您好我正试图在7周内从7本书Concurreny Models中提取示例代码。作者使用macbook，而我正在使用带有Windows 10的dell xps。
我的程序崩溃是因为在调用函数timing_event后clEnqueueNDRangeKernel()仍然为空。
cl_event timing_event;
size_t work_units = NUM_ELEMENTS;
clEnqueueNDRangeKernel(queue, kernel, 1, NULL, &work_units,
NULL, 0, NULL,&timing_event);
关于事件参数
的docs状态事件
返回标识此特定内核的事件对象 执行实例。事件对象是唯一的，可用于 稍后识别特定的内核执行实例。如果事件是 NULL，不会为此内核执行实例创建任何事件 因此，应用程序无法查询或 排队等待这个特定的内核执行实例。
sombody可以解释为什么这会发生在我的戴尔而不是作者的macbook上？
答案 0 :(得分：0)
我找到了解决方案。这个问题并非来自clEnqueueNDRangeKernel()早先在clBuildProgram(program, 0, NULL, NULL, NULL, NULL);中发生的问题。我用clGetProgramBuildInfo()检索了构建信息。问题是我的 multiply_arrays.cl 文件不是 utf 8编码
对于每个不熟悉opencl的人。每个opencl函数都返回一个状态整数，该整数映射到特定的错误代码。如果函数确实返回不返回状态代码，则可以传递指向该函数的指针。请参阅下面链接的示例函数。这对调试程序非常有用。
<强>的main.cpp
/***
* Excerpted from "Seven Concurrency Models in Seven Weeks",
* published by The Pragmatic Bookshelf.
* Copyrights apply to this code. It may not be used to create training material,
* courses, books, articles, and the like. Contact us if you are in doubt.
* We make no guarantees that this code is fit for any purpose.
* Visit http://www.pragmaticprogrammer.com/titles/pb7con for more book information.
***/
#ifdef __APPLE__
#include <OpenCL/cl.h>
#include <mach/mach_time.h>
#else
#include <CL/cl.h>
#include <Windows.h>
#endif
#include <stdio.h>
#include<iostream>
#include <inttypes.h>
#include <chrono>
#define NUM_ELEMENTS (100000)
char* read_source(const char* filename) {
FILE *h = fopen(filename, "r");
fseek(h, 0, SEEK_END);
size_t s = ftell(h);
rewind(h);
char* program = (char*)malloc(s + 1);
fread(program, sizeof(char), s, h);
program[s] = '\0';
fclose(h);
return program;
}
void random_fill(cl_float array[], size_t size) {
for (int i = 0; i < size; ++i)
array[i] = (cl_float)rand() / RAND_MAX;
}
int main() {
//Status for Errorhandling
cl_int status;
//Identify Platform
cl_platform_id platform;
clGetPlatformIDs(1, &platform, NULL);
//Get Id of GPU
cl_device_id device;
cl_uint num_devices = 0;
clGetDeviceIDs(platform, CL_DEVICE_TYPE_GPU, 1, &device, &num_devices);
// Create Context
cl_context context = clCreateContext(NULL, 1, &device, NULL, NULL, NULL);
//Use context to create Command Queue
//Que enables us to send commands to the gpu device
cl_command_queue queue = clCreateCommandQueue(context, device, CL_QUEUE_PROFILING_ENABLE, NULL);
//Load Kernel
char* source = read_source("multiply_arrays.cl");
cl_program program = clCreateProgramWithSource(context, 1,
(const char**)&source, NULL, &status);
free(source);
// Build Program
status = clBuildProgram(program, 0, NULL, NULL, NULL, NULL);
size_t len;
char *buffer;
clGetProgramBuildInfo(program, device, CL_PROGRAM_BUILD_LOG, 0, NULL, &len);
buffer = (char *) malloc(len);
clGetProgramBuildInfo(program, device, CL_PROGRAM_BUILD_LOG, len, buffer, NULL);
printf("%s\n", buffer);
//Create Kernel
cl_kernel kernel = clCreateKernel(program, "multiply_arrays", &status);
// Create Arrays with random Numbers
cl_float a[NUM_ELEMENTS], b[NUM_ELEMENTS];
random_fill(a, NUM_ELEMENTS);
random_fill(b, NUM_ELEMENTS);
//uint64_t startGPU = mach_absolute_time();
auto start = std::chrono::high_resolution_clock::now();
//Create Readonly input Buffers with value from a and b
cl_mem inputA = clCreateBuffer(context, CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR,
sizeof(cl_float) * NUM_ELEMENTS, a, NULL);
cl_mem inputB = clCreateBuffer(context, CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR,
sizeof(cl_float) * NUM_ELEMENTS, b, NULL);
//Create Output buffer write Only
cl_mem output = clCreateBuffer(context, CL_MEM_WRITE_ONLY,
sizeof(cl_float) * NUM_ELEMENTS, NULL, NULL);
//set Kernel Arguments
clSetKernelArg(kernel, 0, sizeof(cl_mem), &inputA);
clSetKernelArg(kernel, 1, sizeof(cl_mem), &inputB);
clSetKernelArg(kernel, 2, sizeof(cl_mem), &output);
cl_event timing_event;
size_t work_units = NUM_ELEMENTS;
status = clEnqueueNDRangeKernel(queue, kernel, 1, NULL, &work_units,
NULL, 0, NULL,&timing_event);
cl_float results[NUM_ELEMENTS];
//Calculate Results and copy from output buffer to results
clEnqueueReadBuffer(queue, output, CL_TRUE, 0, sizeof(cl_float) * NUM_ELEMENTS,
results, 0, NULL, NULL);
//uint64_t endGPU = mach_absolute_time();
auto finish = std::chrono::high_resolution_clock::now();
//printf("Total (GPU): %lu ns\n\n", (unsigned long)(endGPU - startGPU));
std::cout << "Total(GPU) :"<< std::chrono::duration_cast<std::chrono::nanoseconds>(finish - start).count() << "ns\n";
cl_ulong starttime;
clGetEventProfilingInfo(timing_event, CL_PROFILING_COMMAND_START,
sizeof(cl_ulong), &starttime, NULL);
cl_ulong endtime;
clGetEventProfilingInfo(timing_event, CL_PROFILING_COMMAND_END,
sizeof(cl_ulong), &endtime, NULL);
printf("Elapsed (GPU): %lu ns\n\n", (unsigned long)(endtime - starttime));
clReleaseEvent(timing_event);
clReleaseMemObject(inputA);
clReleaseMemObject(inputB);
clReleaseMemObject(output);
clReleaseKernel(kernel);
clReleaseProgram(program);
clReleaseCommandQueue(queue);
clReleaseContext(context);
//uint64_t startCPU = mach_absolute_time();
start = std::chrono::high_resolution_clock::now();
for (int i = 0; i < NUM_ELEMENTS; ++i)
results[i] = a[i] * b[i];
//uint64_t endCPU = mach_absolute_time();
finish = std::chrono::high_resolution_clock::now();
//printf("Elapsed (CPU): %lu ns\n\n", (unsigned long)(endCPU - startCPU));
std::cout << "Elapsed (CPU) :" << std::chrono::duration_cast<std::chrono::nanoseconds>(finish - start).count() << "ns\n";
return 0;
}
<强> multiply_arrays.cl
__kernel void multiply_arrays(__global const float* inputA,
__global const float* inputB,
__global float* output) {
int i = get_global_id(0);
output[i] = inputA[i] * inputB[i];
}
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