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Matthew E. Taylor, Katherine E. Coons, Behnam Robatmili, Bertrand A. Maher,
Doug Burger, and Kathryn S. McKinley. Evolving Compiler Heuristics to Manage Communication and Contention. In Proceedings
of the Twenty-Fourth Conference on Artificial Intelligence (AAAI), July 2010. Nectar Track, 25% acceptance rate
AAAI-2010. This paper is based on results presented in our
earlier PACT-08 paper.
As computer architectures become increasingly complex, hand-tuningcompiler heuristics becomes increasingly tedious and time
consumingfor compiler developers. This paper presents a case study that uses agenetic algorithm to learn a compiler policy.
The target policyimplicitly balances communication and contention among processingelements of the TRIPS processor, a physically
realized prototype chip.We learn specialized policies for individual programs as well asgeneral policies that work well across
all programs. We also employ atwo-stage method that first classifies the code being compiled basedon salient characteristics,
and then chooses a specialized policybased on that classification.
This work is particularly interesting for the AI community
because it1 emphasizes the need for increased collaboration between AIresearchers and researchers from other branches of computer
scienceand 2 discusses a machine learning setup where training on the customhardware requires weeks of training, rather than
the more typicalminutes or hours.
@InProceedings(AAAI10-Nectar-taylor, author="Matthew E. Taylor and Katherine E. Coons and Behnam Robatmili and Bertrand A. Maher and Doug Burger and Kathryn S. McKinley", title="Evolving Compiler Heuristics to Manage Communication and Contention", note = "Nectar Track, 25% acceptance rate", booktitle="Proceedings of the Twenty-Fourth Conference on Artificial Intelligence ({AAAI})", month="July",year="2010", abstract=" As computer architectures become increasingly complex, hand-tuning compiler heuristics becomes increasingly tedious and time consuming for compiler developers. This paper presents a case study that uses a genetic algorithm to learn a compiler policy. The target policy implicitly balances communication and contention among processing elements of the TRIPS processor, a physically realized prototype chip. We learn specialized policies for individual programs as well as general policies that work well across all programs. We also employ a two-stage method that first classifies the code being compiled based on salient characteristics, and then chooses a specialized policy based on that classification. <br> This work is particularly interesting for the AI community because it 1 emphasizes the need for increased collaboration between AI researchers and researchers from other branches of computer science and 2 discusses a machine learning setup where training on the custom hardware requires weeks of training, rather than the more typical minutes or hours.", wwwnote={<a href="http://www.aaai.org/Conferences/AAAI/aaai10.php">AAAI-2010</a>. This paper is based on results presented in our earlier <a href="b2hd-PACT08-coons.html">PACT-08 paper</a>.}, )
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