稀疏矩阵提供经 GPU 加速的基本线性代数子程序,与仅使用 CPU 的替代方案相比,这一程序的执行速度有显著的提升。此库提供了用于构建 GPU 加速型求解器的功能。在从事机器学习、计算流体力学、地震勘探及计算科学等应用的工程师和科学家群体中,cuSPARSE 得到了广泛采用。通过使用 cuSPARSE,应用将能自动从定期性能提升及全新 GPU 架构中受益。cuSPARSE 库包含在 NVIDIA HPC SDK 和 CUDA 工具包中。

cuSPARSE使用说明

思源一号上的cuSPARSE

  1. 先创建一个目录cuSPARSEtest并进入该目录:

mkdir cuSPARSEtest
cd cuSPARSEtest
  1. 在该目录下创建如下测试文件cuSPARSEtest.cu:

// 求解下三角稀疏线性方程组Ax=b

#include <cuda_runtime_api.h> // cudaMalloc, cudaMemcpy, etc.
#include <cusparse.h>         // cusparseSpSV
#include <stdio.h>            // printf
#include <stdlib.h>           // EXIT_FAILURE

#define CHECK_CUDA(func)                                               \
{                                                                  \
    cudaError_t status = (func);                                   \
    if (status != cudaSuccess)                                     \
    {                                                              \
        printf("CUDA API failed at line %d with error: %s (%d)\n", \
               __LINE__, cudaGetErrorString(status), status);      \
        return EXIT_FAILURE;                                       \
    }                                                              \
}

#define CHECK_CUSPARSE(func)                                               \
{                                                                      \
    cusparseStatus_t status = (func);                                  \
    if (status != CUSPARSE_STATUS_SUCCESS)                             \
    {                                                                  \
        printf("CUSPARSE API failed at line %d with error: %s (%d)\n", \
               __LINE__, cusparseGetErrorString(status), status);      \
        return EXIT_FAILURE;                                           \
    }                                                                  \
}

int main(void)
{
    // A = [1, 0, 0, 0
    //      0, 4, 0, 0
    //      5, 0, 6, 0
    //      0, 8, 0, 9]

    // b = [1
    //      8
    //      23
    //      52]

    // Host problem definition
    const int A_num_rows = 4;
    const int A_num_cols = 4;
    const int A_nnz = 6;
    int hA_csrOffsets[] = {0, 1, 2, 4, 6};
    int hA_columns[] = {0, 1, 0, 2, 1, 3};
    float hA_values[] = {1.0f, 4.0f, 5.0f, 6.0f, 8.0f, 9.0f};
    float hX[] = {1.0f, 8.0f, 23.0f, 52.0f};
    float hY[] = {0.0f, 0.0f, 0.0f, 0.0f};
    float hY_result[] = {1.0f, 2.0f, 3.0f, 4.0f};
    float alpha = 1.0f;
    //--------------------------------------------------------------------------
    // Device memory management
    int *dA_csrOffsets, *dA_columns;
    float *dA_values, *dX, *dY;
    CHECK_CUDA(cudaMalloc((void **)&dA_csrOffsets, (A_num_rows + 1) * sizeof(int)))
    CHECK_CUDA(cudaMalloc((void **)&dA_columns, A_nnz * sizeof(int)))
    CHECK_CUDA(cudaMalloc((void **)&dA_values, A_nnz * sizeof(float)))
    CHECK_CUDA(cudaMalloc((void **)&dX, A_num_cols * sizeof(float)))
    CHECK_CUDA(cudaMalloc((void **)&dY, A_num_rows * sizeof(float)))

    CHECK_CUDA(cudaMemcpy(dA_csrOffsets, hA_csrOffsets, (A_num_rows + 1) * sizeof(int), cudaMemcpyHostToDevice))
    CHECK_CUDA(cudaMemcpy(dA_columns, hA_columns, A_nnz * sizeof(int), cudaMemcpyHostToDevice))
    CHECK_CUDA(cudaMemcpy(dA_values, hA_values, A_nnz * sizeof(float), cudaMemcpyHostToDevice))
    CHECK_CUDA(cudaMemcpy(dX, hX, A_num_cols * sizeof(float), cudaMemcpyHostToDevice))
    CHECK_CUDA(cudaMemcpy(dY, hY, A_num_rows * sizeof(float), cudaMemcpyHostToDevice))
    //--------------------------------------------------------------------------
    // CUSPARSE APIs
    cusparseHandle_t handle = NULL;
    cusparseSpMatDescr_t matA;
    cusparseDnVecDescr_t vecX, vecY;
    void *dBuffer = NULL;
    size_t bufferSize = 0;
    cusparseSpSVDescr_t spsvDescr;
    CHECK_CUSPARSE(cusparseCreate(&handle))
    // Create sparse matrix A in CSR format
    CHECK_CUSPARSE(cusparseCreateCsr(&matA, A_num_rows, A_num_cols, A_nnz,
                                     dA_csrOffsets, dA_columns, dA_values,
                                     CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I,
                                     CUSPARSE_INDEX_BASE_ZERO, CUDA_R_32F))
    // Create dense vector X
    CHECK_CUSPARSE(cusparseCreateDnVec(&vecX, A_num_cols, dX, CUDA_R_32F))
    // Create dense vector y
    CHECK_CUSPARSE(cusparseCreateDnVec(&vecY, A_num_rows, dY, CUDA_R_32F))
    // Create opaque data structure, that holds analysis data between calls.
    CHECK_CUSPARSE(cusparseSpSV_createDescr(&spsvDescr))
    // Specify Lower|Upper fill mode.
    cusparseFillMode_t fillmode = CUSPARSE_FILL_MODE_LOWER;
    CHECK_CUSPARSE(cusparseSpMatSetAttribute(matA, CUSPARSE_SPMAT_FILL_MODE, &fillmode, sizeof(fillmode)))
    // Specify Unit|Non-Unit diagonal type.
    cusparseDiagType_t diagtype = CUSPARSE_DIAG_TYPE_NON_UNIT;
    CHECK_CUSPARSE(cusparseSpMatSetAttribute(matA, CUSPARSE_SPMAT_DIAG_TYPE, &diagtype, sizeof(diagtype)))
    // allocate an external buffer for analysis
    CHECK_CUSPARSE(cusparseSpSV_bufferSize(handle, CUSPARSE_OPERATION_NON_TRANSPOSE,
                                           &alpha, matA, vecX, vecY, CUDA_R_32F,
                                           CUSPARSE_SPSV_ALG_DEFAULT, spsvDescr,
                                           &bufferSize))
    CHECK_CUDA(cudaMalloc(&dBuffer, bufferSize))
    CHECK_CUSPARSE(cusparseSpSV_analysis(handle, CUSPARSE_OPERATION_NON_TRANSPOSE,
                                         &alpha, matA, vecX, vecY, CUDA_R_32F,
                                         CUSPARSE_SPSV_ALG_DEFAULT, spsvDescr, dBuffer))
    // execute SpSV
    CHECK_CUSPARSE(cusparseSpSV_solve(handle, CUSPARSE_OPERATION_NON_TRANSPOSE,
                                      &alpha, matA, vecX, vecY, CUDA_R_32F,
                                      CUSPARSE_SPSV_ALG_DEFAULT, spsvDescr))

    // destroy matrix/vector descriptors
    CHECK_CUSPARSE(cusparseDestroySpMat(matA))
    CHECK_CUSPARSE(cusparseDestroyDnVec(vecX))
    CHECK_CUSPARSE(cusparseDestroyDnVec(vecY))
    CHECK_CUSPARSE(cusparseSpSV_destroyDescr(spsvDescr));
    CHECK_CUSPARSE(cusparseDestroy(handle))
    //--------------------------------------------------------------------------
    // device result check
    CHECK_CUDA(cudaMemcpy(hY, dY, A_num_rows * sizeof(float), cudaMemcpyDeviceToHost))
    int correct = 1;
    for (int i = 0; i < A_num_rows; i++)
    {
        if (hY[i] != hY_result[i])
        {                // direct floating point comparison is not
            correct = 0; // reliable
            break;
        }
    }
    if (correct)
        printf("spsv_csr_example test PASSED\n");
    else
        printf("spsv_csr_example test FAILED: wrong result\n");
    for (size_t i = 0; i < A_num_rows; i++)
    {
        printf("x[%d] = %f\n", i, hY[i]);
    }
    //--------------------------------------------------------------------------
    // device memory deallocation
    CHECK_CUDA(cudaFree(dBuffer))
    CHECK_CUDA(cudaFree(dA_csrOffsets))
    CHECK_CUDA(cudaFree(dA_columns))
    CHECK_CUDA(cudaFree(dA_values))
    CHECK_CUDA(cudaFree(dX))
    CHECK_CUDA(cudaFree(dY))
    return EXIT_SUCCESS;
}
  1. 在该目录下创建如下作业提交脚本cuSPARSEtest.slurm:

#!/bin/bash

#SBATCH --job-name=cuSPARSEtest        # 作业名
#SBATCH --partition=a100             # a100 队列
#SBATCH -N 1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=1            # 1:1 的 GPU:CPU 配比
#SBATCH --gres=gpu:1                 # 1 块 GPU
#SBATCH --output=%j.out
#SBATCH --error=%j.err

module load cuda/11.5.0
module load gcc/11.2.0

nvcc cuSPARSEtest.cu -o cuSPARSEtest -lcusparse
./cuSPARSEtest
  1. 使用如下命令提交作业:

sbatch cuSPARSEtest.slurm
  1. 作业完成后在.out文件中可看到如下结果:

spsv_csr_example test PASSED
x[0] = 1.000000
x[1] = 2.000000
x[2] = 3.000000
x[3] = 4.000000

pi2.0上的cuSPARSE

  1. 此步骤和上文完全相同;

  2. 此步骤和上文完全相同;

  3. 在该目录下创建如下作业提交脚本cuSPARSEtest.slurm:

#!/bin/bash

#SBATCH --job-name=cuSPARSEtest        # 作业名
#SBATCH --partition=dgx2             # dgx2 队列
#SBATCH -N 1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=1            # 1:1 的 GPU:CPU 配比
#SBATCH --gres=gpu:1                 # 1 块 GPU
#SBATCH --output=%j.out
#SBATCH --error=%j.err

module load cuda/11.6.2-gcc-8.3.0
module load gcc/8.3.0

nvcc cuSPARSEtest.cu -o cuSPARSEtest -lcusparse
./cuSPARSEtest
  1. 使用如下命令提交作业:

sbatch cuSPARSEtest.slurm
  1. 作业完成后在.out文件中可看到如下结果:

spsv_csr_example test PASSED
x[0] = 1.000000
x[1] = 2.000000
x[2] = 3.000000
x[3] = 4.000000

参考资料


最后更新: 2024 年 11 月 14 日