Multi- and Many-Threaded Heterogeneous Parallel Grammatical Evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InCollection{Dufek:2018:hbge,
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author = "Amanda Sabatini Dufek and Douglas Adriano Augusto and
Helio Jose Correa Barbosa and
Pedro Leite {da Silva Dias}",
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title = "Multi- and Many-Threaded Heterogeneous Parallel
Grammatical Evolution",
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booktitle = "Handbook of Grammatical Evolution",
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publisher = "Springer",
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year = "2018",
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editor = "Conor Ryan and Michael O'Neill and J. J. Collins",
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chapter = "9",
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pages = "219--244",
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keywords = "genetic algorithms, genetic programming, Grammatical
Evolution",
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isbn13 = "978-3-319-78716-9",
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DOI = "doi:10.1007/978-3-319-78717-6_9",
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abstract = "There are some algorithms suited for inference of
human-interpretable models for classification and
regression tasks in machine learning, but it is hard to
compete with Grammatical Evolution (GE) when it comes
to powerfulness, model expressiveness and ease of
implementation. On the other hand, algorithms that
iteratively optimize a set of programs of arbitrary
complexity (which is the case of GE) may take an
inconceivable amount of running time when tackling
complex problems. Fortunately, GE may scale to such
problems by carefully harnessing the parallel
processing of modern heterogeneous systems, taking
advantage of traditional multi-core processors and
many-core accelerators to speed up the execution by
orders of magnitude. This chapter covers the subject of
parallel GE, focusing on heterogeneous multi- and
many-threaded decomposition in order to achieve a fully
parallel implementation, where both the breeding and
evaluation are parallelised. In the studied benchmarks,
the overall parallel implementation runtime was 68
times faster than the sequential version, with the
program evaluation kernel alone hitting an acceleration
of 350 times. Details on how to efficiently accomplish
that are given in the context of two well-established
open standards for parallel computing: OpenMP and
OpenCL. Decomposition strategies, optimization
techniques and parallel benchmarks followed by analyses
are presented",
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notes = "Part of \cite{Ryan:2018:hbge}",
- }
Genetic Programming entries for
Amanda Sabatini Dufek
Douglas A Augusto
Helio J C Barbosa
Pedro Leite da Silva Dias
Citations