Vectorization-Aware Loop Unrolling with Seed Forwarding

Rodrigo Rocha Rocha, Vasileios Porpodas, Pavlos Petoumenos, Luis F.W. Goes, Zheng Wang, Murray Cole, Hugh Leather

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Loop unrolling is a widely adopted loop transformation, commonly used for enabling subsequent optimizations. Straightline-code vectorization (SLP) is an optimization that benefits from unrolling. SLP converts isomorphic instruction sequences into vector code. Since unrolling generates repeatead isomorphic instruction sequences, it enables SLP to vectorize more code. However, most production compilers apply these optimizations independently and uncoordinated. Unrolling is commonly tuned to avoid code bloat, not maximizing the potential for vectorization, leading to missed vectorization opportunities. We are proposing VALU, a novel loop unrolling heuristic that takes vectorization into account when making unrolling decisions. Our heuristic is powered by an analysis that estimates the potential benefit of SLP vectorization for the unrolled version of the loop. Our heuristic then selects the unrolling factor that maximizes the utilization of the vector units. VALU also forwards the vectorizable code to SLP, allowing it to bypass its greedy search for vectorizable seed instructions, exposing more vectorization opportunities. Our evaluation on a production compiler shows that VALU uncovers many vectorization opportunities that were missed by the default loop unroller and vectorizers. This results in more vectorized code and significant performance speedups for 17 of the kernels of the TSVC benchmarks suite, reaching up to 2× speedup over the already highly optimized -O3. Our evaluation on full benchmarks from FreeBench and MiBench shows that VALU results in a geo-mean speedup of 1.06×.
Original languageEnglish
Title of host publicationCC 2020: Proceedings of the 29th International Conference on Compiler Construction
PublisherAssociation for Computing Machinery (ACM)
Pages1-13
Number of pages13
ISBN (Print)9781450371209
DOIs
Publication statusPublished - 22 Feb 2020
EventACM SIGPLAN 2020 International Conference on Compiler Construction - San Diego, United States
Duration: 22 Feb 202023 Feb 2020
Conference number: 29
https://conf.researchr.org/home/CC-2020

Conference

ConferenceACM SIGPLAN 2020 International Conference on Compiler Construction
Abbreviated titleCC 2020
Country/TerritoryUnited States
CitySan Diego
Period22/02/2023/02/20
Internet address

Keywords

  • Auto-Vectorization
  • SIMD
  • loop unrolling
  • SLP

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