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Evolving a CUDA Kernel from an nVidia Template

By W. B. Langdon and M. Harman

Abstract

Rather than attempting to evolve a complete program from scratch we demonstrate genetic interface programming (GIP) by automatically generating a parallel CUDA kernel with identical functionality to existing highly optimised ancient sequential C code (gzip). Generic GPGPU nVidia kernel C++ code is converted into a BNF grammar. Strongly typed genetic programming uses the BNF to generate compilable and executable graphics card kernels. Their fitness is given by running the population on a GPU with randomised subsets of training data itself derived from gzip’s SIR test suite. Back-to-back validation uses the original code as a test oracle

Year: 2010
OAI identifier: oai:CiteSeerX.psu:10.1.1.173.5882
Provided by: CiteSeerX
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