Abstract
The availability of low cost powerful parallel graphics cards has stimulated the port of Genetic Programming (GP) on Graphics Processing Units (GPUs). Our work focuses on the possibilities offered by Nvidia G80 GPUs when programmed in the CUDA language. We compare two par-allelization schemes that evaluate several GP programs in parallel. We show that the fine grain distribution of computations over the elementary processors greatly impacts performances. We also present memory and representation optimizations that further enhance computation speed, up to 2.8 billion GP operations per second. The code has been developed with the well known ECJ library. Copyright 2009 ACM.
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CITATION STYLE
Robilliard, D., Marion, V., & Fonlupt, C. (2009). High performance genetic programming on GPU. In Proceedings of the 2009 Workshop on Bio-inspired Algorithms for Distributed Systems, BADS ’09 (pp. 85–93). Association for Computing Machinery (ACM). https://doi.org/10.1145/1555284.1555299
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