openmp | 易学教程

pragma omp parallel for vs. pragma omp parallel

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问题 In C++ with openMP, is there any difference between #pragma omp parallel for for(int i=0; i<N; i++) { ... } and #pragma omp parallel for(int i=0; i<N; i++) { ... } ? Thanks! 回答1: #pragma omp parallel for(int i=0; i<N; i++) { ... } This code creates a parallel region, and each individual thread executes what is in your loop. In other words, you do the complete loop N times, instead of N threads splitting up the loop and completing all iterations just once. You can do: #pragma omp parallel {

OpenMP tasks in Visual Studio

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问题 I am trying to learn OMP library task based programming and as an example I copied and pasted the code below taken from a book and it outputs errors 'task' : expected an OpenMP directive name and 'taskwait' : expected an OpenMP directive name I can run omp parallel for loops but not tasks. Do you know whether omp tasking needs any further adjustments in visual studio? #include "stdafx.h" #include <omp.h> int fib(int n) { int i, j; if (n<2) return n; else { #pragma omp task shared(i)

A parallel algorithm for order-preserving selection from an index table

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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() std::vector<T> result; for(auto key:keys) if(key<data.size()) result.push_back(data[key]); return result;

Openblas, OpenMP, and R is there a decent test?

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I am trying to setup a multithreaded R with Openblas and OpenMP. I am using OpenSuSE 12.2 with an AMD fx-8230 8-core processor. After fighting awhile with ATLAS it was suggested that I bag it and try openblas, which I have. First. There was some reports of opensuse 12.2 gcc having a broken openmp, so I figured I should test it. I went to http://openmp.org/wp/openmp-compilers/ and compiled and executed the example file hello.c with all threads responding. Second. I set up a git clone of openblas. I read the instructions and executed 'make USE_OPENMP=1' followed by 'make PREFIX=/usr/lib64

f2py: using openMP parallel in fortran fails

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I am trying to compile a fortran routine that uses openMP for python using f2py . This is the file bsp.f90 : module OTmod !$ use omp_lib implicit none public :: get_threads contains function get_threads() result(nt) integer :: nt nt = 0 !$ nt = omp_get_max_threads() !$omp parallel num_threads(nt) write( *, * ) 'hello world!' !$omp end parallel end function get_threads end module OTmod If I compile it with f2py -m testmod --fcompiler=gfortran --f90flags='-fopenmp' -lgomp -c bsp.f90 compilation works, but importing it to python fails with the error ImportError: dlopen(/Users/USER/omp_py/testmod

OpenMP offloaded target region executed in both host and target-device

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I'm working on a project which requires OpenMP offloading to Nvidia GPUs using Clang. I was able to install Clang to support offloading by following instructions mentioned here . System specification OS - Ubuntu 16.04 LTS Clang -version 4.00 Processor - Intel(R) Core(TM) i7 -4700MQ CPU Cuda -version - 9.0 Nvidia GPU - GeForce 740M (sm_capability - 35) But the problem is I when I execute a sample program to test OpenMP to Nvidia GPUs, part of the target region tends to run in GPU and then same target region starts executing in the host. Please find the sample program here, This a small C

Is there a difference between nested parallelism and collapsed for loops?

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I know that enabling nested parallelism will allow for a nested omp parallel for loop to also be parallelized. But I use collapse(2) in my nested for loops (for inside of for) instead. Is there a difference? Why or why not? Assume the best case scenario: no dependence between the loop indices and other things equal. Yes there is a huge difference - use collapse (not collapsed ). Do not use nested parallelism. Nested parallelism means that there are independent teams of threads working on the different levels of worksharing. You can run into all sorts of trouble either with oversubscribing CPU

Efficient parallelisation of a linear algebraic function in C++ OpenMP

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问题 I have little experience with parallel programming and was wondering if anyone could have a quick glance at a bit of code I've written and see, if there are any obvious ways I can improve the efficiency of the computation. The difficulty arises due to the fact that I have multiple matrix operations of unequal dimensionality that I need to compute, so I'm not sure the most condensed way of coding the computation. Below is my code. Note this code DOES work. The matrices I am working with are of

How to do an ordered reduction in OpenMP

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OpenMP 4.5+ provides the capability to do vector/array reductions in C++ ( press release ) Using said capability allows us to write, e.g.: #include <vector> #include <iostream> int main(){ std::vector<int> vec; #pragma omp declare reduction (merge : std::vector<int> : omp_out.insert(omp_out.end(), omp_in.begin(), omp_in.end())) #pragma omp parallel for default(none) schedule(static) reduction(merge: vec) for(int i=0;i<100;i++) vec.push_back(i); for(const auto x: vec) std::cout<<x<<"\n"; return 0; } The problem is, upon executing such code, the results of the various threads may be ordered in

Sorting an array in openmp

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I have an array of 100 elements that needs to be sorted with insertion sort using OpenMP. When I parallelize my sort it does not give correct values. Can some one help me void insertionSort(int a[]) { int i, j, k; #pragma omp parallel for private(i) for(i = 0; i < 100; i++) { k = a[i]; for (j = i; j > 0 && a[j-1] > k; j--) #pragma omp critical a[j] = a[j-1]; a[j] = k; } } Variables "j" and "k" need to be private on the parallel region. Otherwise you have a data race condition. Alexey Kukanov Unless it's a homework, sorting as few as 100 elements in parallel makes no sense: the overhead