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在 CUDA 中,为了覆盖多个块,从而增加数组的索引范围,我们执行以下操作:

主机端代码:

 dim3 dimgrid(9,1)// total 9 blocks will be launched    
 dim3 dimBlock(16,1)// each block is having 16 threads  // total no. of threads in  
                   //   the grid is thus 16 x9= 144.        

设备端代码

 ...
 ...     
 idx=blockIdx.x*blockDim.x+threadIdx.x;// idx will range from 0 to 143 
 a[idx]=a[idx]*a[idx];
 ...
 ...    

OpenCL 中实现上述情况的等价物是什么?

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2 回答 2

4

在主机上,当您使用 将内核排入队列时clEnqueueNDRangeKernel,您必须指定全局和本地工作大小。例如:

size_t global_work_size[1] = { 144 }; // 16 * 9 == 144
size_t local_work_size[1] = { 16 };
clEnqueueNDRangeKernel(cmd_queue, kernel, 1, NULL,
                       global_work_size, local_work_size,
                       0, NULL, NULL);

在您的内核中,使用:

size_t get_global_size(uint dim);
size_t get_global_id(uint dim);
size_t get_local_size(uint dim);
size_t get_local_id(uint dim);

分别检索全局和局部工作大小和索引,其中dim0for x1fory2for z

因此,相当于您的idx遗嘱size_t idx = get_global_id(0);

请参阅OpenCL 参考页

于 2012-05-02T15:25:47.090 回答
1

CUDA 和 OpenCL 之间的等价关系是:

blockIdx.x*blockDim.x+threadIdx.x = get_global_id(0)

LocalSize = blockDim.x

GlobalSize = blockDim.x * gridDim.x
于 2012-05-02T02:37:35.023 回答