openmp

I am trying to create a simple program that uses Intel's AVX technology and perform vector multiplication and addition. Here I am using Open MP alongside this. But it is getting segmentation fault due to the function call _mm256_store_ps(). I have tried with OpenMP atomic features like atomic, critical, etc so that if this function is atomic in nature and multiple cores are attempting to execute at the same time, but it is not working. #include<stdio.h> #include<time.h> #include<stdlib.h> #include<immintrin.h> #include<omp.h> #define N 64 __m256 multiply_and_add_intel(__m256 a, __m256 b, _

I have a routine that uses a loop to compute the minimum height of a particle given a surface of particles beneath. This routine tries random positions and compute the minimum height and then returns the x, y, z values, where z is the minimum height found. This routine can be parallelized with omp parallel for . But I am having problems figuring out how to get the triplet (x, y, z) , not just the minimum z (because the minimum z of course corresponds to a given x, y coordinates). I can actually get the smallest z by using a reduction operation as follows double x = 0, y = 0, z = 1.0e300; //

I am very new to openMP, but am trying to write a simple program that generates the entries of matrix in parallel, namely for the N by M matrix A, let A(i,j) = i*j. A minimal example is included below: #include <stdio.h> #include <stdlib.h> #include <omp.h> int main(int argc, char **argv) { int i, j, N, M; N = 20; M = 20; int* A; A = (int*) calloc(N*M, sizeof(int)); // compute entries of A in parallel #pragma omp parallel for shared(A) for (i = 0; i < N; ++i){ for (j = 0; j < M; ++j){ A[i*M + j] = i*j; } } // print parallel results for (i = 0; i < N; ++i){ for (j = 0; j < M; ++j){ printf("%d "

I am trying to get a hybrid OpenMP / MPI job to run so that OpenMP threads are separated by core (only one thread per core). I have seen other answers which use numa-ctl and bash scripts to set environment variables, and I don't want to do this. I would like to be able to do this only by setting OMP_NUM_THREADS and or OMP_PROC_BIND and mpiexec options on the command line. I have tried the following - let's say I want 2 MPI processes that each have 2 OpenMP threads, and each of the threads are run on separate cores, so I want 4 cores total. OMP_PROC_BIND=true OMP_PLACES=cores OMP_NUM_THREADS=2

I have tried many things now and have come to some conclusions. Maybe, I oversee something but it seems that I cannot accomplish what I desire. The question is: Is there any possibility to compile C++ on MacOS High Sierra with OpenMP and boost? Some findings (correct me if I am wrong): OpenMP is supported by Clang BUT not by the standard MacOS-clang compiler delivered with MacOS which is ALSO the only compiler XCode9 supports g++ supports OpenMP If I install Boost via homebrew, then it will use the clang compiler (which cannot be changed easily), so that libc++ will be used g++ uses libstdc++

openMP 处理for循环 ////////////////////////////////////////////////////////////////////////////////////////////// template <typename PointInT, typename PointOutT> void pcl::MovingLeastSquares<PointInT, PointOutT>::performProcessing (PointCloudOut &output) { // Compute the number of coefficients nr_coeff_ = (order_ + 1) * (order_ + 2) / 2; size_t mls_result_index = 0; #ifdef _OPENMP // (Maximum) number of threads const unsigned int threads = threads_ == 0 ? 1 : threads_; // Create temporaries for each thread in order to avoid synchronization typename PointCloudOut::CloudVectorType projected_points

I'm building a distance matrix on which each row represent a point and each column is the distance between this point and all the other points in the data and my algorithm works very fine in the sequentially. However, when I try to parallelize it I get segmentation fault error.The following is my code for parallel where dat is a map that contain all my data. Any help here will be highly appreciated. map< int,string >::iterator datIt; map< int,string >::iterator datIt2; map <int, map< int, double> > dist; int mycont=0; datIt=dat.begin(); int size=dat.size(); #pragma omp parallel //construct the