2026-09-18T17:41:07Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1447222022-08-30T03:43:03Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Chlipala, Adam
Ragan-Kelley, Jonathan
Liu, Amanda
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-08-29T16:07:12Z
2022-08-29T16:07:12Z
2022-05
2022-06-21T19:25:47.865Z
https://hdl.handle.net/1721.1/144722
https://orcid.org/ 0000-0001-5549-9177
I propose a lightweight Coq framework for optimizing tensor kernels written in a pure, functional array language. Optimizations rely on user scheduling using series of verified, semantics-preserving rewrites. Unusually for compilation targeting imperative code with arrays and nested loops, all rewrites are source-to-source within a purely functional language. This language comprises a set of core constructs for expressing high-level computation detail and a set of what we call reshape operators, which can be derived from core constructs but trigger low-level decisions about storage patterns and ordering. We will demonstrate that not only is this system capable of deriving the optimizations of existing state-of-the-art languages like Halide and generating comparably performant code, it is also able to schedule a family of useful program transformations beyond what is reachable in Halide.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
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http://rightsstatements.org/page/InC-EDU/1.0/
Verified Scheduling Via High-Level Scheduling Rewrites
Thesis
application/pdf
Master
Master of Science in Electrical Engineering and Computer Science
Publication
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unknown
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<Abstract>I propose a lightweight Coq framework for optimizing tensor kernels written in a pure, functional array language. Optimizations rely on user scheduling using series of verified, semantics-preserving rewrites. Unusually for compilation targeting imperative code with arrays and nested loops, all rewrites are source-to-source within a purely functional language. This language comprises a set of core constructs for expressing high-level computation detail and a set of what we call reshape operators, which can be derived from core constructs but trigger low-level decisions about storage patterns and ordering. We will demonstrate that not only is this system capable of deriving the optimizations of existing state-of-the-art languages like Halide and generating comparably performant code, it is also able to schedule a family of useful program transformations beyond what is reachable in Halide.</Abstract>
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