使用OpenCL进行简单的矩阵乘法：
// Multiply two matrices A * B = C
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#include <oclUtils.h>
#define WA 3
#define HA 3
#define WB 3
#define HB 3
#define WC 3
#define HC 3
// Allocates a matrix with random float entries.
void randomInit(float* data, int size)
{
for (int i = 0; i < size; ++i)
data[i] = rand() / (float)RAND_MAX;
}
/////////////////////////////////////////////////////////
// Program main
/////////////////////////////////////////////////////////
int
main(int argc, char** argv)
{
// set seed for rand()
srand(2006);
// 1. allocate host memory for matrices A and B
unsigned int size_A = WA * HA;
unsigned int mem_size_A = sizeof(float) * size_A;
float* h_A = (float*) malloc(mem_size_A);
unsigned int size_B = WB * HB;
unsigned int mem_size_B = sizeof(float) * size_B;
float* h_B = (float*) malloc(mem_size_B);
// 2. initialize host memory
randomInit(h_A, size_A);
randomInit(h_B, size_B);
// 3. print out A and B
printf("\n\nMatrix A\n");
for(int i = 0; i < size_A; i++)
{
printf("%f ", h_A[i]);
if(((i + 1) % WA) == 0)
printf("\n");
}
printf("\n\nMatrix B\n");
for(int i = 0; i < size_B; i++)
{
printf("%f ", h_B[i]);
if(((i + 1) % WB) == 0)
printf("\n");
}
// 4. allocate host memory for the result C
unsigned int size_C = WC * HC;
unsigned int mem_size_C = sizeof(float) * size_C;
float* h_C = (float*) malloc(mem_size_C);
// 5. Initialize OpenCL
// OpenCL specific variables
cl_context clGPUContext;
cl_command_queue clCommandQue;
cl_program clProgram;
cl_kernel clKernel;
size_t dataBytes;
size_t kernelLength;
cl_int errcode;
// OpenCL device memory for matrices
cl_mem d_A;
cl_mem d_B;
cl_mem d_C;
/*****************************************/
/* Initialize OpenCL */
/*****************************************/
clGPUContext = clCreateContextFromType(0,
CL_DEVICE_TYPE_GPU,
NULL, NULL, &errcode);
shrCheckError(errcode, CL_SUCCESS);
// get the list of GPU devices associated
// with context
errcode = clGetContextInfo(clGPUContext,
CL_CONTEXT_DEVICES, 0, NULL,
&dataBytes);
cl_device_id *clDevices = (cl_device_id *)
malloc(dataBytes);
errcode |= clGetContextInfo(clGPUContext,
CL_CONTEXT_DEVICES, dataBytes,
clDevices, NULL);
//shrCheckError(errcode, CL_SUCCESS);
//Create a command-queue
clCommandQue = clCreateCommandQueue(clGPUContext,
clDevices[0], 0, &errcode);
//shrCheckError(errcode, CL_SUCCESS);
// Setup device memory
d_C = clCreateBuffer(clGPUContext,
CL_MEM_READ_WRITE,
mem_size_A, NULL, &errcode);
d_A = clCreateBuffer(clGPUContext,
CL_MEM_READ_WRITE | CL_MEM_COPY_HOST_PTR,
mem_size_A, h_A, &errcode);
d_B = clCreateBuffer(clGPUContext,
CL_MEM_READ_WRITE | CL_MEM_COPY_HOST_PTR,
mem_size_B, h_B, &errcode);
// 6. Load and build OpenCL kernel
char *clMatrixMul = oclLoadProgSource("kernel.cl",
"// My comment\n",
&kernelLength);
//shrCheckError(clMatrixMul != NULL, shrTRUE);
clProgram = clCreateProgramWithSource(clGPUContext,
1, (const char **)&clMatrixMul,
&kernelLength, &errcode);
//shrCheckError(errcode, CL_SUCCESS);
errcode = clBuildProgram(clProgram, 0,
NULL, NULL, NULL, NULL);
//shrCheckError(errcode, CL_SUCCESS);
clKernel = clCreateKernel(clProgram,
"matrixMul", &errcode);
//shrCheckError(errcode, CL_SUCCESS);
// 7. Launch OpenCL kernel
size_t localWorkSize[2], globalWorkSize[2];
int wA = WA;
int wC = WC;
errcode = clSetKernelArg(clKernel, 0,
sizeof(cl_mem), (void *)&d_C);
errcode |= clSetKernelArg(clKernel, 1,
sizeof(cl_mem), (void *)&d_A);
errcode |= clSetKernelArg(clKernel, 2,
sizeof(cl_mem), (void *)&d_B);
errcode |= clSetKernelArg(clKernel, 3,
sizeof(int), (void *)&wA);
errcode |= clSetKernelArg(clKernel, 4,
sizeof(int), (void *)&wC);
//shrCheckError(errcode, CL_SUCCESS);
localWorkSize[0] = 3;
localWorkSize[1] = 3;
globalWorkSize[0] = 3;
globalWorkSize[1] = 3;
errcode = clEnqueueNDRangeKernel(clCommandQue,
clKernel, 2, NULL, globalWorkSize,
localWorkSize, 0, NULL, NULL);
//shrCheckError(errcode, CL_SUCCESS);
// 8. Retrieve result from device
errcode = clEnqueueReadBuffer(clCommandQue,
d_C, CL_TRUE, 0, mem_size_C,
h_C, 0, NULL, NULL);
//shrCheckError(errcode, CL_SUCCESS);
// 9. print out the results
printf("\n\nMatrix C (Results)\n");
for(int i = 0; i < size_C; i++)
{
printf("%f ", h_C[i]);
if(((i + 1) % WC) == 0)
printf("\n");
}
printf("\n");
// 10. clean up memory
free(h_A);
free(h_B);
free(h_C);
clReleaseMemObject(d_A);
clReleaseMemObject(d_C);
clReleaseMemObject(d_B);
free(clDevices);
free(clMatrixMul);
clReleaseContext(clGPUContext);
clReleaseKernel(clKernel);
clReleaseProgram(clProgram);
clReleaseCommandQueue(clCommandQue);
}
在上面的代码中，我不断收到错误：
/ * ** * ** * ** * ** * ** * ** * *** / / 初始化OpenCL / / ** * ** * ** * * * * ** * ** * ** * * / clGPUContext = clCreateContextFromType（0， CL_DEVICE_TYPE_GPU， NULL，NULL和＆amp; errcode）; shrCheckError（错误代码，CL_SUCCESS）;
返回的错误代码是-32，表示：CL_INVALID_PLATFORM“
如何删除此错误？
操作系统：Windows 7,32位，NVIDIA GPU GeForce 610
答案 0 :(得分：3)
Nvidia驱动程序希望您提供非NULL属性指针作为clCreateContextFromType调用的第一个参数。
clCreateContextFromType的Khronos规范指出，如果为properties参数传递NULL，则选择的平台是依赖于实现的。在Nvidia的情况下，如果传递NULL指针，则选择似乎根本没有选择平台。有关详细信息，请参阅clCreateContextFromType。
另一方面，此行为与cl_khr_icd extension中的问题＃3一致，如果您通过ICD使用OpenCL，则该问题适用，并说明：
3: How will the ICD handle a NULL cl_platform_id? RESOLVED: The NULL platform is not supported by the ICD.
要将属性传递给clCreateContextFromType，请首先使用clGetPlatformIDs查询平台。然后使用所需的平台ID构造一个属性数组，并将其传递给clCreateContextFromType。以下行中的内容应与符合C99的编译器一起使用：
// query the number of platforms
cl_uint numPlatforms;
errcode = clGetPlatformIDs(0, NULL, &numPlatforms);
shrCheckError(errcode, CL_SUCCESS);
// now get all the platform IDs
cl_platform_id platforms[numPlatforms];
errcode = clGetPlatformIDs(numPlatforms, platforms, NULL);
shrCheckError(errcode, CL_SUCCESS);
// set platform property - we just pick the first one
cl_context_properties properties[] = {CL_CONTEXT_PLATFORM, (int) platforms[0], 0};
clGPUContext = clCreateContextFromType(properties, CL_DEVICE_TYPE_GPU, NULL, NULL, &errcode);
shrCheckError(errcode, CL_SUCCESS);