openmp

My problem is related to the one discussed here: Is there a way that OpenMP can operate on Qt spanwed threads? Upon trying to run my Qt-based program under Mac OS that has an OpenMP clause in a secondary thread, it crashed. After browsing through the web, now I understand that it is caused by a bug in the rather old version (4.2) of gcc supplied by Apple. Then I downloaded the latest 4.6 version of gcc from http://hpc.sourceforge.net and tried to compile the project, but I got the following errors from g++ compiler: unrecognized option ‘-arch’ unrecognized option ‘-Xarch_x86_64’ I learned that

I have a for loop that can be executed using schedule(static) or schedule(dynamic, 10) depending on a condition. Currently, My code is not DRY (Don't repeat yourself) enough and to accommodate the previous functionality it has the following repetition: boolean isDynamic; //can be true or false if(isDynamic){ #pragma omp parallel for num_threads(thread_count) default(shared) private(...) schedule(dynamic, 10) for(...){ //for code inside } }else{ #pragma omp parallel for num_threads(thread_count) default(shared) private(...) schedule(static) for(...){ //SAME for code inside, in fact, this is the

What is the proper way to parallelize a multi-dimensional embarrassingly parallel loop in OpenMP? The number of dimensions is known at compile-time, but which dimensions will be large is not. Any of them may be one, two, or a million. Surely I don't want N omp parallel 's for an N-dimensional loop... Thoughts: The problem is conceptually simple. Only the outermost 'large' loop needs to be parallelized, but the loop dimensions are unknown at compile-time and may change. Will dynamically setting omp_set_num_threads(1) and #pragma omp for schedule(static, huge_number) make certain loop

I need to know the total number of threads that my application has spawned via OpenMP. Unfortunately, the omp_get_num_threads() function does not work here since it only yields the number of threads in the current team. However, my code runs recursively (divide and conquer, basically) and I want to spawn new threads as long as there are still idle processors, but no more. Is there a way to get around the limitations of omp_get_num_threads and get the total number of running threads? If more detail is required, consider the following pseudo-code that models my workflow quite closely: function

I have some code which looks a bit like this: std::random_device rd; #pragma omp parallel { std::mt19937 gen(rd()); #pragma omp for for(int i=0; i < N; i++) { /* Do stuff with random numbers from gen() */ } } I have a few questions: Is std::random_device thread safe? i.e. Is it going to do something unhelpful when several threads call it at once? Is this generally a good idea? Should I be worried about overlapping random number streams? Is there a better way to achieve what I want (independent random number streams in each thread - I'm not too worried about reproducibility at the moment)? In

I have little experience with parallel programming and was wondering if anyone could have a quick glance at a bit of code I've written and see, if there are any obvious ways I can improve the efficiency of the computation. The difficulty arises due to the fact that I have multiple matrix operations of unequal dimensionality that I need to compute, so I'm not sure the most condensed way of coding the computation. Below is my code. Note this code DOES work. The matrices I am working with are of dimension approx 700x700 [see int s below] or 700x30 [int n]. Also, I am using the armadillo library