Kavli Affiliate: Jia Liu
| First 5 Authors: Chris Cummins, Bram Wasti, Jiadong Guo, Brandon Cui, Jason Ansel
| Summary:
Interest in applying Artificial Intelligence (AI) techniques to compiler
optimizations is increasing rapidly, but compiler research has a high entry
barrier. Unlike in other domains, compiler and AI researchers do not have
access to the datasets and frameworks that enable fast iteration and
development of ideas, and getting started requires a significant engineering
investment. What is needed is an easy, reusable experimental infrastructure for
real world compiler optimization tasks that can serve as a common benchmark for
comparing techniques, and as a platform to accelerate progress in the field.
We introduce CompilerGym, a set of environments for real world compiler
optimization tasks, and a toolkit for exposing new optimization tasks to
compiler researchers. CompilerGym enables anyone to experiment on production
compiler optimization problems through an easy-to-use package, regardless of
their experience with compilers. We build upon the popular OpenAI Gym interface
enabling researchers to interact with compilers using Python and a familiar
API.
We describe the CompilerGym architecture and implementation, characterize the
optimization spaces and computational efficiencies of three included compiler
environments, and provide extensive empirical evaluations. Compared to prior
works, CompilerGym offers larger datasets and optimization spaces, is 27x more
computationally efficient, is fault-tolerant, and capable of detecting
reproducibility bugs in the underlying compilers.
In making it easy for anyone to experiment with compilers – irrespective of
their background – we aim to accelerate progress in the AI and compiler
research domains.
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