Evolving GPU Machine Code

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Cleomar Pereira {da Silva} and Douglas Mota Dias and 
                 Cristiana Bentes and 
                 Marco Aurelio Cavalcanti Pacheco and Leandro Fontoura Cupertino",
  title =        "Evolving GPU Machine Code",
  journal =      "Journal of Machine Learning Research",
  year =         "2015",
  volume =       "16",
  pages =        "673--712",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, GPU, PTX,
  publisher =    "Microtome Publishing",
  ISSN =         "1533-7928",
  URL =          "http://jmlr.org/papers/v16/dasilva15a.html",
  URL =          "http://jmlr.org/papers/volume16/dasilva15a/dasilva15a.pdf",
  abstract =     "Parallel Graphics Processing Unit (GPU)
                 implementations of GP have appeared in the literature
                 using three main methodologies: (i) compilation, which
                 generates the individuals in GPU code and requires
                 compilation; (ii) pseudo-assembly, which generates the
                 individuals in an intermediary assembly code and also
                 requires compilation; and (iii) interpretation, which
                 interprets the codes. This paper proposes a new
                 methodology that uses the concepts of quantum computing
                 and directly handles the GPU machine code instructions.
                 Our methodology uses a probabilistic representation of
                 an individual to improve the global search capability.
                 In addition, the evolution in machine code eliminates
                 both the overhead of compiling the code and the cost of
                 parsing the program during evaluation. We obtained up
                 to 2.74 trillion GP operations per second for the
                 20-bit Boolean Multiplexer benchmark. We also compared
                 our approach with the other three GPU-based
                 acceleration methodologies implemented for
                 quantum-inspired linear GP. Significant gains in
                 performance were obtained.",
  notes =        "20-Mux",

Genetic Programming entries for Cleomar Pereira da Silva Douglas Mota Dias Cristiana Bentes Marco Aurelio Cavalcanti Pacheco Leandro Fontoura Cupertino