我正在测试一些场景，其中与为dgetrf编写函数相比，与cuBLAS/cuSOLVER一起使用时返回函数LAPACK的方式有所不同。例如，我正在研究以下矩阵的LU分解：
2.0 4.0 1.0 -3.0 0.0
-1.0 -2.0 2.0 4.0 0.0
4.0 2.0 -3.0 5.0 0.0
5.0 -4.0 -3.0 1.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0
我首先尝试按照以下步骤从dgetrf调用cuBLAS/cuSOLVER（警告，前面的丑陋测试代码！）
#include <cblas.h>
#include <time.h>
#include <stdio.h>
#include <string.h>
#include <cuda_runtime.h>
#include <cublas_v2.h>
#include <cusolverDn.h>
int main(int argc, char** argv){
const int matrixSize = 5;
int i, j;
double arrA[matrixSize][matrixSize] = {
{2.0, 4.0, 1.0, -3.0, 0.0},
{-1.0, -2.0, 2.0, 4.0, 0.0},
{4.0, 2.0, -3.0, 5.0, 0.0},
{5.0, -4.0, -3.0, 1.0, 0.0},
{0.0, 0.0, 0.0, 0.0, 0.0}
};
double *arrADev, *workArray;
double **matrixArray;
int *pivotArray;
int *infoArray;
double flat[matrixSize*matrixSize] = {0};
cublasHandle_t cublasHandle;
cublasStatus_t cublasStatus;
cudaError_t error;
cudaError cudaStatus;
cusolverStatus_t cusolverStatus;
cusolverDnHandle_t cusolverHandle;
double *matrices[2];
error = cudaMalloc(&arrADev, sizeof(double) * matrixSize*matrixSize);
if (error != cudaSuccess) fprintf(stderr,"\nError: %s\n",cudaGetErrorString(error));
error = cudaMalloc(&matrixArray, sizeof(double*) * 2);
if (error != cudaSuccess) fprintf(stderr,"\nError: %s\n",cudaGetErrorString(error));
error = cudaMalloc(&pivotArray, sizeof(int) * matrixSize*matrixSize);
if (error != cudaSuccess) fprintf(stderr,"\nError: %s\n",cudaGetErrorString(error));
error = cudaMalloc(&infoArray, sizeof(int) * matrixSize*matrixSize);
if (error != cudaSuccess) fprintf(stderr,"\nError: %s\n",cudaGetErrorString(error));
cublasStatus = cublasCreate(&cublasHandle);
if (cublasStatus != CUBLAS_STATUS_SUCCESS) fprintf(stderr,"error %i\n",cublasStatus);
//maps matrix to flat vector
for(i=0; i<matrixSize; i++){
for(j=0; j<matrixSize; j++){
flat[i+j*matrixSize] = arrA[i][j];
}
}
//copy matrix A to device
cublasStatus = cublasSetMatrix(matrixSize, matrixSize, sizeof(double), flat, matrixSize, arrADev, matrixSize);
if (cublasStatus != CUBLAS_STATUS_SUCCESS) fprintf(stderr,"error %i\n",cublasStatus);
//save matrix address
matrices[0] = arrADev;
//copy matrices references to device
error = cudaMemcpy(matrixArray, matrices, sizeof(double*)*1, cudaMemcpyHostToDevice);
if (error != cudaSuccess) fprintf(stderr,"\nError: %s\n",cudaGetErrorString(error));
int Lwork;
// calculate buffer size for cuSOLVER LU factorization
cusolverStatus = cusolverDnDgetrf_bufferSize(cusolverHandle, matrixSize, matrixSize, arrADev, matrixSize, &Lwork);
cudaStatus = cudaMalloc((void**)&workArray, Lwork*sizeof(double));
// cuBLAS LU factorization
cublasStatus = cublasDgetrfBatched(cublasHandle, matrixSize, matrixArray, matrixSize, pivotArray, infoArray, 1);
if (cublasStatus == CUBLAS_STATUS_SUCCESS)
printf("cuBLAS DGETRF SUCCESSFUL! \n");
else
printf("cuBLAS DGETRF UNSUCCESSFUL! \n");
// cuSOLVER LU factorization
cusolverStatus = cusolverDnCreate(&cusolverHandle);
cusolverStatus = cusolverDnDgetrf(cusolverHandle, matrixSize, matrixSize, arrADev, matrixSize, workArray, pivotArray, infoArray);
if (cusolverStatus == CUSOLVER_STATUS_SUCCESS)
printf("cuSOLVER DGETRF SUCCESSFUL! \n");
else
printf("cuSOLVER DGETRF UNSUCCESSFUL! \n");
return 0;
}
上面代码的输出是
cuBLAS DGETRF SUCCESSFUL!
cuSOLVER DGETRF SUCCESSFUL!
当我尝试对LAPACK进行相同操作时（警告：更难看的代码！）：
#include <iostream>
#include <vector>
using namespace std;
extern "C" void dgetrf_(int* dim1, int* dim2, double* a, int* lda, int* ipiv, int* info);
extern "C" void dgetrs_(char *TRANS, int *N, int *NRHS, double *A, int *LDA, int *IPIV, double *B, int *LDB, int *INFO );
int main()
{
char trans = 'N';
int dim = 5;
int LDA = dim;
int info;
vector<double> a,b;
a.push_back(2.0); a.push_back(4.0); a.push_back(1.0); a.push_back(-3.0); a.push_back(0.0);
a.push_back(-1.0); a.push_back(-2.0); a.push_back(2.0); a.push_back(4.0); a.push_back(0.0);
a.push_back(4.0); a.push_back(2.0); a.push_back(-3.0); a.push_back(5.0); a.push_back(0.0);
a.push_back(5.0); a.push_back(-4.0); a.push_back(-3.0); a.push_back(1.0); a.push_back(0.0);
a.push_back(0.0); a.push_back(0.0); a.push_back(0.0); a.push_back(0.0); a.push_back(0.0);
int ipiv[5];
dgetrf_(&dim, &dim, &*a.begin(), &LDA, ipiv, &info);
if (info == 0)
printf("dgetrf successful\n");
else
printf("dgetrf unsuccessful\n");
return 0;
}
我得到的输出是
dgetrf unsuccessful
我知道它们是不同的库，但是这种行为是预期的吗？
答案 0 :(得分：2)
当我编译您的CUDA代码时，在设置其值之前，我收到一条警告，指示正在使用cusolver句柄。您不应忽略此类警告，因为您在大小调整功能中的用法不正确。但是，这不是问题所在。
我认为您的两个测试用例之间没有任何区别。您似乎错误地解释了结果。
从netlib documentation看，我们看到info的值为5意味着U(5,5)为零，这对于将来的使用会造成问题。这并不意味着在打印时dgetrf因式分解是成功还是失败，而是意味着您输入的数据有所变化。实际上，文档中已明确指出，分解已经完成。
同样，仅通过查看cusolver函数的函数返回值，就无法获得有关该条件的任何信息。为了发现类似于lapack报告的信息，其necessary to look at the infoArray values。
有了这些更改，您的代码报告的是同一件事（信息值为5）：
$ cat t1556.cu
#include <time.h>
#include <stdio.h>
#include <string.h>
#include <cuda_runtime.h>
#include <cublas_v2.h>
#include <cusolverDn.h>
int main(int argc, char** argv){
const int matrixSize = 5;
int i, j;
double arrA[matrixSize][matrixSize] = {
{2.0, 4.0, 1.0, -3.0, 0.0},
{-1.0, -2.0, 2.0, 4.0, 0.0},
{4.0, 2.0, -3.0, 5.0, 0.0},
{5.0, -4.0, -3.0, 1.0, 0.0},
{0.0, 0.0, 0.0, 0.0, 0.0}
};
double *arrADev, *workArray;
double **matrixArray;
int *pivotArray;
int *infoArray;
double flat[matrixSize*matrixSize] = {0};
cublasHandle_t cublasHandle;
cublasStatus_t cublasStatus;
cudaError_t error;
cudaError cudaStatus;
cusolverStatus_t cusolverStatus;
cusolverDnHandle_t cusolverHandle;
double *matrices[2];
error = cudaMalloc(&arrADev, sizeof(double) * matrixSize*matrixSize);
if (error != cudaSuccess) fprintf(stderr,"\nError: %s\n",cudaGetErrorString(error));
error = cudaMalloc(&matrixArray, sizeof(double*) * 2);
if (error != cudaSuccess) fprintf(stderr,"\nError: %s\n",cudaGetErrorString(error));
error = cudaMalloc(&pivotArray, sizeof(int) * matrixSize*matrixSize);
if (error != cudaSuccess) fprintf(stderr,"\nError: %s\n",cudaGetErrorString(error));
error = cudaMalloc(&infoArray, sizeof(int) * matrixSize*matrixSize);
if (error != cudaSuccess) fprintf(stderr,"\nError: %s\n",cudaGetErrorString(error));
cublasStatus = cublasCreate(&cublasHandle);
if (cublasStatus != CUBLAS_STATUS_SUCCESS) fprintf(stderr,"error %i\n",cublasStatus);
//maps matrix to flat vector
for(i=0; i<matrixSize; i++){
for(j=0; j<matrixSize; j++){
flat[i+j*matrixSize] = arrA[i][j];
}
}
//copy matrix A to device
cublasStatus = cublasSetMatrix(matrixSize, matrixSize, sizeof(double), flat, matrixSize, arrADev, matrixSize);
if (cublasStatus != CUBLAS_STATUS_SUCCESS) fprintf(stderr,"error %i\n",cublasStatus);
//save matrix address
matrices[0] = arrADev;
//copy matrices references to device
error = cudaMemcpy(matrixArray, matrices, sizeof(double*)*1, cudaMemcpyHostToDevice);
if (error != cudaSuccess) fprintf(stderr,"\nError: %s\n",cudaGetErrorString(error));
int Lwork;
// calculate buffer size for cuSOLVER LU factorization
cusolverStatus = cusolverDnCreate(&cusolverHandle);
cusolverStatus = cusolverDnDgetrf_bufferSize(cusolverHandle, matrixSize, matrixSize, arrADev, matrixSize, &Lwork);
cudaStatus = cudaMalloc((void**)&workArray, Lwork*sizeof(double));
// cuBLAS LU factorization
cublasStatus = cublasDgetrfBatched(cublasHandle, matrixSize, matrixArray, matrixSize, pivotArray, infoArray, 1);
if (cublasStatus == CUBLAS_STATUS_SUCCESS)
printf("cuBLAS DGETRF SUCCESSFUL! \n");
else
printf("cuBLAS DGETRF UNSUCCESSFUL! \n");
// cuSOLVER LU factorization
cusolverStatus = cusolverDnDgetrf(cusolverHandle, matrixSize, matrixSize, arrADev, matrixSize, workArray, pivotArray, infoArray);
if (cusolverStatus == CUSOLVER_STATUS_SUCCESS)
printf("cuSOLVER DGETRF SUCCESSFUL! \n");
else
printf("cuSOLVER DGETRF UNSUCCESSFUL! \n");
int *hinfoArray = (int *)malloc(matrixSize*matrixSize*sizeof(int));
cudaMemcpy(hinfoArray, infoArray, matrixSize*matrixSize*sizeof(int), cudaMemcpyDeviceToHost);
for (int i = 0; i < matrixSize*matrixSize; i++) printf("%d,", hinfoArray[i]);
printf("\n");
return 0;
}
$ nvcc -o t1556 t1556.cu -lcublas -lcusolver
t1556.cu(30): warning: variable "cudaStatus" was set but never used
$ ./t1556
cuBLAS DGETRF SUCCESSFUL!
cuSOLVER DGETRF SUCCESSFUL!
5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
$ cat t1557.cpp
#include <iostream>
#include <vector>
#include <lapacke/lapacke.h>
using namespace std;
// extern "C" void dgetrf_(int* dim1, int* dim2, double* a, int* lda, int* ipiv, int* info);
// extern "C" void dgetrs_(char *TRANS, int *N, int *NRHS, double *A, int *LDA, int *IPIV, double *B, int *LDB, int *INFO );
int main()
{
char trans = 'N';
int dim = 5;
int LDA = dim;
int info;
vector<double> a,b;
a.push_back(2.0); a.push_back(4.0); a.push_back(1.0); a.push_back(-3.0); a.push_back(0.0);
a.push_back(-1.0); a.push_back(-2.0); a.push_back(2.0); a.push_back(4.0); a.push_back(0.0);
a.push_back(4.0); a.push_back(2.0); a.push_back(-3.0); a.push_back(5.0); a.push_back(0.0);
a.push_back(5.0); a.push_back(-4.0); a.push_back(-3.0); a.push_back(1.0); a.push_back(0.0);
a.push_back(0.0); a.push_back(0.0); a.push_back(0.0); a.push_back(0.0); a.push_back(0.0);
int ipiv[5];
LAPACK_dgetrf(&dim, &dim, &*a.begin(), &LDA, ipiv, &info);
printf("info = %d\n", info);
if (info == 0)
printf("dgetrf successful\n");
else
printf("dgetrf unsuccessful\n");
return 0;
}
$ g++ t1557.cpp -o t1557 -llapack
$ ./t1557
info = 5
dgetrf unsuccessful
$
我正在使用centOS安装的lapack。
centOS 7，CUDA 10.1.243，Tesla V100。