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  1. Proceedings of the eighteenth international conference on Architectural support for programming languages and operating systems (ASPLOS '13)
  2. Stochastic superoptimization
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Stochastic superoptimization

Content Provider ACM Digital Library
Author Schkufza, Eric Sharma, Rahul Aiken, Alex
Abstract We formulate the loop-free binary superoptimization task as a stochastic search problem. The competing constraints of transformation correctness and performance improvement are encoded as terms in a cost function, and a Markov Chain Monte Carlo sampler is used to rapidly explore the space of all possible programs to find one that is an optimization of a given target program. Although our method sacrifices completeness, the scope of programs we are able to consider, and the resulting quality of the programs that we produce, far exceed those of existing superoptimizers. Beginning from binaries compiled by llvm -O0 for 64-bit x86, our prototype implementation, STOKE, is able to produce programs which either match or outperform the code produced by gcc -O3, icc -O3, and in some cases, expert handwritten assembly.
Starting Page 305
Ending Page 316
Page Count 12
File Format PDF
ISBN 9781450318709
DOI 10.1145/2451116.2451150
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-03-16
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Markov chain monte carlo Superoptimization 64-bit Stochastic search X86 Mcmc Binary Smt X86-64
Content Type Text
Resource Type Article
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