Parallelization: pthreads or OpenMP?

Question

Most people in scientific computing use OpenMP as a quasi-standard when it comes to shared memory parallelization.

Is there any reason (other than readability) to use OpenMP over pthreads? The latter seems more basic and I suspect it could be faster and easier to optimize.

Answer 1

It basically boils down to what level of control you want over your parallelization. OpenMP is great if all you want to do is add a few #pragma statements and have a parallel version of your code quite quickly. If you want to do really interesting things with MIMD coding or complex queueing, you can still do all this with OpenMP, but it is probably a lot more straightforward to use threading in that case. OpenMP also has similar advantages in portability in that a lot of compilers for different platforms support it now, as with pthreads.

So you're absolutely correct - if you need fine-tuned control over your parallelization, use pthreads. If you want to parallelize with as little work as possible, use OpenMP.

Whichever way you decide to go, good luck!

Answer 2

One other reason: the OpenMP is task-based, Pthreads is thread based. It means that OpenMP will allocate the same number of threads as number of cores. So you will get scalable solution. It is not so easy task to do it using raw threads.

The second opinion: OpenMP provides reduction features: when you need to compute partial results in threads and combine them. You can implement it just using single line of code. But using raw threads you should do more job.

Just think about your requirements and try to understand: is OpenMP enough for you? You will save lots of time.