Parallel Performance Modeling using a Genetic Programming-based Error Correction Procedure
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{journals/simulation/RaghavacharMWZR07,
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author = "Kavitha Raghavachar and G. Mahinthakumar and
Patrick H. Worley and Emily M. Zechman and S. Ranji Ranjithan",
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title = "Parallel Performance Modeling using a Genetic
Programming-based Error Correction Procedure",
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journal = "Simulation",
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year = "2007",
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volume = "83",
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number = "7",
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pages = "515--527",
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month = jul,
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keywords = "genetic algorithms, genetic programming, Error
correction procedure, performance modeling",
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DOI = "doi:10.1177/0037549707084691",
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size = "14 pages",
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abstract = "Performance models of high performance computing (HPC)
applications are important for several reasons. First,
they provide insight to designers of HPC systems on the
role of subsystems such as the processor or the network
in determining application performance. Second, they
allow HPC centers more accurately to target
procurements to resource requirements. Third, they can
be used to identify application performance bottlenecks
and to provide insights about scalability issues. The
suitability of a performance model, however, for a
particular performance investigation is a function of
both the accuracy and the cost of the model.
A semi-empirical model previously published by the
authors for an astrophysics application was shown to be
inaccurate when predicting communication cost for large
numbers of processors. It is hypothesized that this
deficiency is due to the inability of the model
adequately to capture communication contention
(threshold effects) as well as other unmodeled
components such as noise and I/O contention. In this
paper we present a new approach to capture these
unknown features to improve the predictive capabilities
of the model. This approach uses a systematic model
error-correction procedure that uses evolutionary
algorithms to find an error correction term to augment
the existing model. Four variations of this procedure
were investigated and all were shown to produce better
results than the original model. Successful
cross-platform application of this approach showed that
it adequately captures machine dependent
characteristics. This approach was then successfully
demonstrated for a second application, further showing
its versatility.",
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notes = "GYRO B1-std, B2-cy and B3-gtc problems (fitting)",
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bibdate = "2009-09-28",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/simulation/simulation83.html#RaghavacharMWZR07",
- }
Genetic Programming entries for
Kavitha Raghavachar
G (Kumar) Mahinthakumar
Patrick H Worley
Emily M Zechman
S Ranji Ranjithan
Citations