Stargazer: Automated regression-based GPU design space exploration
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{DBLP:conf/ispass/JiaSM12,
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author = "Wenhao Jia and Kelly A. Shaw and Margaret Martonosi",
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title = "Stargazer: Automated regression-based {GPU} design
space exploration",
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booktitle = "2012 IEEE International Symposium on Performance
Analysis of Systems \& Software",
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year = "2012",
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editor = "Rajeev Balasubramonian and Vijayalakshmi Srinivasan",
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pages = "2--13",
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address = "New Brunswick, NJ, USA",
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month = apr # " 1-3",
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keywords = "genetic algorithms, genetic programming, genetic
improvement, SBSE, GPU",
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timestamp = "Wed, 16 Oct 2019 14:14:56 +0200",
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biburl = "https://dblp.org/rec/conf/ispass/JiaSM12.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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URL = "http://jiawenhao.com/stargazer.pdf",
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DOI = "doi:10.1109/ISPASS.2012.6189201",
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size = "12 pages",
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abstract = "Graphics processing units (GPUs) are of increasing
interest because they offer massive parallelism for
high-throughput computing. While GPUs promise high peak
performance, their challenge is a less-familiar
programming model with more complex and irregular
performance trade-offs than traditional CPUs or CMPs.
In particular, modest changes in software or hardware
characteristics can lead to large or unpredictable
changes in performance. In response to these
challenges, our work proposes, evaluates, and offers
usage examples of Stargazer 1 , an automated GPU
performance exploration framework based on stepwise
regression modeling. Stargazer sparsely and randomly
samples parameter values from a full GPU design space
and simulates these designs. Then, our automated
stepwise algorithm uses these sampled simulations to
build a performance estimator that identifies the most
significant architectural parameters and their
interactions. The result is an application-specific
performance model which can accurately predict program
runtime for any point in the design space. Because very
few initial performance samples are required relative
to the extremely large design space, our method can
drastically reduce simulation time in GPU studies. For
example, we used Stargazer to explore a design space of
nearly 1 million possibilities by sampling only 300
designs. For 11 GPU applications, we were able to
estimate their runtime with less than 1.1 percent
average error. In addition, we demonstrate several
usage scenarios of Stargazer.",
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notes = "not GP? breadth-first search GPGPU-Sim benchmark suite
and the Rodinia benchmark suite",
- }
Genetic Programming entries for
Wenhao Jia
Kelly A Shaw
Margaret Martonosi
Citations