[aerosayan]

Hello everyone,

I was reading opensource codes, and observed that some devs use MPI+OpenMP for their solvers. I understand the reasoning, but currently I'm finding OpenMP code to be much more problematic. Essentially, apart from increasing the complexity of the solver, OpenMP loops apparently have a high startup cost.

For me, surprisingly MPI seems like a better option.

I haven't written implicit solvers with MPI, so I'm little bit inexperienced in that regards, but it seems like distributing mesh partitions on multiple processors, and letting the code run on each processor in a single threaded fashion seems like a very simple way to do things.

I like that our code running on each processor can be single threaded for most part, and only needs to transfer boundary information between neighbors.

I like it because, writing our linear solvers, and other things become easy.

OpenMP parallelizing even a Gaussian elimination solver is somewhat complicated, and has not been fruitful for me.

I like OpenMP for SIMD vectorization though. #pragma omp simd for example, instructs the compiler to generate optimized SIMD code. Similarly, I'm interested in it's graph based task execution model. Professor Jack Dongarra is apparently using this for finding the most efficient pathways through things like QR decomposition solvers, or Cholesky factorization solvers, etc...

What is your view on MPI vs MPI+OpenMP?

Thanks and regards
~Sayan