CUDA 矩阵特征值计算示例 - 使用 cuSolver 库

#include <stdio.h>\n#include <stdlib.h>\n#include <cuda_runtime.h>\n#include <cusolverDn.h>\n\n#define N 3\n\nvoid printMatrix(float *matrix, int rows, int cols) {\n for (int i = 0; i < rows; i++) {\n for (int j = 0; j < cols; j++) {\n printf("%f ", matrix[i * cols + j]);\n }\n printf("\n");\n }\n}\n\nint main() {\n float *h_A, *d_A, *h_W, *h_work;\n int lwork;\n int *devInfo;\n cusolverDnHandle_t handle;\n cusolverDnEigMode_t jobz = CUSOLVER_EIG_MODE_NOVECTOR;\n cublasFillMode_t uplo = CUBLAS_FILL_MODE_LOWER;\n\n // Allocate host memory\n h_A = (float *)malloc(N * N * sizeof(float));\n h_W = (float *)malloc(N * sizeof(float));\n h_work = (float *)malloc(sizeof(float));\n devInfo = (int )malloc(sizeof(int));\n\n // Generate random matrix\n for (int i = 0; i < N; i++) {\n for (int j = 0; j < N; j++) {\n h_A[i * N + j] = (float)rand() / RAND_MAX;\n }\n }\n\n // Allocate device memory\n cudaMalloc((void **)&d_A, N * N * sizeof(float));\n\n // Copy input matrix from host to device\n cudaMemcpy(d_A, h_A, N * N * sizeof(float), cudaMemcpyHostToDevice);\n\n // Create cuSolver handle\n cusolverDnCreate(&handle);\n\n // Query working space of eig decomposition\n cusolverDnSsyevd_bufferSize(handle, jobz, uplo, N, d_A, N, h_W, &lwork);\n\n // Allocate working space\n cudaMalloc((void **)&h_work, lwork * sizeof(float));\n\n // Compute eig decomposition\n cusolverDnSsyevd(handle, jobz, uplo, N, d_A, N, h_W, h_work, lwork, devInfo);\n\n // Copy eigenvalues from device to host\n cudaMemcpy(h_W, d_A, N * sizeof(float), cudaMemcpyDeviceToHost);\n\n // Print eigenvalues\n printMatrix(h_W, N, 1);\n\n // Clean up\n cudaFree(d_A);\n cudaFree(h_work);\n cusolverDnDestroy(handle);\n free(h_A);\n free(h_W);\n free(devInfo);\n\n return 0;\n}\n\n该示例代码使用 cuSolver 库来进行特征值计算。首先，我们生成一个随机的 NN 矩阵，并将其复制到 CUDA 设备内存中。然后，我们使用 cuSolver 库的函数来计算特征值。最后，我们将特征值从设备内存复制回主机内存，并打印出来。\n\n请注意，上述代码只是一个简单的示例，可能需要根据你的具体需求进行修改和扩展。