openmp

问题 I am developing large dense matrix multiplication code. When I profile the code it sometimes gets about 75% of the peak flops of my four core system and other times gets about 36%. The efficiency does not change between executions of the code. It either starts at 75% and continues with that efficiency or starts at 36% and continues with that efficiency. I have traced the problem down to hyper-threading and the fact that I set the number of threads to four instead of the default eight. When I

问题 In my code I have loop in which I construct and over determined linear system and try to solve it: #pragma omp parallel for for (int i = 0; i < n[0]+1; i++) { for (int j = 0; j < n[1]+1; j++) { for (int k = 0; k < n[2]+1; k++) { arma::mat A(max_points, 2); arma::mat y(max_points, 1); // initialize A and y arma::vec solution = solve(A,y); } } } Sometimes, quite randomly the program hangs or the results in the solution vector are NaN. And if I put do this: arma::vec solution; #pragma omp

问题 I am trying to understand the exact difference between #pragma omp critical and #pragma omp single in OpenMP: Microsoft definitions for these are: Single: Lets you specify that a section of code should be executed on a single thread, not necessarily the master thread. Critical: Specifies that code is only be executed on one thread at a time. So it means that in both, the exact section of code afterwards would be executed by just one thread and other threads will not enter that section e.g. if

问题 I'm currently trying to parallelize an existing hierarchical MCMC sampling scheme. The majority of my (by now sequential) source code is written in RcppArmadillo, so I'd like to stick with this framework for parallelization, too. Before starting with parallelizing my code I have read a couple of blog posts on Rcpp/Openmp. In the majority of these blog posts (e.g. Drew Schmidt, wrathematics) the authors warn about the issue of thread safety, R/Rcpp data structures and Openmp. The bottom line

问题 I'm currently trying to parallelize an existing hierarchical MCMC sampling scheme. The majority of my (by now sequential) source code is written in RcppArmadillo, so I'd like to stick with this framework for parallelization, too. Before starting with parallelizing my code I have read a couple of blog posts on Rcpp/Openmp. In the majority of these blog posts (e.g. Drew Schmidt, wrathematics) the authors warn about the issue of thread safety, R/Rcpp data structures and Openmp. The bottom line

问题 I need to implement reduction operation (for each thread the value should be stored in different array entry). However, it runs slower for more threads. Any suggestions? double local_sum[16];. //Initializations.... #pragma omp parallel for shared(h,n,a) private(x, thread_id) for (i = 1; i < n; i++) { thread_id = omp_get_thread_num(); x = a + i* h; local_sum[thread_id] += f(x); } 回答1: You are experiencing the effects of false sharing. On x86 a single cache line is 64 bytes long and therefore

问题 Is there a way to know the order of execution for a reduction operator in OpenMP? In other words, I would like to know how the threads execute reduction operation- is it left to right? What happens when there are numbers that are not power of 2? 回答1: I think you'll find that OpenMP will only reduce on associative operations, such as + and * (or addition and multiplication if you prefer) which means that it can proceed oblivious to the order of evaluation of the component parts of the

问题 I've got a part of a fortran program consisting of some nested loops which I want to parallelize with OpenMP. integer :: nstates , N, i, dima, dimb, dimc, a_row, b_row, b_col, c_row, row, col double complex, dimension(4,4):: mat double complex, dimension(:), allocatable :: vecin,vecout nstates = 2 N = 24 allocate(vecin(nstates**N), vecout(nstates**N)) vecin = ...some data vecout = 0 mat = reshape([...some data...],[4,4]) dimb=nstates**2 !$OMP PARALLEL DO PRIVATE(dima,dimc,row,col,a_row,b_row

问题 I have implemented knapsack using OpenMP (gcc version 4.6.3) #define MAX(x,y) ((x)>(y) ? (x) : (y)) #define table(i,j) table[(i)*(C+1)+(j)] for(i=1; i<=N; ++i) { #pragma omp parallel for for(j=1; j<=C; ++j) { if(weights[i]>j) { table(i,j) = table(i-1,j); }else { table(i,j) = MAX(profits[i]+table(i-1,j-weights[i]), table(i-1,j)); } } } execution time for the sequential program = 1s execution time for the openmp with 1 thread = 1.7s (overhead = 40%) Used the same compiler optimization flags (

问题 I am using OpenSSL in a multithreaded program in C and having issues. So I wrote a small program to try to narrow down what the problem is. The functions besides the main function were copy pasted from https://github.com/plenluno/openssl/blob/master/openssl/crypto/threads/mttest.c My program is as follows. #include<stdio.h> #include<stdlib.h> #include<stdarg.h> #include <strings.h> #include <string.h> #include <math.h> #include <sys/stat.h> #include <fcntl.h> #include <unistd.h> #include<omp