openmp

Reasons for omp_set_num_threads(1) slower than no openmp

由 a 夏天 提交于 2019-12-02 14:05:36
问题 I believe everyone agree with the title of this post. Can someone point me the reason ? Any reference to that like book etc ? I have tried to find but no luck. I believe the reason is something about openmp has a synchronization overhead that no openmp project doesn't have. Hope someone can expand the reason more? Thanks 回答1: While there is some overhead at runtime from using OpenMP even with only one thread, the more important issue is likely to be that the code transformations that the

Reduction with OpenMP: linear merging or log(number of threads) merging

由 旧城冷巷雨未停 提交于 2019-12-02 14:03:23
问题 I have a general question about reductions with OpenMP that's bothered me for a while. My question is in regards to merging the partial sums in a reduction. It can either be done linearly or as the log of the number of threads. Let's assume I want to do a reduction of some function double foo(int i) . With OpenMP I could do it like this. double sum = 0.0; #pragma omp parallel for reduction (+:sum) for(int i=0; i<n; i++) { sum += f(i); } However, I claim that the following code will be just as

Order of execution in Reduction Operation in OpenMP

由 喜夏-厌秋 提交于 2019-12-02 12:59:50
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? 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 reduction expression across threads. I strongly suggest that you proceed in the same way when using OpenMP, trying

increase number of threads decrease time

由 烂漫一生 提交于 2019-12-02 10:18:29
I'm newbie in openmp. Beginning with a tutorial from the official page of openmp https://www.youtube.com/playlist?list=PLLX-Q6B8xqZ8n8bwjGdzBJ25X2utwnoEG In that page there is a hello world program to calculate pi by an approximation of integral. I simply wrote the code below following the instructions but the time-speed of it increase as I increase the number of threads changing the NUM_THREADS. In the video, the speed goes down. I'm executing the program in a remote server with 64 cpus having 8 cores each. #include <stdio.h> #include <omp.h> static long num_steps = 100000; double step;

Not sure how to explain some of the performance results of my parallelized matrix multiplication code

由 自古美人都是妖i 提交于 2019-12-02 09:56:12
问题 I'm running this code in OpenMP for matrix multiplication and I measured its results: #pragma omp for schedule(static) for (int j = 0; j < COLUMNS; j++) for (int k = 0; k < COLUMNS; k++) for (int i = 0; i < ROWS; i++) matrix_r[i][j] += matrix_a[i][k] * matrix_b[k][j]; There are different versions of the code based on where i put the #pragma omp directive - before the j loop, k loop, or the i loop. Also, for every one of those variants I ran different versions for default static scheduling,

Rcpp causes segfault RcppArmadillo does not

由 故事扮演 提交于 2019-12-02 09:50:37
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 of all posts I have read so far is, R and Rcpp are not thread safe, don't call them from within an omp

Why does C++ array creation cause segmentation fault?

由 ℡╲_俬逩灬. 提交于 2019-12-02 09:36:52
I have a program that needs an array of set<vector<bool>> . For the small value of array size, the program works well. When the program runs into large array size, it exits with exit code -1073741571. So, I debug the code and find when it occurs. Below is the simplest code that reproduces my error. #include <iostream> #include <cmath> #include <omp.h> #include <set> #include <vector> using namespace std; int main() { set<vector<bool>> C[43309]; } Values smaller than 43309 cause no error. I try debugging and it shows Thread 1 received signal SIGSEGV, Segmentation fault. 0x00007fff0d17ca99 in

OpenMP outer loop private or shared

由 情到浓时终转凉″ 提交于 2019-12-02 06:23:16
问题 I have a question about OpenMP. Does it make any difference whether I declare i in the outer loop as private or shared? int i,j; #pragma omp parallel for private(j) for (i=0; i<n; i++) { for(j=0; j<n; j++) { //do something } } 回答1: i should be defined as private in your code. As each thread will run a proportion of the i loop, each thread should keep a private i so that the loop body knows which iteration he is in. However a better way is to define variables when you use them, so that you don

Does OpenMP move stack or data variables to the heap if they are shared?

由 独自空忆成欢 提交于 2019-12-02 06:20:34
问题 I'm watching this Introduction to OpenMP series of videos, and the presenter keeps repeating that "heap is shared, stack is private". It is also mentioned that data and text areas are shared. However he gives examples where stack variables of the parent thread are obviously shared and he keeps referring to those variables as being "on the heap". Here's an example: https://youtu.be/dlrbD0mMMcQ?t=2m57s He claims that variables index and count are "on the heap". Isn't index on the stack of the

OpenMP - Why does the number of comparisons decrease?

由 江枫思渺然 提交于 2019-12-02 06:19:50
I have the following algorithm: int hostMatch(long *comparisons) { int i = -1; int lastI = textLength-patternLength; *comparisons=0; #pragma omp parallel for schedule(static, 1) num_threads(1) for (int k = 0; k <= lastI; k++) { int j; for (j = 0; j < patternLength; j++) { (*comparisons)++; if (textData[k+j] != patternData[j]) { j = patternLength+1; //break } } if (j == patternLength && k > i) i = k; } return i; } When changing num_threads I get the following results for number of comparisons: 01 = 9949051000 02 = 4992868032 04 = 2504446034 08 = 1268943748 16 = 776868269 32 = 449834474 64 =
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