openmp

问题 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

问题 Heluuu, I have a rather large program that I'm attempting to thread. So far, this has been succesful, and the basics are all working as intended. I now want to do some fancy work with cascading threads in nested mode. Essentially, I want the main parallel region to use any free threads in lower parallel regions. To detail the current system, the main parallel region starts 10 threads. I have 12 cores, so I can use 2 more threads. There is a second parallel region where some heavy computing

问题 I am cross compiling for a system with an OpenMP parallelized program, but when I run on the target, I get the error: can't load library 'libgomp.so.1' After looking around, I see that it's an OpenMP runtime library. Is there any was to statically link the library it on the compiler host machine, or does it need to be present on the target machine? If it can be statically linked, then what makes a runtime library different from a dynamic library? Could one statically or dynamically link any

问题 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. 回答1: 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

问题 This code is slower with OpenMP. Without OpenMP I get about 10s. With OpenMP i get about 40s. What is happening? Thank you very much friends! for (i=2;i<(nnoib-2);++i){ #pragma omp parallel for for (j=2; j<(nnojb-2); ++j) { C[i][j]= absi[i]*absj[j]* (2.0f*B[i][j] + absi[i]*absj[j]* (VEL[i][j]*VEL[i][j]*fat* (16.0f*(B[i][j-1]+B[i][j+1]+B[i-1][j]+B[i+1][j]) -1.0f*(B[i][j-2]+B[i][j+2]+B[i-2][j]+B[i+2][j]) -60.0f*B[i][j] )-A[i][j])); c2 = (abs(C[i][j]) > Amax[i][j]); if (c2) { Amax[i][j] = abs(C

问题 I am trying to use OpenMP for threading as it is cross platform. However I can't work out how to make the code after the parallel continue while the loop is running? It basically just executes the first loop in parallel but never gets to the second non parallel loop? int main() { #pragma omp parallel while(1) { Sleep(4000); printf("doing work in thread %d, nthreads %d\n", omp_get_thread_num(), omp_get_num_threads()); } while (1) { Sleep(4000); printf("Hello from main %d, nthreads %d\n", omp

问题 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

问题 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

问题 just playing around with openmp. Look at this code fragments: #pragma omp parallel { for( i =0;i<n;i++) { doing something } } and for( i =0;i<n;i++) { #pragma omp parallel { doing something } } Why is the first one a lot more slower (around the factor 5) than the second one? From theory I thought that the first one must be faster, because the parallel region is only created once and not n-times like the second? Can someone explain this to me? The code i want to parallelise has the following

问题 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,