openmp

问题 Here is the Black (Black Scholes less the dividend) option pricing model for options on futures written in Cython with actual multi-threading, but I can't run it. (NOW FIXED, SEE LATER POST BELOW FOR ANSWER). I am using Python 3.5 with Microsoft Visual Studio 2015 compiler. Here is the serial version that takes 3.5s for 10M options: Cython program is slower than plain Python (10M options 3.5s vs 3.25s Black Scholes) - what am I missing? I attempted to make this parallel by using nogil but

问题 I have a clarification question. It is my understanding, that sourceCpp automatically passes on the RNG state, so that set.seed(123) gives me reproducible random numbers when calling Rcpp code. When compiling a package, I have to add a set RNG statement. Now how does this all work with openMP either in sourceCpp or within a package? Consider the following Rcpp code #include <Rcpp.h> #include <omp.h> // [[Rcpp::depends("RcppArmadillo")]] // [[Rcpp::export]] Rcpp::NumericVector rnormrcpp1(int n

问题 Is it possible to assign a specific thread to a specific core in opeMP. If so, can anyone tell me how to do that. I am using openMp in fortran language 回答1: For Intel Fortran, recent version(s) of the compiler are supposed to support this - details here. Version : Intel® C++ and Fortran Compilers for Windows* (versions 11.1.048 or higher) Intel® C++ and Fortran Compilers for Linux* (versions 11.1.056 or higher) 来源： https://stackoverflow.com/questions/6428891/openmp-assigning-specific-thread

问题 my question is like this one. but i'd like to do something different... for instance, inside my parallel region i'd like to run my code on 4 threads. when each thread enters the for loop, i'd like to run my code on 8 threads. something like #pramga omp parallel num_threads(4) { //do something on 4 threads #pragma omp parallel for num_threads(2) for(int i=0;i<2;i++){ //do something on 8 threads in total } } so, is there a way to "split" each (4) running threads into two (new) threads so inside

问题 Background I am relying on OpenMP parallelization and pseudo-random number generation in my program but at the same I would like to make the results to be perfectly replicable if desired (provided the same number of threads). I'm seeding a thread_local PRNG for each thread separately like this, { std::minstd_rand master{}; #pragma omp parallel for ordered for(int j = 0; j < omp_get_num_threads(); j++) #pragma omp ordered global::tl_rng.seed(master()); } and I've come up with the following way

问题 If we look at the Visual C++ documentation of omp_set_dynamic , it is literally copy-pasted from the OMP 2.0 standard (section 3.1.7 on page 39): If [the function argument] evaluates to a nonzero value, the number of threads that are used for executing upcoming parallel regions may be adjusted automatically by the run-time environment to best use system resources. As a consequence, the number of threads specified by the user is the maximum thread count. The number of threads in the team

问题 If I create a bunch of OpenMP tasks and do not use taskwait , where does the program wait for that tasks completion? Consider the following example: #pragma omp parallel { #pragma omp single { for (int i = 0; i < 1000; i++) { #pragma omp task ... // e.g., call some independent function } // no taskwait here } // all the tasks completed now? } Does the program wait for task completion at the implicit barrier at the end of the single block? I assume so, but cannot find any information about

问题 I am running the following OpenMP code #pragma omp parallel shared(S2,nthreads,chunk) private(a,b,tid) { tid = omp_get_thread_num(); if (tid == 0) { nthreads = omp_get_num_threads(); printf("\nNumber of threads = %d\n", nthreads); } #pragma omp for schedule(dynamic,chunk) reduction(+:S2) for(a=0;a<NREC;a++){ for(b=0;b<NLIG;b++){ S2=S2+cos(1+sin(atan(sin(sqrt(a*2+b*5)+cos(a)+sqrt(b))))); } } // end for a } /* end of parallel section */ And for NREC=NLIG=1024 and higher values, in a 8 core

问题 Following this answer, I actually have more complicated code with three loops: !$omp parallel !$omp do do i=1,4 ! can be parallelized ... do k=1,1000 !to be executed sequentially ... do j=1,4 ! can be parallelized call job(i,j) The outer loops finish quickly except for i=4 . So I want to start threads on the innermost loop, but leaving the k -loop sequentially within each i -iteration. In fact, k loops over the changing states of a random number generator, so this cannot be parallelized. How

问题 Quick question...I have the following code: void testingOMP() { #pragma omp parallel for for(int i=0;i<5;i++) { #pragma omp single cout << "During single: " <<omp_get_thread_num() << endl; cout << "After single: " << omp_get_thread_num() << endl; } } which hangs, giving the following output: During single: 1 After single: 1 After single: After single: 2During single: 0 1 I had to ctrl+c to stop it. The single work sharing directive assures that only one thread runs the code block having a