The difference between tasks and sections is in the time frame in which the code will execute. Sections are enclosed within the sections construct and (unless the nowait clause was specified) threads will not leave it until all sections have been executed: [ sections ] Thread 0: ——-< section 1 >—->*—— Thread 1: ——-< section … Read more
OpenMP is a way to program on shared memory devices. This means that the parallelism occurs where every parallel thread has access to all of your data. You can think of it as: parallelism can happen during execution of a specific for loop by splitting up the loop among the different threads. MPI is a … Read more
Others have since answered most of the question but I would like to point to some specific cases where a particular scheduling type is more suited than the others. Schedule controls how loop iterations are divided among threads. Choosing the right schedule can have great impact on the speed of the application. static schedule means … Read more
Pthreads and OpenMP represent two totally different multiprocessing paradigms. Pthreads is a very low-level API for working with threads. Thus, you have extremely fine-grained control over thread management (create/join/etc), mutexes, and so on. It’s fairly bare-bones. On the other hand, OpenMP is much higher level, is more portable and doesn’t limit you to using C. … Read more
. I sent an email to OpenMP developers about this subject, the answer I got: For signed int, the wrap around behavior is undefined. If we allow !=, programmers may get unexpected tripcount. The problem is whether the compiler can generate code to compute a trip count for the loop. For a simple loop, like: … Read more
Cython Cython has OpenMP support: With Cython, OpenMP can be added by using the prange (parallel range) operator and adding the -fopenmp compiler directive to setup.py. When working in a prange stanza, execution is performed in parallel because we disable the global interpreter lock (GIL) by using the with nogil: to specify the block where … Read more
Are you sure your systems are over determined? solve_ud in your stack trace says otherwise. Though you have solve_od too, and probably that’s nothing to do with the issue. But it doesn’t hurt to find why that’s happening and fix it if you think the systems should be od. Is armadillo solve() thread safe? That … Read more
The hardware prefetcher is tuned differently on server vs workstation CPUs. Servers are expected to handle many threads, so the prefetcher will request smaller chunks from RAM. Here is a paper that goes into detail about the issue you’re experiencing, but from the other side of the coin: Hardware Prefetcher Aggressiveness Controllers: Do We Need … Read more
The OpenMP 4.0 specification was finalised and published several days ago here. It still mandates that parallel loops should be in the canonical form (ยง2.6, p.51): for (init-expr; test-expr; incr-expr) structured-block The standard allows for containers that provide random-access iterators to be used in all of the expressions, e.g.: #pragma omp parallel for for (it … Read more
private variables are not initialised, i.e. they start with random values like any other local automatic variable (and they are often implemented using automatic variables on the stack of each thread). Take this simple program as an example: #include <stdio.h> #include <omp.h> int main (void) { int i = 10; #pragma omp parallel private(i) { … Read more