openmp

问题 With C++11 I have been using the following pattern for implementing a graph data structure with parallel iterators. Nodes are just indices, edges are entries in an adjacency data structure. For iterating over all nodes, a function (lambda, closure...) is passed to a parallelForNodes method and called with each node as an argument. Iteration details are nicely encapsulated in the method. Now I would like to try the same concept with Cython. Cython provides the cython.parallel.prange function

问题 Given an example function (example is given below), the for loop can either be parallelized using OpenMP or be vectorized using vectorization (assuming that compiler does the vectorization). Example void function(float* a, float* b, float* c, int n) { for(int i = 0; i < n; i++) { c[i] = a[i] * b[i]; } } I would like to know Whether there will be any difference in performance between OpenMP and Vectorization Is there any advantage in using one over the other. Is there any possibility of using

问题 I have a routine that uses a loop to compute the minimum height of a particle given a surface of particles beneath. This routine tries random positions and compute the minimum height and then returns the x, y, z values, where z is the minimum height found. This routine can be parallelized with omp parallel for . But I am having problems figuring out how to get the triplet (x, y, z) , not just the minimum z (because the minimum z of course corresponds to a given x, y coordinates). I can

问题 What schedule algorithm is used when no schedule clause is specified? I.e.: #pragma omp parallel for for (int i = 0; i < n; ++i) Foo(i); 回答1: Start from the documentation that you have linked to. Section 2.7.1.1 Determining the Schedule of a Worksharing Loop reads: If the loop directive does not have a schedule clause then the current value of the def-sched-var ICV determines the schedule. The sentence preceding the quoted one refers to Section 2.3.1 which reads: def-sched-var - controls the

问题 So here's the code: #pragma omp parallel private (myId) { set_affinity(); myId = omp_get_thread_num(); if (myId<myConstant) { #pragma omp for schedule(static,1) for(count = 0; count < AnotherConstant; count++) { //Do stuff, everything runs as it should } } #pragma omp barrier //all threads wait as they should #pragma omp single { //everything in here is executed by one thread as it should be } #pragma omp barrier //this is the barrier in which threads run ahead par_time(cc_time_tot, phi_time

问题 I am learning OPENMP and wrote the following code to solve nqueens problem. //Full Code: https://github.com/Shafaet/Codes/blob/master/OPENMP/Parallel%20N- Queen%20problem.cpp int n; int call(int col,int rowmask,int dia1,int dia2) { if(col==n) { return 1; } int row,ans=0; for(row=0;row<n;row++) { if(!(rowmask & (1<<row)) & !(dia1 & (1<<(row+col))) & !(dia2 & (1<<((row+n-1)-col)))) { ans+=call(col+1,rowmask|1<<row,dia1|(1<<(row+col)), dia2|(1<<((row+n-1)-col))); } } return ans; } double

问题 I do not understand why such code is not vectorized with gcc 4.4.6 int MyFunc(const float *pfTab, float *pfResult, int iSize, int iIndex) { for (int i = 0; i < iSize; i++) pfResult[i] = pfResult[i] + pfTab[iIndex]; } note: not vectorized: unhandled data-ref However, if I write the following code int MyFunc(const float *pfTab, float *pfResult, int iSize, int iIndex) { float fTab = pfTab[iIndex]; for (int i = 0; i < iSize; i++) pfResult[i] = pfResult[i] + fTab; } gcc succeeds auto-vectorize

问题 I have a question about the Microsoft PPL library, and parallel programming in general. I am using FFTW to perform a large set (100,000) of 64 x 64 x 64 FFTs and inverse FFTs. In my current implementation, I use a parallel for loop and allocate the storage arrays within the loop. I have noticed that my CPU usage only tops out at about 60-70% in these cases. (Note this is still better utilization than the built in threaded FFTs provided by FFTW which I have tested). Since I am using fftw

问题 I am using OpenMP and I want to spawn threads such that one thread executes one piece of code and finishes, in parallel with N threads running the iterations of a parallel-for loop. Execution should be like this: Section A (one thread) || Section B (parallel-for, multiple threads) | || | | | | | | | | | | | || | | | | | | | | | | | || | | | | | | | | | | | || | | | | | | | | | | | || | | | | | | | | | | V || V V V V V V V V V V I cannot just write a parallel-for with a #pragma omp once

问题 I am using OpenMP to do multithreading with my nested loops. Since new to this stuff, I am not sure if I am using OpenMP in the correct way so that it can actually do the parallel programming. So I like to know if I can measure the performance of my C++ program that uses OpenMP so I can tell it actually works and I am on the right track? Like how many threads are running in parallel and how long it takes for each of them to finish. Thanks and regards! 回答1: #include <omp.h> ... int target