Kernels VS ompi

https://github.com/ParRes/Kernels/tree/default/C1z has some examples I’ve tested in the past. 2d is in the filenames of the relevant ones.

I've evaluated all of these against each other. One presentation is https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s41620/ (sorry, you have to register - it's not my preference). The performance numbers there are based on code derived from https://github.com/ParRes/Kernels/tree/default/FORTRAN (the code differences are not interesting). Another comparison is found in https://github.com/jeffhammond/nwchem-tce-triples-kernels, which is more complicated in some ways.

If you want to learn by viewing code side by side, https://github.com/ParRes/Kernels/tree/default/Cxx11 might be useful. I haven’t kept up with my RAJA ports because they kept making breaking changes in the API a few years ago (should be stable now).

Try https://github.com/ParRes/Kernels/tree/default/FORTRAN coarray programs. Those were written by people who know what they’re doing and have been proven to execute correctly before. That might help you understand if your implementation is broken.

https://github.com/ParRes/Kernels has examples of the same thing written in Fortran, MATLAB/Octave and Numpy, if it helps.

You absolutely went to take a look at the Parallel Research Kernels (PRK) repo at https://github.com/ParRes/Kernels .

The commercial Linux world (see https://github.com/open-mpi/ompi/issues/4349) and other open source OSes (eg FreeBSD) seem to have lined up behind little-endian PowerPC. IBM still has a big-endian problem with AIX, IBM i, and Linux on Z.

Roughly, the sets of computational problems that people used (use?) MPI for. Things like numerical solvers for sparse matrices that are so big that you need to split them across your entire cluster. These still require a lot of node-to-node communication, and on top of it, the pattern is dependent on each problem (so easy solutions like map-reduce are effectively out). See eg https://www.open-mpi.org/, and https://courses.csail.mit.edu/18.337/2005/book/Lecture_08-Do... for the prototypical use case.

One of the guys at the local LUG is one of the lead maintainers of Open MPI. He told us about a comment that ran into the hundreds of lines, all for a one-line change in the code.

But it would be very inconvenient to have to keep crediting everyone who's ever worked on it. If you look at old projects, their licenses can have like 10-20 of those lines (here's one I was recently looking into).

I have a bit of experience programming for a highly-parallel supercomputer, specifically in my case an IBM BlueGene/Q. In that case, the answer is a lot of message passing (we used Open MPI [0]). Since the nodes are discrete and don't have any shared memory, you end up with something kinda reminiscent of the actor model as popularized by Erlang and co -- but in C for number-crunching performance.

That said, each of the nodes is itself composed of multiple cores with shared memory. So in cases where you really want to grind out performance, you actually end up using message passing to divvy up chunks of work, and then use classic pthreads to parallelize things further, with lower latency.

Debugging is a bit of a nightmare, though, since some bugs inevitably only come up once you have a large number of nodes running the algorithm in parallel. But you'll probably be in a mainframe-style time-sharing setup, so you may have to wait hours or more to rerun things.

This applies less to some of the newer supercomputers, which are more or less clusters of GPUs instead of clusters of CPUs. I imagine there's some commonality, but I haven't worked with any of them so I can't really say.

[0] https://www.open-mpi.org/

I would suggest using OpenMPI because it's pretty easy to get started with. You can build OpenMPI with CUDA support, then you can pass device pointers directly to MPI_Send and MPI_Recv. Then you don't have to deal with transfers and synchronization issues.

backends from native torch distributed configuration: nccl, gloo, mpi.

I use a linux ubuntu machine with MPI (https://www.open-mpi.org/). I had a question on making my computer simulations faster. Would be better to get an older AMD 9590 machine clocked at 4.7 ghz or continue using my Ryzen 7 1700 machine clocked at something like 3.5ghz?

OpenMPI - Message passing interface implementation. BSD-3-Clause