openmp

问题 How I calculate the time in each thread ? the CPU_time not work in this case , because If the process is multithreaded, the CPU time is the sum for all threads. Pseudocode example: PROGRAM MAIN implicit none REAL Times_thread1_Started,Times_thread2_Started,.... REAL Times_thread1_finiched !$OMP PARALLEL !$OMP DO !for each thread do : call CPU_TIME_thread1(Times_thread1_Started) call CPU_TIME_thread2(Times_thread2_Started) .......... .......... !$OMP END DO ...................... .............

问题 I am writing a Fortran program that needs to have reproducible results (for publication). My understanding of the following code is that it should be reproducible. program main implicit none real(8) :: ybest,xbest,x,y integer :: i ybest = huge(0d0) !$omp parallel do ordered private(x,y) shared(ybest,xbest) schedule(static,1) do i = 1,10 !$omp ordered !$omp critical call random_number(x) !$omp end critical !$omp end ordered ! Do a lot of work call sleep(1) y = -1d0 !$omp ordered !$omp critical

问题 In order to make some speed comparisons between Cython with SIMD intrinsics (AVX) VS Numpy methods (which from what i know, also provides vectorizing), i have build this simple sum function: import time import numpy as np cimport numpy as np cimport cython cdef extern from 'immintrin.h': ctypedef double __m256d __m256d __cdecl _mm256_load_pd(const double *to_load) nogil void __cdecl _mm256_store_pd(double *to_store, __m256d __M) nogil __m256d __cdecl _mm256_add_pd(__m256d __M1, __m256d __M2)

问题 SOLVED: see EDIT 2 below I am trying to parallelise an algorithm which does some operation on a matrix (lets call it blurring for simplicity sake). Once this operation has been done, it finds the biggest change between the old and new matrix (max of absolute difference between old and new matrix on a per element basis). If this maximum difference is above some threshold, then do another iteration of the matrix operation. So my main program has the following loop: converged = 0; for( i = 1; i

问题 I am attempting to multithread some code with OMP. Currently my sequentially version using rand() to generate a set of random numbers with a consistent seed so that they return the same results when run each time. I want to parallelise my code but rand() is not thread safe. Can someone please show me how i would go about using a random number generator that works on threads so i can produce the same data set upon each test similar to that of using a seed with rand(). My code im parallelising

问题 I am working with OpenMP in order to obtain an algorithm with a near-linear speedup. Unfortunately I noticed that I could not get the desired speedup. So, in order to understand the error in my code, I wrote another code, an easy one, just to double-check that the speedup was in principle obtainable on my hardware. This is the toy example i wrote: #include <omp.h> #include <cmath> #include <stdio.h> #include <stdlib.h> #include <time.h> #include <string.h> #include <cstdlib> #include <fstream

问题 I'm trying to parallelize parts of a project that relies on a lot of recursive algorithms. Most of them are some form of binary tree creation or traversal and processing. I'm stuck using GCC v. 4.1.2 on RedHat and the VC++ compiler on Windows (both don't support OpenMP 3.0 with its convenient task construct). I found this question which seems to get the job done with nested parallel sections and some throttling to prevent an exorbitant number of threads. My question: any way to avoid this

问题 I have the following code that I want to parallelize: int ncip( int dim, double R) { int i; int r = (int)floor(R); if (dim == 1) { return 1 + 2*r; } int n = ncip(dim-1, R); // last coord 0 #pragma omp parallel for for(i=1; i<=r; ++i) { n += 2*ncip(dim-1, sqrt(R*R - i*i) ); // last coord +- i } return n; } The program execution time when ran without openmp is 6.956s when I try and parallelize the for loop my execution time is greater than 3 minutes (and that's because I ended it myself). What

问题 There are many responses on other posts related to the issue of stack space, OpenMP, and how to deal with it. However, I could not find information to truly understand why OpenMP adjusts the compiler options: What is the reasoning behind why -fopenmp in gfortran implies -frecursive ? The documentation says: Allow indirect recursion by forcing all local arrays to be allocated on the stack However, I don't have the context to understand this. Why would parallelization require indirect recursion

问题 The Intel compiler allows us to vectorize loops via #pragma simd for ( ... ) However, you also have the option to do this with OpenMP 4's directive: #pragma omp simd for ( ... ) Is there any difference between the two? 回答1: For all intents and purposes they should be identical. The difference is that the OpenMP 4.0 #pragma omp simd directive is portable and should work with other compilers that support OpenMP 4.0 as well as Intel's. Furthemore, there are several clauses in the OpenMP version