Evolving a CUDA kernel from an nVidia template

William B. Langdon, Mark Harman
Department of Computer Science, King’s College, London, Strand, London, WC2R 2LS, UK
IEEE Congress on Evolutionary Computation (CEC), 2010, p.2376-2383


   title={Evolving a CUDA Kernel from an nVidia Template},

   author={Langdon, WB and Harman, M.},

   booktitle={2010 IEEE World Congress on Computational Intelligence, Barcelona},





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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. Backto-back validation uses the original code as a test oracle.
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