openmp

问题 Will sched_setaffinity or pthread_attr_setaffinity_np work to set thread affinity under OpenMP? Related: CPU Affinity 回答1: Yes, named calls will work to set thread affinity. The only problem is to fix thread number and to set right affinity in right thread (you can try using static scheduling of for loop for known number of threads). As I know, almost every openmp allows to set affinity via environment. The name of environment variable varies (it was not standartized some time ago). I use

问题 I was trying to compiled the following code: #pragma omp parallel shared (j) { #pragma omp for schedule(dynamic) for(i = 0; i != j; i++) { // do something } } I get this error: error: invalid controlling predicate . I check the openMP reference guide and it says that for the parallel for it "only" allows one of the following operators: < <= > >=. I don't understand why not allow i != j . I could understand if it was the static schedule, since openMP need to pre-compute the number of

问题 I was trying to compiled the following code: #pragma omp parallel shared (j) { #pragma omp for schedule(dynamic) for(i = 0; i != j; i++) { // do something } } I get this error: error: invalid controlling predicate . I check the openMP reference guide and it says that for the parallel for it "only" allows one of the following operators: < <= > >=. I don't understand why not allow i != j . I could understand if it was the static schedule, since openMP need to pre-compute the number of

问题 How can I get OpenMP to run on Mac OSX 10.11, so that I can execute scripts via terminal ? I have installed OpenMP: brew install clang-omp . When I run, for example: gcc -fopenmp -o Parallel.b Parallel.c the following expression returns: fatal error: 'omp.h' file not found I have also tried: brew install gcc --without-multilib but unfortunately this eventually returned the following (after first installing some dependencies): The requested URL returned error: 404 Not Found Error: Failed to

问题 I am running a .cpp code (i) in sequential style and (ii) using OpenMP statements. I am trying to see the time difference. For calculating time, I use this: #include <time.h> ..... main() { clock_t start, finish; start = clock(); . . . finish = clock(); processing time = (double(finish-start)/CLOCKS_PER_SEC); } The time is pretty accurate in sequential (above) run of the code. It takes about 8 seconds to run this. When I insert OpenMP statements in the code and thereafter calculate the time I

问题 As an OpenMP & Rcpp performance test I wanted to check how fast I could calculate the Mandelbrot set in R using the most straightforward and simple Rcpp + OpenMP implementation. Currently what I did was: #include <Rcpp.h> #include <omp.h> // [[Rcpp::plugins(openmp)]] using namespace Rcpp; // [[Rcpp::export]] Rcpp::NumericMatrix mandelRcpp(const double x_min, const double x_max, const double y_min, const double y_max, const int res_x, const int res_y, const int nb_iter) { Rcpp::NumericMatrix

问题 I've looked at the official definitions, but I'm still quite confused. firstprivate : Specifies that each thread should have its own instance of a variable, and that the variable should be initialized with the value of the variable, because it exists before the parallel construct. To me, that sounds a lot like private. I've looked for examples, but I don't seem to understand how it's special or how it can be used. lastprivate : Specifies that the enclosing context's version of the variable is

问题 Given n partial sums it's possible to sum all the partial sums in log2 parallel steps. For example assume there are eight threads with eight partial sums: s0, s1, s2, s3, s4, s5, s6, s7 . This could be reduced in log2(8) = 3 sequential steps like this; thread0 thread1 thread2 thread4 s0 += s1 s2 += s3 s4 += s5 s6 +=s7 s0 += s2 s4 += s6 s0 += s4 I would like to do this with OpenMP but I don't want to use OpenMP's reduction clause. I have come up with a solution but I think a better solution

问题 I have blocks of memory that can be quite large (larger than the L2 cache), and sometimes I must set them to all zero. memset is good in a serial code, but what about parallel code ? Has somebody experience if calling memset from concurrent threads actually speed things up for large arrays ? Or even using simple openmp parallel for loops ? 回答1: People in HPC usually say that one thread is usually not enough to saturate a single memory link, the same usually being true for network links as

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 6 years ago . Based on this thread, OpenMP and STL vector, which data structures are good alternatives for a shared std::vector in a parallel for loop? The main aspect is speed, and the vector might require resizing during the loop. 回答1: The question you link was talking about the fact that "that STL vector container is not