Evolving GPU Machine Code

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{JMLR:v16:dasilva15a,

• 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",
• number = "22",
• pages = "673--712",
• month = apr,
• keywords = "genetic algorithms, genetic programming, GPU, PTX, CUDA",
• publisher = "Microtome Publishing",
• ISSN = "1533-7928",
• URL = "http://jmlr.org/papers/v16/dasilva15a.html",
• URL = "http://jmlr.org/papers/volume16/dasilva15a/dasilva15a.pdf",
• size = "40 pages",
• 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

Citations