如何用cusparse串行跑矩阵乘法

要使用CUSPARSE库实现串行矩阵乘法，可以按照以下步骤进行：

1. 定义矩阵和向量的维度和大小。
2. 分配和初始化矩阵和向量的内存空间。
3. 使用CUSPARSE函数创建矩阵和向量的描述符。
4. 使用CUSPARSE函数将稠密矩阵转换为稀疏矩阵。
5. 使用CUSPARSE函数执行稀疏矩阵向量乘法。
6. 处理结果并释放内存。

下面是一个使用CUSPARSE库实现矩阵向量乘法的简单示例代码：

#include <stdio.h>
#include <stdlib.h>
#include <cuda_runtime.h>
#include <cusparse.h>

int main()
{
    cusparseHandle_t handle;
    cusparseCreate(&handle);

    // Define matrix and vector dimensions and sizes
    int m = 3, n = 3;  // Matrix dimensions
    int nnz = 6;        // Number of non-zero elements in the matrix
    int size = n * sizeof(float);  // Size of the vector

    // Allocate and initialize matrix and vector memory
    float *h_A = (float *)malloc(nnz * sizeof(float));
    int *h_IA = (int *)malloc((n+1) * sizeof(int));
    int *h_JA = (int *)malloc(nnz * sizeof(int));
    float *h_x = (float *)malloc(size);
    float *h_y = (float *)malloc(size);

    // Initialize matrix and vector data
    h_A[0] = 1.0f; h_A[1] = 2.0f; h_A[2] = 3.0f;
    h_A[3] = 4.0f; h_A[4] = 5.0f; h_A[5] = 6.0f;
    h_IA[0] = 0; h_IA[1] = 2; h_IA[2] = 4; h_IA[3] = 6;
    h_JA[0] = 0; h_JA[1] = 1; h_JA[2] = 0; h_JA[3] = 1; h_JA[4] = 2; h_JA[5] = 2;
    h_x[0] = 1.0f; h_x[1] = 2.0f; h_x[2] = 3.0f;

    // Allocate device memory
    float *d_A, *d_x, *d_y;
    int *d_IA, *d_JA;
    cudaMalloc((void **)&d_A, nnz * sizeof(float));
    cudaMalloc((void **)&d_IA, (n+1) * sizeof(int));
    cudaMalloc((void **)&d_JA, nnz * sizeof(int));
    cudaMalloc((void **)&d_x, size);
    cudaMalloc((void **)&d_y, size);

    // Copy data to device memory
    cudaMemcpy(d_A, h_A, nnz * sizeof(float), cudaMemcpyHostToDevice);
    cudaMemcpy(d_IA, h_IA, (n+1) * sizeof(int), cudaMemcpyHostToDevice);
    cudaMemcpy(d_JA, h_JA, nnz * sizeof(int), cudaMemcpyHostToDevice);
    cudaMemcpy(d_x, h_x, size, cudaMemcpyHostToDevice);

    // Create matrix and vector descriptors
    cusparseMatDescr_t descrA, descrY;
    cusparseCreateMatDescr(&descrA);
    cusparseCreateMatDescr(&descrY);

    // Set matrix and vector properties
    cusparseSetMatType(descrA, CUSPARSE_MATRIX_TYPE_GENERAL);
    cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ZERO);
    cusparseSetMatType(descrY, CUSPARSE_MATRIX_TYPE_GENERAL);
    cusparseSetMatIndexBase(descrY, CUSPARSE_INDEX_BASE_ZERO);

    // Convert dense matrix to sparse matrix
    cusparseDense2Csr(handle, m, n, descrA, d_A, n, d_IA, d_JA);

    // Perform sparse matrix-vector multiplication
    float alpha = 1.0f, beta = 0.0f;
    cusparseScsrmv(handle, CUSPARSE_OPERATION_NON_TRANSPOSE, n, n, nnz, &alpha, descrA, d_A, d_IA, d_JA, d_x, &beta, d_y);

    // Copy result back to host memory
    cudaMemcpy(h_y, d_y, size, cudaMemcpyDeviceToHost);

    // Print result
    printf("Result: {%f, %f, %f}\n", h_y[0], h_y[1], h_y[2]);

    // Free memory
    free(h_A);
    free(h_IA);
    free(h_JA);
    free(h_x);
    free(h_y);
    cudaFree(d_A);
    cudaFree(d_IA);
    cudaFree(d_JA);
    cudaFree(d_x);
    cudaFree(d_y);
    cusparseDestroyMatDescr(descrA);
    cusparseDestroyMatDescr(descrY);
    cusparseDestroy(handle);

    return 0;
}

该示例代码将一个3x3的稠密矩阵转换为稀疏矩阵并与一个3维向量相乘，然后打印结果。请注意，为了简化代码，错误检查已省略。在实际使用中，必须正确地检查每个CUSPARSE函数的返回值以确保操作成功。