openmp

I have a number crunching C/C++ application. It is basically a main loop for different data sets. We got access to a 100 node cluster with openmp and mpi available. I would like to speedup the application but I am an absolut newbie for both mpi and openmp. I just wonder what is the easiest one to learn and to debug even if the performance is not the best. I also wonder what is the most adequate for my main loop application. Thanks If your program is just one big loop using OpenMP can be as simple as writing: #pragma omp parallel for OpenMP is only useful for shared memory programming, which

Disclaimer: following example is just an dummy example to quickly understand the problem. If you are thinking about real world problem, think anything dynamic programming. The problem: We have an n*m matrix, and we want to copy elements from previous row as in the following code: for (i = 1; i < n; i++) for (j = 0; j < m; j++) x[i][j] = x[i-1][j]; Approach: Outer loop iterations have to be executed in order, they would be executed sequentially. Inner loop can be parallelized. We would want to minimize overhead of creating and killing threads, so we would want to create team of threads just

I have a #pragma omp parallel for loop inside a class method. Each thread readonly accesses few method local variables, few call private data and a method's parameter. All of them are declared in a shared clause. My questions: Performance wise should not make any difference declare these variables shared or firstprivate . Right? Is the same true if I'm not careful about making variable not sharing the same cache line? If one of the shared variables is a pointer and inside the thread I read a value through it, is there an aliasing problem like in ordinary loops? Tomorrow I will try to profile

Friends, I am trying to learn the openMP paradigm. I used the following code to understand the #omp for pragma. int main(void){ int tid; int i; omp_set_num_threads(5); #pragma omp parallel \ private(tid) { tid=omp_get_thread_num(); printf("tid=%d started ...\n", tid); fflush(stdout); #pragma omp for for(i=1; i<=20; i++){ printf("t%d - i%d \n", omp_get_thread_num(), i); fflush(stdout); } printf("tid=%d work done ...\n", tid); } return 0; } In the above code, there is an implicit barrier at the end of #pragma omp parallel, meaning all the threads 0,1,2,3,4 must reach there before going to the

I'm just learning OpenMP from online tutorials and resources. I want to square a matrix (multiply it with itself) using a parallel for loop. In IBM compiler documentation , I found the requirement that "the iteration variable must be a signed integer." Is this also true in the GCC implementation? Is it specified in the OpenMP standard? If so, is there a reason for this requirement? (It doesn't matter much as the expected dimensions are far smaller than INT_MAX , but it does cost me some casts.) According to OpenMP 3.0 specification: http://www.openmp.org/mp-documents/spec30.pdf , for variable

I have been testing the code at In an OpenMP parallel code, would there be any benefit for memset to be run in parallel? and I'm observing something unexpected. My system is a single socket Xeon E5-1620 which is an Ivy Bridge processor with 4 physical cores and eight hyper-threads. I'm using Ubuntu 14.04 LTS, Linux Kernel 3.13, GCC 4.9.0, and EGLIBC 2.19. I compile with gcc -fopenmp -O3 mem.c When I run the code in the link it defaults to eight threads and gives Touch: 11830.448 MB/s Rewrite: 18133.428 MB/s However, when I bind the threads and set the number of threads to the number of

In the changelog of data.table v1.12.0 I noticed the following: Subsetting, ordering and grouping now use more parallelism I tested if I could speed-up some grouping but without any success. I made several different tests and I always get identical results. Is grouping actually parallelised? Maybe I do not properly use the thread options? As you can see data.table has been compiled with openmp otherwise setDTthread print a message to tell the user that there is no support of openmp . Here a reproducile example of one of my tests. library(data.table) n = 5e6 k = 1e4 DT = data.table(x = runif(n)