Distributed Evolutionary Computation using REST P.A. Castillo, M.G. Arenas, A.M. Mora, J.L.J. Laredo, G. Romero,V.M Rivas ...
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)
What problem? <ul><li>Optimization of topology + initial weights for a multilayer perceptron: G-Prop algorithm (Castillo e...
Evaluation lengthy and time-consuming </li></ul>
Client/server implementation
Old McREST had a farm <ul><li>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. </li></ul>
Reminder: REST architecture Send chromosomes to the server Request chromosomes Send chromosomes Get evaluated chromosomes ...
Evolutionary algorithm parameters
Experimental results
Speedup nodes speedup
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Distributed Evolutionary Computation using REST

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Presentation for IWDECIE within CEC2011

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  • Picture taken from http://www.flickr.com/photos/joeschram/207822199/in/photostream/
  • Picture from http://www.flickr.com/photos/25653307@N03/2716213900/in/photostream/
  • 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
  • Picture from http://www.flickr.com/photos/beauteous/48847025/in/photostream/
  • 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&apos;s available from the author&apos;s web site
  • Distributed Evolutionary Computation using REST

    1. 1. Distributed Evolutionary Computation using REST P.A. Castillo, M.G. Arenas, A.M. Mora, J.L.J. Laredo, G. Romero,V.M Rivas J.J. Merelo GeNeura Group http://geneura.wordpress.com http://twitter.com/geneura
    2. 2. If you've been paying attention REST beats other web-services implementations: lighter, more scalable
    3. 3. Let's put it to work Using a single server, different clients (master/slave implementation)
    4. 4. What problem? <ul><li>Optimization of topology + initial weights for a multilayer perceptron: G-Prop algorithm (Castillo et al., 1999)
    5. 5. Evaluation lengthy and time-consuming </li></ul>
    6. 6. Client/server implementation
    7. 7. Old McREST had a farm <ul><li>But we will not be using it for this.
    8. 8. Populations running in parallel on clients and use REST server for communication.
    9. 9. Hope to achieve better scaling that way. </li></ul>
    10. 10. Reminder: REST architecture Send chromosomes to the server Request chromosomes Send chromosomes Get evaluated chromosomes Uses PerlDancer Uses Algorithm::Evolutionary
    11. 11. Evolutionary algorithm parameters
    12. 12. Experimental results
    13. 13. Speedup nodes speedup
    14. 14. REST your parallelization in peace Just add clients!
    15. 15. Open source your science!
    16. 16. Any question? http://geneura.wordpress.com http://twitter.com/geneura

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