Parallel computing : matlab on the HPC cluster

I've improved the codes for the parallel computing, which I talked about at the seminar a month ago - it should be really simple to use now :). Also, the problem with the atomic operation, at least under Linux, is solved as well now. I've written up a description of the codes, commented them and compiled two examples: one to be run on a single machine with several copies of Matlab running in parallel and another is for the HPC cluster. Everything can be found here: http://neurotheory.columbia.edu/~max/codes/ParallelComputation.zip

If you have comments, suggestions - will be happy to hear! Will also be glad to help resolving problems, if they arise, or to explain the code, if needed. Also, if you start using the code, please let me know - it's always encouraging to know that the work goes to masses :).

Submit jobs to the HPC cluster from matlab

While we are talking about tools for using the HPC cluster, here's an ad for a tool of my own.

I have been using agricola to submit jobs to the HPC cluster from within matlab.  It is a very simple tool:  Instead of launching a calculation on your local machine by typing in the matlab prompt:

my_result = my_function( some_parameters ) ;

one types:

sow( 'my_result' , @()my_function( some_parameters ) ) ;

This will copy all the .m files in your current directory into a folder on the HPC submit machine, generate a submit file there, and launch the calculation on the cluster. Then some time later, when you suspect the job is done, you type:

reap

which makes the variable  my_result  appear in your matlab workspace.  reap itself returns all the .out, .log, and .err files for you to look at from within matlab.

Unlike Max's code, agricola does not aim to parallelize your code; it just handles sending files back and forth with ssh and job submission.