Distributed genetic programming on GPUs using CUDA

Simon Harding, Wolfgang Banzhaf
Department of Computer Science, Memorial University Canada
Proceedings of the Second International Workshop on Parallel Architectures and Bioinspired Algorithms (WPABA 2009), WPABA’09, p.1-10


   title={Distributed genetic programming on GPUs using CUDA},

   author={Harding, S.L. and Banzhaf, W.},

   journal={Wks Paral. Arch. and Bioinspired Algs},




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Using of a cluster of Graphics Processing Unit (GPU) equipped computers, it is possible to accelerate the evaluation of individuals in Genetic Programming. Program compilation, fitness case data and fitness execution are spread over the cluster of computers, allowing for the efficient processing of very large datasets. Here, the implementation is demonstrated on datasets containing over 10 million rows and several hundred megabytes in size. Populations of candidate individuals are compiled into NVidia CUDA programs and executed on a set of client computers – each with a different subset of the dataset. The paper discusses the implementation of the system and acts as a tutorial for other researchers experimenting with genetic programming and GPUs.
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