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)
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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.
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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!
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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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