On Sean's question, we are working toward expanding models that will require
1.a (a coral reef grid full of agents that is too big or too slow if handled
without parallelization), and possibly 1.b (3D corals, each of which are
each made up of hundreds of polyps). We will be tying into Lattice Boltzmann
and/or smoothed particle hydrodynamic models associated with both scales.
All of the discussions recently of various options have been of interest,
though an extension of Mason that would facilitate this would be of
considerable assistance. 

Cheers!

John McManus  

-----Original Message-----
From: MASON Multiagent Simulation Toolkit
[mailto:[log in to unmask]] On Behalf Of Sean Luke
Sent: Friday, December 07, 2007 8:58 AM
To: [log in to unmask]
Subject: Re: cluster or grid parallelization

There are three kinds of uses of parallelization here that I'd like  
to disambiguate:

0  Parallelizing an experiment within a single machine.

1. Parallelizing a single experiment by spreading it across multiple  
machines that work together.  This  commonly involves one or both of  
the following tasks.

	1.a Distribute the *space* of the world across multiple machines
	    because it's so big in memory
	1.b Distribute the *agents* of the world because their
	    computational cost is too large

2. Using multiple machines to do multiple experiments, one experiment  
per machine.


#0 is trivial.

For #2, shell scripts and rsh do just fine for us; indeed use of PVM/ 
MPI etc., is just way too heavyweight, particularly since Java and  
MPI don't play well together still.  #2 is also very cleanly done on  
grid computing solutions.  If anyone's interested, there's a company  
in my area (Parabon) which negotiates to borrow excess cycles on  
organizations' PCs and then sells this time with a Java-only piece of  
grid computing software.  For a large NASA project they've adapted my  
ECJ code to this system and I'm sure would be interested in tasks you  
might have in MASON.  Give 'em a call (parabon.com).

For #1, I'm very interested in knowing how people are adapting MASON  
(or RePast) to distribute their experiment.  Do you have 1.a as a  
need?  1.b?  How are you going about hacking MASON etc. to do it?

The reason I ask is that one of our grants may push us to look into  
creating a version of MASON which does #1 cleanly.  Note that MASON  
was specifically designed for #2, and indeed *good*, efficient  
architectures are very different between #1 designs and #2 designs.   
So we're going to have to think about what kinds new data structures  
will we need.  Probably at least we'll need a distributed schedule  
sync device, a way for agents to migrate, and several mechanisms for  
distributing space.  It's a lot of overhead.

I'm interested that some have mentioned RePast.  RePast etc. weren't  
designed for #1 *or* #2.  I know people have been hacking  
distribution onto RePast, and am interested in how they overcame the  
issue of the schedule containing single-machine Java events (repaints  
etc.).  It might help inform us when we get to working on this.

Sean