openmp

问题 I wish to understand what do they mean here. Why would this program "hang"? From https://bisqwit.iki.fi/story/howto/openmp/ OpenMP and fork() It is worth mentioning that using OpenMP in a program that calls fork() requires special consideration. This problem only affects GCC; ICC is not affected. If your program intends to become a background process using daemonize() or other similar means, you must not use the OpenMP features before the fork. After OpenMP features are utilized, a fork is

问题 Is there a list of functions/methods of OpenCV that have been optimized with IPP and/or TBB and/or OpenMP? 回答1: Disclaimer: I have no experience in OpenCV usage. I found no such a list on the official opencv.org site. However, the ChangeLog says: switched all the remaining parallel loops from TBB-only tbb::parallel_for() to universal cv::parallel_for_() with many possible backends (MS Concurrency, Apple's GDC, OpenMP, Intel TBB etc.) Now, we know what to search and grep -IRl parallel_for_

问题 I use g++ (GCC) 4.7.2. on Windows 7, 64-bit version. downloaded from http://nuwen.net/mingw.html I tried to use the "-fopenmp" flag and got the error: g++: error: libgomp.spec: No such file or directory I can't find the file anywhere on my system. Do I need to re-install something? Can I just throw a file somewhere? 回答1: You could try installing TDM-GCC, which looks as though it includes OpenMP. There's also Sezero's personal build. 回答2: I had a similar problem. I got it working installing

问题 Why won't the intel compiler let me specify that some actions in an openmp parallel for block should be executed by the master thread only? And how can I do what I'm trying to achieve without this kind of functionality? What I'm trying to do is update a progress bar through a callback in a parallel for: long num_items_computed = 0; #pragma omp parallel for schedule (guided) for (...a range of items...) { //update item count #pragma omp atomic num_items_computed++; //update progress bar with

问题 I'm fairly new to OpenMP and I'm trying to start an individual thread to process each item in a 2D array. So essentially, this: for (i = 0; i < dimension; i++) { for (int j = 0; j < dimension; j++) { a[i][j] = b[i][j] + c[i][j]; What I'm doing is this: #pragma omp parallel for shared(a,b,c) private(i,j) reduction(+:diff) schedule(dynamic) for (i = 0; i < dimension; i++) { for (int j = 0; j < dimension; j++) { a[i][j] = b[i][j] + c[i][j]; Does this in fact start a thread for each 2D item or no

问题 I'm looking around OpenMP, partially because my program need to make additions of very large vectors (millions of elements). However i see a quite large difference if i use std::vector or raw array. Which i cannot explain. I insist that the difference is only on the loop, not the initialisation of course. The difference in time I refer to, is only timing the addition, especially not to take into account any initialization difference between vectors, arrays, etc. I'm really talking only about

问题 I'm new here and a beginner level programmer in C. I'm having some problem with using openmp to speedup the for-loop. Below is simple example: #include <stdlib.h> #include <stdio.h> #include <gsl/gsl_rng.h> #include <omp.h> gsl_rng *rng; main() { int i, M=100000000; double tmp; /* initialize RNG */ gsl_rng_env_setup(); rng = gsl_rng_alloc (gsl_rng_taus); gsl_rng_set (rng,(unsigned long int)791526599); // option 1: parallel #pragma omp parallel for default(shared) private( i, tmp ) schedule

问题 I do some scientific computations in C++, and try to utilize OpenMP for the parallelisation of some of the loops. This worked well so far, e.g. on a Intel i7-4770 with 8 threads. Setup We have a small workstation which consists of two Intel CPUs (E5-2680v2) on one mainboard. The code works as long as it runs on 1 CPU with as many threads as I like. But as soon as I employ the second CPU, I observe incorrect results from time to time (around every 50th-100th time I run the code). This happens

问题 I want to use LSF to submit a job which: runs on 4 nodes, in parallel each node has a single mpi process each process has 12 threads In the absence of LSF, I would simply launch with mpi on 4 nodes, like: mpirun -hosts host1,host2,host3,host4 -np 4 ./myprocess --numthreads=12 However, in the presence of LSF, I can't see how to do this? I'm sure there's probably a very standard way to do it, but I'm quite new to LSF. I googled around, but the answer wasn't immediately obvious to me. I found

问题 Order-preserving selection from an index table is trivial in serial code, but in multi-threading is less straightforward, in particular if one wants to retain efficiency (the whole point of multi-threading) by avoiding linked lists. Consider the serial code template<typename T> std::vector<T> select_in_order( std::vector<std::size_t> const&keys, // permutation of 0 ... key.size()-1 std::vector<T> const&data) // anything copyable { // select data[keys[i]] allowing keys.size() >= data.size()