Let’s say we have an ‘intrinsically parallel’ problem to solve with our Erlang software. We have a lot of parallel processes and each of them executes sequential code (not number crunching) and the more CPUs we throw at them the better.
I have heard about CUDA bindings for Erlang, but after watching the Kevin Smith’s presentation I am not sure that it is the solution: the whole purpose of pteracuda buffer is to assign a difficult number crunching task to the buffer and get the result back. It is not possible to use GPU’s processors to serve Elrang’s processes. (Am I right?).
On the other side multicore CPUs are really expensive (8 cores CPU prices start at $300). So, to build a 10-machine Erlang parallel processing ‘cluster’ you have to spend at least $3000 on CPUs only.
So, the question is:
What kind of affordable CPU or GPU can be used to build a ‘server cluster’ for a parallel Erlang software?
There was a student project at Uppsala University in 2009 called LOPEC that had this aim, in cooperation with Erlang Solutions (then still called Erlang Training & Consulting, or ETC for short).
I couldn’t find any slides from their final project report, but this is a poster they showed at the Erlang User Conference in 2009: http://www.it.uu.se/edu/course/homepage/projektDV/ht09/ETC_poster.pdf
Parts of the code seems to live on here: https://github.com/burbas/LoPECv2 (the user burbas was one of the students), but strangely incomplete. You could ask burbas for more info.
There is of course also the Disco project by Nokia: http://discoproject.org/
In both cases, I think you’ll need to write a C or Python stub to run on the clients to talk to the GPU (or you might run Erlang with CUDA bindings on the clients); the above frameworks just help you distribute the workload and gather results.