Re: Machine learning to schedule optimization passes

From	Jon Chesterfield <jonathanchesterfield@gmail.com>
Newsgroups	comp.compilers
Subject	Re: Machine learning to schedule optimization passes
Date	2024-08-29 20:45 +0100
Organization	Compilers Central
Message-ID	<24-08-013@comp.compilers> (permalink)
References	<24-08-011@comp.compilers>

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This paper makes no mention of correctness or behaviour, only code size. Also
no mention of compile time. I wonder how the results would compare to a
baseline of running the usual O3 pipeline to fixpoint.

Changing order of passes usually uncovers correctness bugs which papers of
this genre rarely worry about. It's very easy to make programs smaller if you
don't mind changing behaviour.

Jon

On Thu, 29 Aug 2024, 19:35 John R Levine,
<[johnl@taugh.com](mailto:johnl@taugh.com)> wrote:

> This paper used machine learning to select and order LLVM optimization
>  passes. Apparently it worked pretty well.
>
>
>  CompilerDream: Learning a Compiler World Model for General Code Optimization
>
>  Effective code optimization in compilers is crucial for computer and
>  software engineering. ...
>
>  Full paper at: <https://arxiv.org/abs/2404.16077>

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Machine learning to schedule optimization passes John R Levine <johnl@taugh.com> - 2024-08-29 12:21 -0400
  Re: Machine learning to schedule optimization passes Jon Chesterfield <jonathanchesterfield@gmail.com> - 2024-08-29 20:45 +0100

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