High performance evaluation of evolutionary-mined association rules on GPUs
Created by W.Langdon from
gp-bibliography.bib Revision:1.7989
- @Article{Cano:2013:JSUP,
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author = "Alberto Cano and Jose Maria Luna and
Sebastian Ventura",
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title = "High performance evaluation of evolutionary-mined
association rules on GPUs",
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journal = "The Journal of Supercomputing",
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year = "2013",
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volume = "66",
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number = "3",
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pages = "1438--1461",
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month = dec,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Performance
evaluation, Association rules, Parallel computing,
GPU",
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ISSN = "0920-8542",
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language = "English",
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URL = "http://link.springer.com/article/10.1007/s11227-013-0937-4/fulltext.html",
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DOI = "doi:10.1007/s11227-013-0937-4",
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size = "24 pages",
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abstract = "Association rule mining is a well-known data mining
task, but it requires much computational time and
memory when mining large scale data sets of high
dimensionality. This is mainly due to the evaluation
process, where the antecedent and consequent in each
rule mined are evaluated for each record. This paper
presents a novel methodology for evaluating association
rules on graphics processing units (GPUs). The
evaluation model may be applied to any association rule
mining algorithm. The use of GPUs and the compute
unified device architecture (CUDA) programming model
enables the rules mined to be evaluated in a massively
parallel way, thus reducing the computational time
required. This proposal takes advantage of concurrent
kernels execution and asynchronous data transfers,
which improves the efficiency of the model. In an
experimental study, we evaluate interpreter performance
and compare the execution time of the proposed model
with regard to single-threaded, multi-threaded, and
graphics processing unit implementation. The results
obtained show an interpreter performance above 67
billion giga operations per second, and speed-up by a
factor of up to 454 over the single-threaded CPU model,
when using two NVIDIA 480 GTX GPUs. The evaluation
model demonstrates its efficiency and scalability
according to the problem complexity, number of
instances, rules, and GPU devices.",
- }
Genetic Programming entries for
Alberto Cano Rojas
Jose Maria Luna
Sebastian Ventura
Citations