reduction

Is there a way to know the order of execution for a reduction operator in OpenMP? In other words, I would like to know how the threads execute reduction operation- is it left to right? What happens when there are numbers that are not power of 2? I think you'll find that OpenMP will only reduce on associative operations, such as + and * (or addition and multiplication if you prefer) which means that it can proceed oblivious to the order of evaluation of the component parts of the reduction expression across threads. I strongly suggest that you proceed in the same way when using OpenMP, trying

In following reduction + map operations, no. 3 is puzzling me. Can anyone please explain why // 1 [1,2,3,4,5].filter(x => x==3).reduce((x, y) => y) // -> 3, all good // 2 [1,2,3,4,5].filter(x => x<=3).reduce((x, y) => 0) // -> 0, still good // 3 [1,2,3,4,5].filter(x => x==3).reduce((x, y) => 0) // -> 3, hello? In other words: how come the reduction on the array of one element ignores the map to 0 operation? This would ultimately be used on an array of objects, as in .reduce((x,y) => y.attr) which also returns y instead of y.attr for single element arrays. The filtered array contains only one

I am trying to implement an OpenCL version for doing reduction of a array of float. To achieve it, I took the following code snippet found on the web : __kernel void sumGPU ( __global const double *input, __global double *partialSums, __local double *localSums) { uint local_id = get_local_id(0); uint group_size = get_local_size(0); // Copy from global memory to local memory localSums[local_id] = input[get_global_id(0)]; // Loop for computing localSums for (uint stride = group_size/2; stride>0; stride /=2) { // Waiting for each 2x2 addition into given workgroup barrier(CLK_LOCAL_MEM_FENCE); //

I used AMD's two-stage reduction example to compute the sum of all numbers from 0 to 65 536 using floating point precision. Unfortunately, the result is not correct. However, when I modify my code, so that I compute the sum of 65 536 smaller numbers (for example 1), the result is correct. I couldn't find any error in the code. Is it possible that I am getting wrong results, because of the float type? If this is the case, what is the best approach to solve the issue? There is probably no error in the coding of your kernel or host application. The issue is with the single-precision floating

I'm implementing a function to find the sum of an array by using reduction, my array have 32*32 elements and its values is 0 ... 1023. The my expected sum value is 523776, but my reult is 15872, it wrong. Here is my code: #include <stdio.h> #include <cuda.h> #define w 32 #define h 32 #define N w*h __global__ void reduce(int *g_idata, int *g_odata); void fill_array (int *a, int n); int main( void ) { int a[N], b[N]; // copies of a, b, c int *dev_a, *dev_b; // device copies of a, b, c int size = N * sizeof( int ); // we need space for 512 integers // allocate device copies of a, b, c cudaMalloc(

I'm writing a CUDA kernel which involves calculating the maximum value on a given matrix and I'm evaluating possibilities. The best way I could find is: Forcing every thread to store a value in the shared memory and using a reduction algorithm after that to determine the maximum (pro: minimum divergence cons: shared memory is limited to 48Kb on 2.0 devices) I couldn't use atomic operations because there are both a reading and a writing operation, so threads could not be synchronized by synchthreads. Any other idea come into your mind? This is the usual way to perform reductions in CUDA Within

I would like to apply a reduce on this piece of my kernel code (1 dimensional data): __local float sum = 0; int i; for(i = 0; i < length; i++) sum += //some operation depending on i here; Instead of having just 1 thread that performs this operation, I would like to have n threads (with n = length) and at the end having 1 thread to make the total sum. In pseudo code, I would like to able to write something like this: int i = get_global_id(0); __local float sum = 0; sum += //some operation depending on i here; barrier(CLK_LOCAL_MEM_FENCE); if(i == 0) res = sum; Is there a way? I have a race