inputget_global_id ( 0 ) ; / Loop for computing localSums.
The shell script run_performances_sumGPU allows to do batch execution in order to produce speedups as a function of input parameters.
M : Figure 1 : SpeedUp GPU vs CPU as a function of array and WorkGroup sizes Best performances gain of OpenCL parallelization is reached for array size higher than 1 Million.
So one gestion concours pétanque has to compute the sum of partialSums array elements : we can do it with CPU or GPU.We have to notice that "Sum Reduction" built-in functions already exist into.Sum Reduction with, openCL-1.x : the goal is to get the summation of all elements of a 1D array.This will be interesting to compare these performances with atomic functions of OpenCL-2.x.Actually, one has to call " cadeau parfait pour maman clEnqueueNDRangeKernel " function in this loop and use after " clSetKernelArg " with the new array as argument.Block 1 might execute the reduce function and write a value to temp21, while Block2 might still be waiting and temp22 still contains some garbage.Dtype) src/p/y/ pycuda download ) sum_a m(a) from duction import get_sum_kernel sum_a_gpu.Did I find the right examples for you?We redo the same operation after dividing by 2 the previous subgroup.For speedup upper to 1, best performances are performed with a Work-Group size equal to 256.The content of global memory is preserved between kernel calls).

OpenCL-2.x will be more high-performing.
So this is here a pedagogical example which can be useful to understand a way of parallelize a sequential code with all OpenCL API functionalities.
Below the results of this evaluation plotted with Matlab script plot_performances_sumGPU.
Nchmark GPU/CPU We accomplish a benchmark between GPU and CPU (sequential) version for different array and Work-Group sizes.Work-Items,.e the total number of calls of Kernel Code, each representing a thread.We will present the.Below figure illustrates the algorithm : Inside a Work-Group, synchronizing all threads is necessary.You can have the reduction as a separate kernel call (as in the original cuda examples but you may decide not to transfer the resulting data back to host.Sources can be donwloaded from this link : To compile, type "make then launch executable " sumReductionGPU " with two arguments (Input array size and Work-Group size).

At last, for a 100 Million array size, we get the best speedup, equal.