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 pro- grammed 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 compu- tations over the elementary processors greatly impacts per- formances. We also present memory and representation op- timizations 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.
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