openmp

问题 I have a recursive program which I want to speed up using openMP. The structure is like below. I am not familiar with omp task and just learnt something from here. It seems that I have to wrap buildTree in a omp single region. However, I also want to parallelize the for loop inside buildTree , how can I achieve that? int main() { #pragma omp parallel { #pragma omp single nowait buildTree(); } } void buildTree { if(endRecursion) return; for(int i = 0; i < problemSize; i++) { // I want to

问题 I have a recursive program which I want to speed up using openMP. The structure is like below. I am not familiar with omp task and just learnt something from here. It seems that I have to wrap buildTree in a omp single region. However, I also want to parallelize the for loop inside buildTree , how can I achieve that? int main() { #pragma omp parallel { #pragma omp single nowait buildTree(); } } void buildTree { if(endRecursion) return; for(int i = 0; i < problemSize; i++) { // I want to

问题 I am not sure why OpenMP uses so many threads. It seems to be not related to the Microsoft implementation because I have also tried the Intel library which shows the same behavior. I have some parallel sections in my code which are compute bound and should not create and use more threads than I have cores. But what I have observed is that for n initiating threads OpenMP creates n*Cores threads. This looks like a big thread leak to me. If I execute a "small" 32 bit application running on a

问题 Recently working in parallel domain i come to know that there are two terms "vertical parallelism " and "horizontal parallelism". Some people says openmp ( shared memory parallelism ) as vertical while mpi ( distributed memory parallelism ) as horizontal parallelism. Why these terms are called so ? I am not getting the reason. Is it just terminology to call them so ? 回答1: The terms don't seem to be widely used, perhaps because often time a process or system is using both without distinction.

问题 Do all mutex implementations ultimately call the same basic system/hardware calls - meaning that they can be interchanged? Specifically, if I'm using __gnu_parallel algorithms (that uses openmp ) and I want to make the classes they call threadsafe may I use boost::mutex for the locking? or must I write my own mutex such as the one described here //An openmp mutex. Can this be replaced with boost::mutex? class Mutex { public: Mutex() { omp_init_lock(&_mutex); } ~Mutex() { omp_destroy_lock(&

问题 One way of installing gcc with openMP support on OSX is using Homebrew. However, when I follow the usual instruction of brew reinstall gcc --without-multilib It gives me a warning that there is no formula corresponding to the --without-multilib option and hence this will have no effect. Consequently, I do not have openMP support after this reinstallation process. Here is the detailed terminal output. poulin8:02-prange-parallel-loops poulingroup$ brew --version Homebrew 1.3.6 Homebrew/homebrew

问题 What is the proper way to parallelize a multi-dimensional embarrassingly parallel loop in OpenMP? The number of dimensions is known at compile-time, but which dimensions will be large is not. Any of them may be one, two, or a million. Surely I don't want N omp parallel 's for an N-dimensional loop... Thoughts: The problem is conceptually simple. Only the outermost 'large' loop needs to be parallelized, but the loop dimensions are unknown at compile-time and may change. Will dynamically

问题 I have a loop which updates a matrix A and I want to make it openmp but I'm not sure what variables should be shared and private. I would have thought just ii and jj would work but it doesn't. I think I need an !$OMP ATOMIC UPDATE somewhere too... The loop just calculates the distance between N and N-1 particles and updates a matrix A. !$OMP PARALLEL DO PRIVATE(ii,jj) do ii=1,N-1 do jj=ii+1,N distance_vector=X(ii,:)-X(jj,:) distance2=sum(distance_vector*distance_vector) distance=DSQRT

问题 I have a loop which updates a matrix A and I want to make it openmp but I'm not sure what variables should be shared and private. I would have thought just ii and jj would work but it doesn't. I think I need an !$OMP ATOMIC UPDATE somewhere too... The loop just calculates the distance between N and N-1 particles and updates a matrix A. !$OMP PARALLEL DO PRIVATE(ii,jj) do ii=1,N-1 do jj=ii+1,N distance_vector=X(ii,:)-X(jj,:) distance2=sum(distance_vector*distance_vector) distance=DSQRT

问题 I have a program that needs an array of set<vector<bool>> . For the small value of array size, the program works well. When the program runs into large array size, it exits with exit code -1073741571. So, I debug the code and find when it occurs. Below is the simplest code that reproduces my error. #include <iostream> #include <cmath> #include <omp.h> #include <set> #include <vector> using namespace std; int main() { set<vector<bool>> C[43309]; } Values smaller than 43309 cause no error. I