Effective Function Merging in the SSA Form

Rodrigo C.O. Rocha, Pavlos Petoumenos, Zheng Wang, Murray Cole, Hugh Leather

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

Abstract

Function merging is an important optimization for reducing code size. This technique eliminates redundant code across functions by merging them into a single function. While initially limited to identical or trivially similar functions, the most recent approach can identify all merging opportunities in arbitrary pairs of functions. However, this approach has a serious limitation which prevents it from reaching its full potential. Because it cannot handle phi-nodes, the state-of-the-art applies register demotion to eliminate them before applying its core algorithm. While a superficially minor workaround, this has a three-fold negative effect: by artificially lengthening the instruction sequences to be aligned, it hinders the identification of mergeable instruction; it prevents a vast number of functions from being profitably merged; it increases compilation overheads, both in terms of compile-time and memory usage.

We present SalSSA, a novel approach that fully supports the SSA form, removing any need for register demotion. By doing so, we notably increase the number of profitably merged functions. We implement SalSSA in LLVM and apply it to the SPEC 2006 and 2017 suites. Experimental results show that our approach delivers on average, 7.9% to 9.7% reduction on the final size of the compiled code. This translates to around 2×more code size reduction over the state-of-the-art. Moreover, as a result of aligning shorter sequences of instructions and reducing the number of wasteful merge operations, our new approach incurs an average compile-time overhead of only 5%, 3× less than the state-of-the-art, while also reducing memory usage by over 2×.
Original languageEnglish
Title of host publicationProceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation
PublisherAssociation for Computing Machinery (ACM)
Pages854-868
Number of pages15
ISBN (Electronic)9781450376136
DOIs
Publication statusPublished - 11 Jun 2020
Event41st ACM SIGPLAN Conference on Programming Language Design and Implementation - London, United Kingdom
Duration: 15 Jun 202020 Jun 2020
Conference number: 41
https://conf.researchr.org/home/pldi-2020

Conference

Conference41st ACM SIGPLAN Conference on Programming Language Design and Implementation
Abbreviated titlePLDI 2020
CountryUnited Kingdom
CityLondon
Period15/06/2020/06/20
Internet address

Keywords

  • Code Size Reduction
  • Function Merging
  • LTO

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