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我構建了我自己的小Opencl示例，使用不同的網絡資源。實際的內核工作，我得到我想要的輸出，但我在其中一個例子中發現的清理函數會導致段錯誤。我做錯了什麼？OpenCl清理導致段錯誤
#include <stdio.h>
#include <stdlib.h>
#include <errno.h>
#include <CL/cl.h> //opencl
#define CL_CHECK(_expr) \
do { \
cl_int _err = _expr; \
if (_err == CL_SUCCESS) \
break; \
fprintf(stderr, "OpenCL Error: '%s' returned %d!\n", #_expr, (int)_err); \
abort(); \
} while (0)
#define CL_CHECK_ERR(_expr) \
({ \
cl_int _err = CL_INVALID_VALUE; \
typeof(_expr) _ret = _expr; \
if (_err != CL_SUCCESS) { \
fprintf(stderr, "OpenCL Error: '%s' returned %d!\n", #_expr, (int)_err); \
abort(); \
} \
_ret; \
})
const char* OpenCLSource[] = {
"__kernel void VectorAdd(__global int* c, __global int* a,__global int* b)",
"{",
" // Index of the elements to add \n",
" unsigned int n = get_global_id(0);",
" // Sum the n’th element of vectors a and b and store in c \n",
" c[n] = a[n] + b[n];",
"}"
};
cl_device_id* init_opencl(cl_context *GPUContext,cl_command_queue *GPUCommandQueue, cl_kernel* cl_forward1,cl_program* OpenCLProgram){
// Create a context to run OpenCL on our CUDA-enabled NVIDIA GPU
cl_int _err;
*GPUContext = clCreateContextFromType(0, CL_DEVICE_TYPE_GPU, NULL, NULL, &_err) ;
printf("\n1-%i\n",_err);
// Get the list of GPU devices associated with this context
size_t ParmDataBytes;
CL_CHECK(clGetContextInfo(*GPUContext, CL_CONTEXT_DEVICES, 0, NULL, &ParmDataBytes));
cl_device_id* GPUDevices;
GPUDevices = (cl_device_id*)malloc(ParmDataBytes);
CL_CHECK(clGetContextInfo(*GPUContext, CL_CONTEXT_DEVICES, ParmDataBytes, GPUDevices, NULL));
// Create a command-queue on the first GPU device
*GPUCommandQueue = clCreateCommandQueue(*GPUContext, GPUDevices[0], 0, &_err);
printf("\n2-%i\n",_err);
// Create OpenCL program with source code
*OpenCLProgram = clCreateProgramWithSource(*GPUContext, 7, OpenCLSource, NULL, &_err);
printf("\n3-%i\n",_err);
CL_CHECK(clBuildProgram(*OpenCLProgram, 0,
NULL, NULL, NULL, NULL));
cl_int errcode;
*cl_forward1 = clCreateKernel(*OpenCLProgram,
"VectorAdd", &errcode);
printf("\n7-%i\n",errcode);
return GPUDevices;
}
int main(int argc, char** argv)
{
cl_context GPUContext;
cl_command_queue GPUCommandQueue;
cl_program OpenCLProgram;
cl_kernel OpenCLVectorAdd;
cl_device_id* GPUDevices;
GPUDevices=init_opencl(&GPUContext,&GPUCommandQueue,&OpenCLVectorAdd,&OpenCLProgram);
// Two integer source vectors in Host memory
int n=5 ;
int x[5]={1,2,4,6,8};
int y[5]={1,2,4,6,8};
int output[n];
int size_x = n*sizeof(x);
int size_y = n*sizeof(y);
int size_output = n*sizeof(output); // this changes for the second forward1
cl_int _err;
// Allocate GPU memory for source vectors AND initialize from CPU memory
cl_mem x_cl = clCreateBuffer(GPUContext, CL_MEM_READ_ONLY |
CL_MEM_COPY_HOST_PTR, size_x, x, &_err);
printf("\n4-%i\n",_err);
cl_mem y_cl = clCreateBuffer(GPUContext, CL_MEM_READ_ONLY |
CL_MEM_COPY_HOST_PTR, size_y, y, &_err);
printf("\n5-%i\n",_err);
// Allocate output memory on GPU
cl_mem total_cl = clCreateBuffer(GPUContext, CL_MEM_WRITE_ONLY,
size_output, NULL, &_err);
printf("\n6-%i\n",_err);
// In the next step we associate the GPU memory with the Kernel arguments
clSetKernelArg(OpenCLVectorAdd, 0, sizeof(cl_mem),(void*)&total_cl);
clSetKernelArg(OpenCLVectorAdd, 1, sizeof(cl_mem), (void*)&x_cl);
clSetKernelArg(OpenCLVectorAdd, 2, sizeof(cl_mem), (void*)&y_cl);
// 7. Launch OpenCL kernel
size_t localWorkSize[1], globalWorkSize[1];
//localWorkSize = ;
globalWorkSize[0] = n;
// Launch the Kernel on the GPU
CL_CHECK(clEnqueueNDRangeKernel(GPUCommandQueue, OpenCLVectorAdd, 1, NULL, globalWorkSize, NULL, 0, NULL, NULL));
// Copy the output in GPU memory back to CPU memory
//float* h_C = (float*) malloc(size_output);
CL_CHECK(clEnqueueReadBuffer(GPUCommandQueue,
total_cl, CL_TRUE, 0, size_output,
output, 0, NULL, NULL));
for (int i=0; i<n;i++){
printf("\n%i",output[i]);
}
// Cleanup (each of the following lines causes a seg fault
// ******************************
CL_CHECK(free(GPUDevices));
CL_CHECK(clReleaseKernel(OpenCLVectorAdd));
CL_CHECK(clReleaseProgram(OpenCLProgram));
CL_CHECK(clReleaseCommandQueue(GPUCommandQueue));
CL_CHECK(clReleaseContext(GPUContext));
CL_CHECK(clReleaseMemObject(total_cl));
CL_CHECK(clReleaseMemObject(x_cl));
CL_CHECK(clReleaseMemObject(y_cl));
/* ****************
return 0;
}
謝謝！
什麼確切的線路崩潰？ – 2010-09-16 14:27:54
您是否應該在讀取輸出數組之前等待排隊的讀命令完成？ – 2010-09-16 17:11:06
對於這個問題，在入隊的內核調用之後是不是應該等待呢？ – 2010-09-16 17:12:11