Evolving CUDA PTX programs by quantum inspired linear genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Leandro F. Cupertino and Cleomar P. Silva and 
                 Douglas M. Dias and Marco Aurelio C. Pacheco and 
                 Cristiana Bentes",
  title =        "Evolving CUDA PTX programs by quantum inspired linear
                 genetic programming",
  booktitle =    "GECCO 2011 Computational intelligence on consumer
                 games and graphics hardware (CIGPU)",
  year =         "2011",
  editor =       "Simon Harding and W. B. Langdon and Man Leung Wong and 
                 Garnett Wilson and Tony Lewis",
  isbn13 =       "978-1-4503-0690-4",
  keywords =     "genetic algorithms, genetic programming, EDA,
                 Artificial Intelligence, automatic programming, program
                 synthesis, Performance, GPU, CUDA, PTX,
                 quantum-inspired algorithms",
  pages =        "399--406",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001858.2002026",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  size =         "8 pages",
  abstract =     "The tremendous computing power of Graphics Processing
                 Units (GPUs) can be used to accelerate the evolution
                 process in Genetic Programming (GP). The automatic
                 generation of code using the GPU usually follows two
                 different approaches: compiling each evolved or
                 interpreting multiple programs. Both approaches,
                 however, have performance drawbacks. In this work, we
                 propose a novel approach where the GPU pseudo-assembly
                 language, PTX (Parallel Thread Execution), is evolved.
                 Evolving PTX programs is faster, since the compilation
                 of a PTX program takes orders of magnitude less time
                 than a CUDA program compilation on the CPU, and no
                 interpreter is necessary. Another important aspect of
                 our approach is that the evolution of PTX programs
                 follows the Quantum Inspired Linear Genetic Programming
                 (QILGP). Our approach, called QILGP3U (QILGP + GPGPU),
                 enables the evolution on a single machine in a
                 reasonable time, enhances the quality of the model with
                 the use of PTX, and for big databases can be much
                 faster than the CPU implementation.",
  notes =        "No absolute speed measure given (cf.
                 \cite{langdon:2008:eurogp}). Mexican Hat. Almost all
                 time spent compiling PTX. Header-body(evolved)-foot.
                 nVidia Tesla C1060 GPU.

                 Also known as \cite{2002026} Distributed on CD-ROM at

                 ACM Order Number 910112.",

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