get_global_id(dim) :CUDA中需要计算线程的id,而在opencl中线程id直接通过这个函数直接获取 get_global_size(dim):线程总数量 get_group_id(dim):dim可以为0,1,2,分别代表CUDA中的blockIdx.x、blockIdx.y、blockIdx.z get_num_groups(dim): get_local_id(dim):dim可以为0,1,2,分别代表CUDA中的thread...
借助上图可以看出,这里的每个工作项对应于输出信号的一个值(每个工作项负责计算一个输出值,结合执行模型来理解)。通过get_global_id获取响应的索引。通过两层嵌套的for循环来遍历mask数组,并与input相应位置的数据相乘,并累加得到输出。注意这里的输入input和mask以及output的数组是线性的,也就是一维数组。所以每次计算...
void simpleMultiply(__global float* outPutC, int widthA, int heightA, int widthB , int heightB , __global float* inputA , __global float* inputB ) { int row = get_global_id(1); int col = get_global_id(0); float sum = 0.0f ; for(int i=0;i<widthA; i++) ...
对get_global_id的理解:如果clEnqueueNDRangeKernel设置的是一维且global_item_size = 9,那么get_global_id(0)返回0到8,对应9个work-group。如果clEnqueueNDRangeKernel设置的是二维且global_item_size[2] = {9, 8},那么get_global_id(0)和get_global_id(1)的结果是(0,j)(1,j)(2,j)(3,j)(4,j)...
size_t gid = get_global_id(0); localBuffer[item_id] = input[gid]; barrier(CLK_LOCAL_MEM_FENCE); if((item_id) ==0) { ints =0; for(inti =0; i <512; i++) s += localBuffer[ i ]; output[get_group_id(0)] = s; ...
__kernel void filter(__global uchar4* inputImage, __global uchar4* outputImage, uint N) { int x = get_global_id(0); int y = get_global_id(1); int width = get_global_size(0); int height = get_global_size(1); int k = (N-1)/2; ...
const int ix = get_global_id(0); const int iy = get_global_id(1); int xc = W/2; int yc = H/2; int xpos = ( ix-xc)*cosTheta - (iy-yc)*sinTheta+xc; int ypos = (ix-xc)*sinTheta + ( iy-yc)*cosTheta+yc;
global_work_offset:这里就是规定上面代码里每个维度上第一个get_global_id()0得到的id,默认为0,例如计算一个一维的长度为255work_size的工作,cl会自动虚拟出255个计算单元,每个单元各自计算0-254位置的数相加,而如果你把他设为3,那么cl会从3开始算,也就是说3-254位置的unit会计算出结果,而0 -2这些unit根...
global_size is 1920x1080 local size is keptNULL.Ihave leftthisto compiler.__kernelvoidexperiment(__read_only image2d_t YIn,__write_only image2d_t YOut){uint4 best_suited=0;uint4 temp=0;int best_sum,ssum;int2 coord_src=(int2)(get_global_id(0),2*get_global_id(1)+1);constsamp...
int tid = get_global_id(0); // OpenCL intrinsic function C[tid] = A[tid] + B[tid]; } Given that OpenCL describes execution in fine-grained work-items and can dispatch vast numbers of work-items on architectures with hardware support for fine-grained threading, it is easy to have ...