Facebook Is Aiming To Make Compilers Faster Using Machine Learning With CompilerGym
Written by Michael Larabel in Programming on 2 October 2021 at 05:53 AM EDT. 44 Comments
PROGRAMMING --
Facebook this week announced the open-sourcing of CompilerGym as their effort to improve compiler performance by leveraging machine learning to tackle optimization work.

CompilerGym is built by Facebook's AI team atop the OpenAI Gym and is ultimately striving to help enhance code compiler performance. They explained in this week's announcement, "CompilerGym packages important compiler optimization problems and makes them look like reinforcement learning problems. The compiler optimization problems we include are large in scale. For example, for one, the search space is 104461, considerably larger than that of the board game Go. For another, the search space is infinite. ​​Advances on problems of such scale are possible for the first time only because of very recent advances in reinforcement learning. CompilerGym makes it easy for anyone with an ML or compiler background to dive right in and start solving the problems, all without the months of tedious setup time that would normally be required. And that’s because we’ve done it for you!"

The announcement went on to add, "Our goal is to be a catalyst for using ML to make compilers faster, which is important as poorly optimized programs are slow and consume too many computing resources as well as too much energy, limiting applications of energy-efficient edge devices and making data centers less environmentally friendly."

Those wishing to learn more about CompilerGym can do so via ai.facebook.com.
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Michael Larabel is the principal author of Phoronix.com and founded the site in 2004 with a focus on enriching the Linux hardware experience. Michael has written more than 20,000 articles covering the state of Linux hardware support, Linux performance, graphics drivers, and other topics. Michael is also the lead developer of the Phoronix Test Suite, Phoromatic, and OpenBenchmarking.org automated benchmarking software. He can be followed via Twitter or contacted via MichaelLarabel.com.

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