cuBLAS 调用后出现 CUDA 未知错误
CUDA unknown error after cuBLAS call
在我当前的项目中,我将 CUDA 与 cublasSgetrfBatched 和 cublasSgetriBatched 一起使用来计算矩阵的逆和 return 结果。然而,虽然我在调用第一个函数时没有收到错误消息,但以下对 cudaDeviceSynchronize 的调用导致错误代码 30:未知错误。
奇怪的是,这似乎只发生在较大尺寸的矩阵 (n ~ 1600+) 上,并且适用于较小的矩阵 (n ~ 1400-)。内存使用量仍然相对较小,所以这似乎不是问题。
任何提示或帮助将不胜感激。
可重现样本
#include <string>
#include <cuda_runtime.h>
#include <cublas_v2.h>
#include <conio.h>
#define CUDA_CALL(res, str) { if (res != cudaSuccess) { printf("CUDA Error : %s : %s %d : ERR %s\n", str, __FILE__, __LINE__, cudaGetErrorName(res)); } }
#define CUBLAS_CALL(res, str) { if (res != CUBLAS_STATUS_SUCCESS) { printf("CUBLAS Error : %s : %s %d : ERR %d\n", str, __FILE__, __LINE__, int(res)); } }
float* d_GetInv(float* L, int n)
{
cublasHandle_t cu_cublasHandle;
CUBLAS_CALL(cublasCreate(&cu_cublasHandle), "Failed to initialize cuBLAS!");
float** adL;
float** adC;
float* dL;
float* dC;
int* dLUPivots;
int* dLUInfo;
size_t szA = n * n * sizeof(float);
CUDA_CALL(cudaMalloc(&adL, sizeof(float*)), "Failed to allocate adL!");
CUDA_CALL(cudaMalloc(&adC, sizeof(float*)), "Failed to allocate adC!");
CUDA_CALL(cudaMalloc(&dL, szA), "Failed to allocate dL!");
CUDA_CALL(cudaMalloc(&dC, szA), "Failed to allocate dC!");
CUDA_CALL(cudaMalloc(&dLUPivots, n * sizeof(int)), "Failed to allocate dLUPivots!");
CUDA_CALL(cudaMalloc(&dLUInfo, sizeof(int)), "Failed to allocate dLUInfo!");
CUDA_CALL(cudaMemcpy(dL, L, szA, cudaMemcpyHostToDevice), "Failed to copy to dL!");
CUDA_CALL(cudaMemcpy(adL, &dL, sizeof(float*), cudaMemcpyHostToDevice), "Failed to copy to adL!");
CUDA_CALL(cudaMemcpy(adC, &dC, sizeof(float*), cudaMemcpyHostToDevice), "Failed to copy to adC!");
CUBLAS_CALL(cublasSgetrfBatched(cu_cublasHandle, n, adL, n, dLUPivots, dLUInfo, 1), "Failed to perform LU decomp operation!");
CUDA_CALL(cudaDeviceSynchronize(), "Failed to synchronize after kernel call!");
CUBLAS_CALL(cublasSgetriBatched(cu_cublasHandle, n, (const float **)adL, n, dLUPivots, adC, n, dLUInfo, 1), "Failed to perform Inverse operation!");
CUDA_CALL(cudaDeviceSynchronize(), "Failed to synchronize after kernel call!");
float* res = (float*)malloc(szA);
CUDA_CALL(cudaMemcpy(res, dC, szA, cudaMemcpyDeviceToHost), "Failed to copy to res!");
CUDA_CALL(cudaFree(adL), "Failed to free adL!");
CUDA_CALL(cudaFree(adC), "Failed to free adC!");
CUDA_CALL(cudaFree(dL), "Failed to free dL!");
CUDA_CALL(cudaFree(dC), "Failed to free dC!");
CUDA_CALL(cudaFree(dLUPivots), "Failed to free dLUPivots!");
CUDA_CALL(cudaFree(dLUInfo), "Failed to free dLUInfo!");
CUBLAS_CALL(cublasDestroy(cu_cublasHandle), "Failed to destroy cuBLAS!");
return res;
}
int main()
{
int n = 1600;
float* L = (float*)malloc(n * n * sizeof(float));
for(int i = 0; i < n * n; i++)
L[i] = ((float)rand()/(float)(RAND_MAX)) * 9.0f;
float* inv = d_GetInv(L, n);
printf("done.");
_getch();
return 0;
}
代码是 运行 并且:
显卡:GTX 780 3GB
CPU:i7-4790S @ 3.20 GHz
问题确实 运行 进入 WDDM 超时。对于遇到类似问题的任何人,只需提高超时限制似乎就可以解决问题。
请参阅下面的 link 了解如何延长限制:http://http.developer.nvidia.com/NsightVisualStudio/2.2/Documentation/UserGuide/HTML/Content/Timeout_Detection_Recovery.htm
在我当前的项目中,我将 CUDA 与 cublasSgetrfBatched 和 cublasSgetriBatched 一起使用来计算矩阵的逆和 return 结果。然而,虽然我在调用第一个函数时没有收到错误消息,但以下对 cudaDeviceSynchronize 的调用导致错误代码 30:未知错误。
奇怪的是,这似乎只发生在较大尺寸的矩阵 (n ~ 1600+) 上,并且适用于较小的矩阵 (n ~ 1400-)。内存使用量仍然相对较小,所以这似乎不是问题。
任何提示或帮助将不胜感激。
可重现样本
#include <string>
#include <cuda_runtime.h>
#include <cublas_v2.h>
#include <conio.h>
#define CUDA_CALL(res, str) { if (res != cudaSuccess) { printf("CUDA Error : %s : %s %d : ERR %s\n", str, __FILE__, __LINE__, cudaGetErrorName(res)); } }
#define CUBLAS_CALL(res, str) { if (res != CUBLAS_STATUS_SUCCESS) { printf("CUBLAS Error : %s : %s %d : ERR %d\n", str, __FILE__, __LINE__, int(res)); } }
float* d_GetInv(float* L, int n)
{
cublasHandle_t cu_cublasHandle;
CUBLAS_CALL(cublasCreate(&cu_cublasHandle), "Failed to initialize cuBLAS!");
float** adL;
float** adC;
float* dL;
float* dC;
int* dLUPivots;
int* dLUInfo;
size_t szA = n * n * sizeof(float);
CUDA_CALL(cudaMalloc(&adL, sizeof(float*)), "Failed to allocate adL!");
CUDA_CALL(cudaMalloc(&adC, sizeof(float*)), "Failed to allocate adC!");
CUDA_CALL(cudaMalloc(&dL, szA), "Failed to allocate dL!");
CUDA_CALL(cudaMalloc(&dC, szA), "Failed to allocate dC!");
CUDA_CALL(cudaMalloc(&dLUPivots, n * sizeof(int)), "Failed to allocate dLUPivots!");
CUDA_CALL(cudaMalloc(&dLUInfo, sizeof(int)), "Failed to allocate dLUInfo!");
CUDA_CALL(cudaMemcpy(dL, L, szA, cudaMemcpyHostToDevice), "Failed to copy to dL!");
CUDA_CALL(cudaMemcpy(adL, &dL, sizeof(float*), cudaMemcpyHostToDevice), "Failed to copy to adL!");
CUDA_CALL(cudaMemcpy(adC, &dC, sizeof(float*), cudaMemcpyHostToDevice), "Failed to copy to adC!");
CUBLAS_CALL(cublasSgetrfBatched(cu_cublasHandle, n, adL, n, dLUPivots, dLUInfo, 1), "Failed to perform LU decomp operation!");
CUDA_CALL(cudaDeviceSynchronize(), "Failed to synchronize after kernel call!");
CUBLAS_CALL(cublasSgetriBatched(cu_cublasHandle, n, (const float **)adL, n, dLUPivots, adC, n, dLUInfo, 1), "Failed to perform Inverse operation!");
CUDA_CALL(cudaDeviceSynchronize(), "Failed to synchronize after kernel call!");
float* res = (float*)malloc(szA);
CUDA_CALL(cudaMemcpy(res, dC, szA, cudaMemcpyDeviceToHost), "Failed to copy to res!");
CUDA_CALL(cudaFree(adL), "Failed to free adL!");
CUDA_CALL(cudaFree(adC), "Failed to free adC!");
CUDA_CALL(cudaFree(dL), "Failed to free dL!");
CUDA_CALL(cudaFree(dC), "Failed to free dC!");
CUDA_CALL(cudaFree(dLUPivots), "Failed to free dLUPivots!");
CUDA_CALL(cudaFree(dLUInfo), "Failed to free dLUInfo!");
CUBLAS_CALL(cublasDestroy(cu_cublasHandle), "Failed to destroy cuBLAS!");
return res;
}
int main()
{
int n = 1600;
float* L = (float*)malloc(n * n * sizeof(float));
for(int i = 0; i < n * n; i++)
L[i] = ((float)rand()/(float)(RAND_MAX)) * 9.0f;
float* inv = d_GetInv(L, n);
printf("done.");
_getch();
return 0;
}
代码是 运行 并且:
显卡:GTX 780 3GB
CPU:i7-4790S @ 3.20 GHz
问题确实 运行 进入 WDDM 超时。对于遇到类似问题的任何人,只需提高超时限制似乎就可以解决问题。
请参阅下面的 link 了解如何延长限制:http://http.developer.nvidia.com/NsightVisualStudio/2.2/Documentation/UserGuide/HTML/Content/Timeout_Detection_Recovery.htm