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PostPosted: Mon May 20, 2013 7:49 pm 
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Joined: Thu Mar 07, 2013 1:14 am
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Hey,

how can I parallelise my chance-sampled cfrm algo over multiple machines? It's pretty obvious that data-transfer between the machines is the bottleneck. Would it work if I just run the algo for the same gametree independently on two machines and merge the regrets and strategies after n iterations?

Thx.


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PostPosted: Mon May 20, 2013 8:36 pm 
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combining the regrets/strategies from two runs of n iterations is definitely not equivalent to one run of 2n iterations. i'm guessing the improvement would be marginal.


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PostPosted: Tue May 21, 2013 4:16 am 
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Ideally you would want to partition it to use the least amount of network. i.e. Splitting it at earlier branches in the game tree.


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PostPosted: Tue May 21, 2013 9:57 am 
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The Polaris paper talks about parallel implementation. You will have to combine the results every iteration. The biggest advantage IMO is the Ram of 2 machines, not a speedup.


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