TMBL kernels for CUDA GPUs compile faster using PTX: computational intelligence on consumer games and graphics hardware

Authors:
Tony E. Lewis;George D. Magoulas
Affiliations:
University of London, London, United Kingdom;University of London, London, United Kingdom
Venue:
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Year:
2011

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Abstract

Many of the most effective attempts to harness the power of the Graphics Processing Unit (GPU) to accelerate Genetic Programming (GP) have dynamically compiled code for individuals as they are to be evaluated. This approach executes very quickly on the GPU but is slow to compile, hence only vast data-sets fully reap its rewards. To reduce compilation time, we generate and compile code in the lower-level language PTX. We investigate this in the context of implementing Tweaking Mutation Behaviour Learning (TMBL) on the GPU. We find that for programs of 300 instructions, using PTX reduces the compile time 5.861 times and even increases the evaluation speed by 23.029%.