我正在尝试使用cusolverDnSgesvdCUDA 7.0 的新例程来计算奇异值。完整代码报告如下：
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include<iostream>
#include<stdlib.h>
#include<stdio.h>
#include <cusolverDn.h>
#include <cuda_runtime_api.h>
/***********************/
/* CUDA ERROR CHECKING */
/***********************/
void gpuAssert(cudaError_t code, char *file, int line, bool abort=true)
{
if (code != cudaSuccess)
{
fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
if (abort) { exit(code); }
}
}
void gpuErrchk(cudaError_t ans) { gpuAssert((ans), __FILE__, __LINE__); }
/********/
/* MAIN */
/********/
int main(){
int M = 10;
int N = 10;
// --- Setting the host matrix
float *h_A = (float *)malloc(M * N * sizeof(float));
for(unsigned int i = 0; i < M; i++){
for(unsigned int j = 0; j < N; j++){
h_A[j*M + i] = (i + j) * (i + j);
}
}
// --- Setting the device matrix and moving the host matrix to the device
float *d_A; gpuErrchk(cudaMalloc(&d_A, M * N * sizeof(float)));
gpuErrchk(cudaMemcpy(d_A, h_A, M * N * sizeof(float), cudaMemcpyHostToDevice));
// --- host side SVD results space
float *h_U = (float *)malloc(M * M * sizeof(float));
float *h_V = (float *)malloc(N * N * sizeof(float));
float *h_S = (float *)malloc(N * sizeof(float));
// --- device side SVD workspace and matrices
int work_size = 0;
int *devInfo; gpuErrchk(cudaMalloc(&devInfo, sizeof(int)));
float *d_U; gpuErrchk(cudaMalloc(&d_U, M * M * sizeof(float)));
float *d_V; gpuErrchk(cudaMalloc(&d_V, N * N * sizeof(float)));
float *d_S; gpuErrchk(cudaMalloc(&d_S, N * sizeof(float)));
cusolverStatus_t stat;
// --- CUDA solver initialization
cusolverDnHandle_t solver_handle;
cusolverDnCreate(&solver_handle);
stat = cusolverDnSgesvd_bufferSize(solver_handle, M, N, &work_size);
if(stat != CUSOLVER_STATUS_SUCCESS ) std::cout << "Initialization of cuSolver failed. \N";
float *work; gpuErrchk(cudaMalloc(&work, work_size * sizeof(float)));
//float *rwork; gpuErrchk(cudaMalloc(&rwork, work_size * sizeof(float)));
// --- CUDA SVD execution
//stat = cusolverDnSgesvd(solver_handle, 'A', 'A', M, N, d_A, M, d_S, d_U, M, d_V, N, work, work_size, NULL, devInfo);
stat = cusolverDnSgesvd(solver_handle, 'N', 'N', M, N, d_A, M, d_S, d_U, M, d_V, N, work, work_size, NULL, devInfo);
cudaDeviceSynchronize();
int devInfo_h = 0;
gpuErrchk(cudaMemcpy(&devInfo_h, devInfo, sizeof(int), cudaMemcpyDeviceToHost));
std::cout << "devInfo = " << devInfo_h << "\n";
switch(stat){
case CUSOLVER_STATUS_SUCCESS: std::cout << "SVD computation success\n"; break;
case CUSOLVER_STATUS_NOT_INITIALIZED: std::cout << "Library cuSolver not initialized correctly\n"; break;
case CUSOLVER_STATUS_INVALID_VALUE: std::cout << "Invalid parameters passed\n"; break;
case CUSOLVER_STATUS_INTERNAL_ERROR: std::cout << "Internal operation failed\n"; break;
}
if (devInfo_h == 0 && stat == CUSOLVER_STATUS_SUCCESS) std::cout << "SVD successful\n\n";
// --- Moving the results from device to host
gpuErrchk(cudaMemcpy(h_S, d_S, N * sizeof(float), cudaMemcpyDeviceToHost));
for(int i = 0; i < N; i++) std::cout << "d_S["<<i<<"] = " << h_S[i] << std::endl;
cusolverDnDestroy(solver_handle);
return 0;
}
如果我要求计算完整的 SVD（用jobu = 'A'and注释行jobvt = 'A'），一切正常。如果我只要求计算奇异值（与jobu = 'N'和一致jobvt = 'N'），则cusolverDnSgesvd返回
CUSOLVER_STATUS_INVALID_VALUE
请注意，在这种情况下devInfo = 0，我无法发现无效参数。
另请注意，文档 PDF 缺少有关rwork参数的信息，因此我将其作为虚拟参数处理。