GEVO-ML: A Proposal for Optimizing ML Code with Evolutionary Computation
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- @InProceedings{Liou:2020:GECCOcomp,
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author = "Jhe-Yu Liou and Xiaodong Wang and
Stephanie Forrest and Carole-Jean Wu",
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title = "{GEVO-ML}: A Proposal for Optimizing {ML} Code with
Evolutionary Computation",
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year = "2020",
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editor = "Richard Allmendinger and Hugo Terashima Marin and
Efren Mezura Montes and Thomas Bartz-Beielstein and
Bogdan Filipic and Ke Tang and David Howard and
Emma Hart and Gusz Eiben and Tome Eftimov and
William {La Cava} and Boris Naujoks and Pietro Oliveto and
Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and
Xiaodong Li and Saul Zapotecas and Qingfu Zhang and
Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and
Hisao Ishibuchi and Jonathan Fieldsend and
Ozgur Akman and Khulood Alyahya and Juergen Branke and
John R. Woodward and Daniel R. Tauritz and Marco Baioletti and
Josu Ceberio Uribe and John McCall and
Alfredo Milani and Stefan Wagner and Michael Affenzeller and
Bradley Alexander and Alexander (Sandy) Brownlee and
Saemundur O. Haraldsson and Markus Wagner and
Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and
Pablo {Valledor Pellicer} and Thomas Stuetzle and
Matthew Johns and Nick Ross and Ed Keedwell and
Herman Mahmoud and David Walker and Anthony Stein and
Masaya Nakata and David Paetzel and Neil Vaughan and
Stephen Smith and Stefano Cagnoni and Robert M. Patton and
Ivanoe {De Falco} and Antonio {Della Cioppa} and
Umberto Scafuri and Ernesto Tarantino and
Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and
Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and
Richard Everson and Handing Wang and Yaochu Jin and
Erik Hemberg and Riyad Alshammari and
Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and
Ponnuthurai Nagaratnam and Roman Senkerik",
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isbn13 = "9781450371278",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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URL = "https://doi.org/10.1145/3377929.3398139",
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DOI = "doi:10.1145/3377929.3398139",
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booktitle = "Proceedings of the 2020 Genetic and Evolutionary
Computation Conference Companion",
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pages = "1849--1856",
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size = "8 pages",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution, genetic improvement, GPU, machine learning,
multi-objective evolutionary computation",
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address = "internet",
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series = "GECCO '20",
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month = jul # " 8-12",
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organisation = "SIGEVO",
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abstract = "Parallel accelerators, such as GPUs, are a key enabler
of large-scale Machine Learning (ML) applications.
However, programmers often lack detailed knowledge of
the underlying architecture and fail to fully leverage
their computational power. This paper proposes GEVO-ML,
a tool for automatically discovering optimization
opportunities and tuning the performance of ML kernels.
GEVO-ML extends earlier work on GEVO (Gpu optimization
using EVOlutionary computation) by focusing directly on
ML frameworks, intermediate languages, and target
architectures. It retains the multi-objective
evolutionary search developed for GEVO, which searches
for edits to GPU code compiled to LLVM-IR and improves
performance on desired criteria while retaining
required functionality. In earlier work, we studied
some ML workloads in GPU settings and found that GEVO
could improve kernel speeds by factors ranging from
1.7X to 2.9X, even with access to only a small portion
of the overall ML framework. This workshop paper
examines the limitations and constraints of GEVO for ML
workloads and discusses our GEVO-ML design, which we
are currently implementing.",
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notes = "Also known as \cite{10.1145/3377929.3398139}
GECCO-2020 A Recombination of the 29th International
Conference on Genetic Algorithms (ICGA) and the 25th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Jhe-Yu Liou
Xiaodong Wang
Stephanie Forrest
Carole-Jean Wu
Citations