Distributed Evolutionary Computation using REST

P.A. Castillo, M.G. Arenas,A.M. Mora, J.L.J. Laredo, G. Romero,V.M RivasJ.J. Merelo

GeNeura Grouphttp://geneura.wordpress.comhttp://twitter.com/geneura

If you've been paying attention

REST beats other web-services implementations: lighter, more scalable

Let's put it to work

Using a single server, different clients (master/slave implementation)

Picture taken fromhttp://www.flickr.com/photos/joeschram/207822199/in/photostream/

What problem?

Optimization of topology + initial weights for a multilayer perceptron: G-Prop algorithm (Castillo et al., 1999)

Evaluation lengthy and time-consuming

Client/server implementation

Old McREST had a farm

But we will not be using it for this.

Populations running in parallel on clients and use REST server for communication.

Hope to achieve better scaling that way.

Picture from http://www.flickr.com/photos/25653307@N03/2716213900/in/photostream/

Reminder: REST architecture

Send chromosomes to the server

Request chromosomes

Send chromosomes

Get evaluated chromosomes

Uses PerlDancerUses Algorithm::Evolutionary

Evolutionary algorithm parameters

Experimental results

Speedup

nodes

speedup

Not all clients all the same, so no linear speedup should be expected. In any rate, the server is bound to have a threshold over which it should overload itself

REST your parallelization in peace

Just add clients!

Picture from http://www.flickr.com/photos/beauteous/48847025/in/photostream/

Open source your science!

All source, data sets, experiment results for this paper are available from Sourceforge (in fact, they were while we were doing it). Source is also available from the CPAN Perl module server worldwide, in two separate modules: the algorithm itself it's available from the author's web site

Any question?

http://geneura.wordpress.comhttp://twitter.com/geneura

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