如果没有CUBLAS_STATUS_EXECUTION_FAILED（13）输出，我无法运行cublasStrsmBatched（第113行）。为简化起见，所有矩阵值和alpha均为1.0，所有矩阵均为正方形，lda，ldb，m和n相等。 我能够以相同的方式运行cublasSgemmBatched和cublasStrsm，没有错误。 cublasStrsmBatched应该是相同的，但它不是，不适合我。 如果您对此代码中的错误有任何疑问，请告诉我：
#include <stdio.h>
#include <stdlib.h>
#include <cuda_runtime.h>
#include <cublas_v2.h>
cublasHandle_t handle;
void CheckCublasCreate(cublasStatus_t status);
void CheckAllocateHost(void* h_pointer);
void CheckCudaMalloc(cudaError_t d_allocStatus);
void CheckCudaMemcpy( cudaError_t error );
void CheckCublasSetGetMatrix(cublasStatus_t status);
void CheckKernelExecution(cublasStatus_t status);
void CheckCublasDestroy(cublasStatus_t status);
void TestCublasStrsmBatched(int size, int numOfLinSys);
int main()
{
cublasStatus_t status = cublasCreate(&handle);
CheckCublasCreate(status);
/*arguments are size of square matrix
and number of linear systems*/
TestCublasStrsmBatched(2,2);
status = cublasDestroy(handle);
CheckCublasDestroy(status);
}
void TestCublasStrsmBatched(int size, int numOfLinSys)
{
cublasStatus_t status;
cudaError_t error;
float **h_A;
float **d_A;
float **h_B;
float **d_B;
float **hd_A;
float **hd_B;
float *alpha;
const int n = size;
const int m = size;
const int lda=m;
const int ldb=m;
const int matA_numOfElem = m*m;
const int matB_numOfElem = m*n;
int i,j;
h_A = (float **)malloc(numOfLinSys * sizeof(float*));
CheckAllocateHost(h_A);
h_B = (float **)malloc(numOfLinSys * sizeof(float*));
CheckAllocateHost(h_B);
alpha=(float *)malloc(sizeof(float));
*alpha = 1.0;
for (j=0; j<numOfLinSys; j++){
h_A[j] = (float *)malloc(matA_numOfElem * sizeof(float));
CheckAllocateHost(h_A);
for (i=0; i < matA_numOfElem; i++)
h_A[j][i] = 1.0;
h_B[j] = (float *)malloc(matB_numOfElem * sizeof(float));
CheckAllocateHost(h_B);
for (i=0; i < matB_numOfElem; i++)
h_B[j][i] = 1.0;
}
hd_A = (float **)malloc(numOfLinSys * sizeof(float*));
CheckAllocateHost(hd_A);
hd_B = (float **)malloc(numOfLinSys * sizeof(float*));
CheckAllocateHost(hd_B);
for (j=0; j<numOfLinSys; j++){
error = cudaMalloc((void **)&hd_A[j],
matA_numOfElem * sizeof(float));
CheckCudaMalloc(error);
error = cudaMalloc((void **)&hd_B[j],
matB_numOfElem * sizeof(float));
CheckCudaMalloc(error);
status = cublasSetMatrix(m, m, sizeof(float),
h_A[j], lda, hd_A[j], lda);
CheckCublasSetGetMatrix(status);
status = cublasSetMatrix(m, n, sizeof(float),
h_B[j], ldb, hd_B[j], ldb);
CheckCublasSetGetMatrix(status);
}
error = cudaMalloc((void **)&d_A, numOfLinSys * sizeof(float*));
CheckCudaMalloc(error);
error = cudaMalloc((void **)&d_B, numOfLinSys * sizeof(float*));
CheckCudaMalloc(error);
error = cudaMemcpy(d_A, hd_A, numOfLinSys * sizeof(float*),
cudaMemcpyHostToDevice);
CheckCudaMemcpy(error);
error = cudaMemcpy(d_B, hd_B, numOfLinSys * sizeof(float*),
cudaMemcpyHostToDevice);
CheckCudaMemcpy(error);
/*After cublasStrsmBatched call
status changes to CUBLAS_STATUS_EXECUTION_FAILED (13)*/
status = cublasStrsmBatched(handle,
CUBLAS_SIDE_LEFT, CUBLAS_FILL_MODE_LOWER,
CUBLAS_OP_N, CUBLAS_DIAG_NON_UNIT,
m, n, alpha, d_A, lda, d_B, ldb, numOfLinSys);
CheckKernelExecution(status);
}
void CheckCublasCreate( cublasStatus_t status )
{
if (status != CUBLAS_STATUS_SUCCESS){
fprintf(stderr,
"!!!! CUBLAS initialization error \n");
exit(EXIT_FAILURE);
}
}
void CheckAllocateHost( void* h_pointer )
{
if (h_pointer == 0){
fprintf(stderr,
"!!!! host memory allocation error \n");
exit(EXIT_FAILURE);
}
}
void CheckCudaMalloc( cudaError_t error )
{
if (error != cudaSuccess){
fprintf(stderr,
"!!!! device memory allocation error (error code %s)\n",
cudaGetErrorString(error));
exit(EXIT_FAILURE);
}
}
void CheckCudaMemcpy( cudaError_t error )
{
if (error != cudaSuccess){
fprintf(stderr, "!!!! data copy error (error code %s)\n",
cudaGetErrorString(error));
exit(EXIT_FAILURE);
}
}
void CheckCublasSetGetMatrix( cublasStatus_t status )
{
if (status != CUBLAS_STATUS_SUCCESS){
fprintf(stderr, "!!!! device access error \n");
exit(EXIT_FAILURE);
}
}
void CheckKernelExecution( cublasStatus_t status )
{
if (status != CUBLAS_STATUS_SUCCESS){
fprintf(stderr, "!!!! kernel execution error.\n");
exit(EXIT_FAILURE);
}
}
void CheckCublasDestroy( cublasStatus_t status )
{
if (status != CUBLAS_STATUS_SUCCESS){
fprintf(stderr, "!!!! shutdown error \n");
exit(EXIT_FAILURE);
}
}
#include <stdio.h>
#include <stdlib.h>
#include <cuda_runtime.h>
#include <cublas_v2.h>
cublasHandle_t handle;
void CheckCublasCreate(cublasStatus_t status);
void CheckAllocateHost(void* h_pointer);
void CheckCudaMalloc(cudaError_t d_allocStatus);
void CheckCudaMemcpy( cudaError_t error );
void CheckCublasSetGetMatrix(cublasStatus_t status);
void CheckKernelExecution(cublasStatus_t status);
void CheckCublasDestroy(cublasStatus_t status);
void TestCublasStrsmBatched(int size, int numOfLinSys);
int main()
{
cublasStatus_t status = cublasCreate(&handle);
CheckCublasCreate(status);
/*arguments are size of square matrix
and number of linear systems*/
TestCublasStrsmBatched(2,2);
status = cublasDestroy(handle);
CheckCublasDestroy(status);
}
void TestCublasStrsmBatched(int size, int numOfLinSys)
{
cublasStatus_t status;
cudaError_t error;
float **h_A;
float **d_A;
float **h_B;
float **d_B;
float **hd_A;
float **hd_B;
float *alpha;
const int n = size;
const int m = size;
const int lda=m;
const int ldb=m;
const int matA_numOfElem = m*m;
const int matB_numOfElem = m*n;
int i,j;
h_A = (float **)malloc(numOfLinSys * sizeof(float*));
CheckAllocateHost(h_A);
h_B = (float **)malloc(numOfLinSys * sizeof(float*));
CheckAllocateHost(h_B);
alpha=(float *)malloc(sizeof(float));
*alpha = 1.0;
for (j=0; j<numOfLinSys; j++){
h_A[j] = (float *)malloc(matA_numOfElem * sizeof(float));
CheckAllocateHost(h_A);
for (i=0; i < matA_numOfElem; i++)
h_A[j][i] = 1.0;
h_B[j] = (float *)malloc(matB_numOfElem * sizeof(float));
CheckAllocateHost(h_B);
for (i=0; i < matB_numOfElem; i++)
h_B[j][i] = 1.0;
}
hd_A = (float **)malloc(numOfLinSys * sizeof(float*));
CheckAllocateHost(hd_A);
hd_B = (float **)malloc(numOfLinSys * sizeof(float*));
CheckAllocateHost(hd_B);
for (j=0; j<numOfLinSys; j++){
error = cudaMalloc((void **)&hd_A[j],
matA_numOfElem * sizeof(float));
CheckCudaMalloc(error);
error = cudaMalloc((void **)&hd_B[j],
matB_numOfElem * sizeof(float));
CheckCudaMalloc(error);
status = cublasSetMatrix(m, m, sizeof(float),
h_A[j], lda, hd_A[j], lda);
CheckCublasSetGetMatrix(status);
status = cublasSetMatrix(m, n, sizeof(float),
h_B[j], ldb, hd_B[j], ldb);
CheckCublasSetGetMatrix(status);
}
error = cudaMalloc((void **)&d_A, numOfLinSys * sizeof(float*));
CheckCudaMalloc(error);
error = cudaMalloc((void **)&d_B, numOfLinSys * sizeof(float*));
CheckCudaMalloc(error);
error = cudaMemcpy(d_A, hd_A, numOfLinSys * sizeof(float*),
cudaMemcpyHostToDevice);
CheckCudaMemcpy(error);
error = cudaMemcpy(d_B, hd_B, numOfLinSys * sizeof(float*),
cudaMemcpyHostToDevice);
CheckCudaMemcpy(error);
/*After cublasStrsmBatched call
status changes to CUBLAS_STATUS_EXECUTION_FAILED (13)*/
status = cublasStrsmBatched(handle,
CUBLAS_SIDE_LEFT, CUBLAS_FILL_MODE_LOWER,
CUBLAS_OP_N, CUBLAS_DIAG_NON_UNIT,
m, n, alpha, d_A, lda, d_B, ldb, numOfLinSys);
CheckKernelExecution(status);
}
void CheckCublasCreate( cublasStatus_t status )
{
if (status != CUBLAS_STATUS_SUCCESS){
fprintf(stderr,
"!!!! CUBLAS initialization error \n");
exit(EXIT_FAILURE);
}
}
void CheckAllocateHost( void* h_pointer )
{
if (h_pointer == 0){
fprintf(stderr,
"!!!! host memory allocation error \n");
exit(EXIT_FAILURE);
}
}
void CheckCudaMalloc( cudaError_t error )
{
if (error != cudaSuccess){
fprintf(stderr,
"!!!! device memory allocation error (error code %s)\n",
cudaGetErrorString(error));
exit(EXIT_FAILURE);
}
}
void CheckCudaMemcpy( cudaError_t error )
{
if (error != cudaSuccess){
fprintf(stderr, "!!!! data copy error (error code %s)\n",
cudaGetErrorString(error));
exit(EXIT_FAILURE);
}
}
void CheckCublasSetGetMatrix( cublasStatus_t status )
{
if (status != CUBLAS_STATUS_SUCCESS){
fprintf(stderr, "!!!! device access error \n");
exit(EXIT_FAILURE);
}
}
void CheckKernelExecution( cublasStatus_t status )
{
if (status != CUBLAS_STATUS_SUCCESS){
fprintf(stderr, "!!!! kernel execution error.\n");
exit(EXIT_FAILURE);
}
}
void CheckCublasDestroy( cublasStatus_t status )
{
if (status != CUBLAS_STATUS_SUCCESS){
fprintf(stderr, "!!!! shutdown error \n");
exit(EXIT_FAILURE);
}
}
使用Linux，CUDA 5.5，T10和Windows，CUDA 5.5，GTX285
谢谢！
答案 0 :(得分：2)
批量生产的三角形后轮是我以前在CUBLAS中没有尝试过的东西，所以我有兴趣看看可能会发生什么。你的代码相当复杂，所以我没有费心去理解它，但是当我运行它时，它似乎失败了内部CUBLAS启动失败：
$ cuda-memcheck ./a.out
========= CUDA-MEMCHHECK
!!!! kernel execution error.
========= Program hit error 8 on CUDA API call to cudaLaunch
========= Saved host backtrace up to driver entry point at error
========= Host Frame:/Library/Frameworks/CUDA.framework/Versions/A/Libraries/libcuda_256.00.35.dylib (cudbgGetAPIVersion + 0x27bd7) [0x4538e7]
========= Host Frame:/usr/local/cuda/lib/libcudart.dylib (cudaLaunch + 0x26c) [0x45c8c]
========= Host Frame:/usr/local/cuda/lib/libcublas.dylib (cublasZgetrfBatched + 0x1e34) [0x196ae4]
========= Host Frame:/usr/local/cuda/lib/libcublas.dylib (cublasCtrsmBatched + 0x64d) [0x1974cd]
========= Host Frame:/usr/local/cuda/lib/libcublas.dylib (cublasCtrsmBatched + 0xacb) [0x19794b]
========= Host Frame:/Users/talonmies/./a.out (_Z22TestCublasStrsmBatchedii + 0x3c1) [0x1b28]
========= Host Frame:/Users/talonmies/./a.out (main + 0x3d) [0x1b7d]
========= Host Frame:/Users/talonmies/./a.out (start + 0x35) [0x14e9]
========= Host Frame:[0x1]
（这是一台带有计算1.2 GPU和CUDA 5.0的OS X机器）。错误8是cudaErrorInvalidDeviceFunction，通常只有当库或fatbinary没有匹配或不能将JIT重新编译为GPU可以运行的内容的架构时才会出现。
好奇，我从头开始编写了我自己更简单的repro案例：
#include <iostream>
#include <cublas_v2.h>
int main(void)
{
const int Neq = 5, Nrhs = 2, Nsys = 4;
float Atri[Neq][Neq] =
{ { 1, 6, 11, 16, 21},
{ 0, 7, 12, 17, 22},
{ 0, 0, 13, 18, 23},
{ 0, 0, 0, 19, 24},
{ 0, 0, 0, 0, 25} };
float B[Nrhs][Neq] =
{ { 1, 27, 112, 290, 595},
{ 2, 40, 148, 360, 710} };
float *syslhs[Nsys], *sysrhs[Nsys];
float *A_, *B_, **syslhs_, **sysrhs_;
size_t Asz = sizeof(float) * (size_t)(Neq * Neq);
size_t Bsz = sizeof(float) * (size_t)(Neq * Nrhs);
cudaMalloc((void **)(&A_), Asz);
cudaMalloc((void **)(&B_), Bsz * size_t(Nsys));
cudaMemcpy(A_, Atri, Asz, cudaMemcpyHostToDevice);
for(int i=0; i<Nsys; i++) {
syslhs[i] = A_;
sysrhs[i] = (float*)((char *)B_ + i*Bsz);
cudaMemcpy(sysrhs[i], B, Bsz, cudaMemcpyHostToDevice);
}
size_t syssz = sizeof(float *) * (size_t)Nsys;
cudaMalloc((void **)&syslhs_, syssz);
cudaMalloc((void **)&sysrhs_, syssz);
cudaMemcpy(syslhs_, syslhs, syssz, cudaMemcpyHostToDevice);
cudaMemcpy(sysrhs_, sysrhs, syssz, cudaMemcpyHostToDevice);
const cublasSideMode_t side = CUBLAS_SIDE_LEFT;
const cublasDiagType_t diag = CUBLAS_DIAG_NON_UNIT;
const cublasFillMode_t ulo = CUBLAS_FILL_MODE_LOWER;
const cublasOperation_t trans = CUBLAS_OP_N;
float alpha = 1.f;
cublasHandle_t handle;
cublasCreate(&handle);
cublasStrsmBatched(
handle,
side, ulo, trans, diag,
Neq, Nrhs,
&alpha,
syslhs_, Neq,
sysrhs_, Neq,
Nsys
);
for(int k=0; k<Nsys; k++) {
cudaMemcpy(B, sysrhs[k], Bsz, cudaMemcpyDeviceToHost);
for(int i=0; i<Nrhs; i++) {
for(int j=0; j<Neq; j++) {
std::cout << B[i][j] << ",";
}
std::cout << std::endl;
}
std::cout << std::endl;
}
return 0;
}
这也与代码失败的方式相同。在第一次检查时，这确实似乎是一个CUBLAS内部问题，尽管很难说出什么。关于我唯一能想到的是这些解算器仅支持计算能力3.5设备在计算1.x设备上不受支持，但文档未提及它。在我们之间，我们已经测试了计算1.2，计算1.3和计算3.0 [我的错误，我在你的问题中读取了K10而不是T10]设备，所以没有其他的东西......
我所能建议的是尝试使用cuda-memcheck运行代码并查看它是否报告相同的错误。如果确实如此，我会在未来看到NVIDIA的错误报告。
库中显然没有sm_1x cubins，因此我的compute_12设备应该产生我看到的运行时库错误。它还解释了GTX 285和Telsa T10的错误，它们都是compute_13。
EDIT2：
正如所怀疑的那样，我的repro代码在带有compute_30设备的Linux系统上完美运行，在CUDA 5.0和CUDA 5.5版本库下。