research-article

YACOS: a Complete Infrastructure to the Design and Exploration of Code Optimization Sequences

Authors:

André Felipe Zanella,

Anderson Faustino da Silva,

Fernando Magno QuintãoAuthors Info & Claims

SBLP '20: Proceedings of the 24th Brazilian Symposium on Context-Oriented Programming and Advanced Modularity

Pages 56 - 63

https://doi.org/10.1145/3427081.3427089

Published: 22 October 2020 Publication History

Abstract

The growing popularity of machine learning frameworks and algorithms has greatly contributed to the design and exploration of good code optimization sequences. Yet, in spite of this progress, mainstream compilers still provide users with only a handful of fixed optimization sequences. Finding optimization sequences that are good in general is challenging because the universe of possible sequences is potentially infinite. This paper describes a infrastructure that provides developers with the means to explore this space. Said infrastructure, henceforth called YaCoS, consists of benchmarks, search algorithms, metrics to estimate the distance between programs, and compilation strategies. YaCoS's features let users build learning models that predict, for unknown programs, optimization sequences that are likely to yield good results for them. In this paper, as a case study, we have used YaCoS to find good optimization sequences for LLVM, using code size as the objective function. Such study lets us evaluate three feature sets: two variations of the feature vectors proposed by Namolaru at al in 2010, plus the optimization statistics produced by LLVM. Our results show that YaCoS is able to find sequences that improve onto clang -Oz by 3.75% on average. Our experiments do not indicate a dominant feature set out of the three approaches that we have investigated---it is possible to find programs in which one of them is strictly better than the others.

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Cited By

Ni YDu XSong LXiao RYe PWang J(2024)A Two-Stage LLVM Option Sequence Optimization Method to Minimize Energy ConsumptionSwarm and Evolutionary Computation10.1016/j.swevo.2024.10159188(101591)Online publication date: Jul-2024
https://doi.org/10.1016/j.swevo.2024.101591
Damasio TCanesche MPacheco VFaustino AQuintao Pereira F(2022)Impacto de Ofuscadores e Otimizadores de Código na Acurácia de Classificadores de ProgramasProceedings of the XXVI Brazilian Symposium on Programming Languages10.1145/3561320.3561322(68-75)Online publication date: 6-Oct-2022
https://dl.acm.org/doi/10.1145/3561320.3561322
Jain SAndaluri YVenkataKeerthy SUpadrasta R(2022)POSET-RL: Phase ordering for Optimizing Size and Execution Time using Reinforcement Learning2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)10.1109/ISPASS55109.2022.00012(121-131)Online publication date: May-2022
https://doi.org/10.1109/ISPASS55109.2022.00012
Show More Cited By

Index Terms

YACOS: a Complete Infrastructure to the Design and Exploration of Code Optimization Sequences
1. Software and its engineering
  1. Software notations and tools
    1. Compilers
      1. Runtime environments
    2. Software libraries and repositories

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cover image ACM Other conferences

SBLP '20: Proceedings of the 24th Brazilian Symposium on Context-Oriented Programming and Advanced Modularity

October 2020

81 pages

ISBN:9781450389433

DOI:10.1145/3427081

General Chairs:
Everton Cavalcante
UFRN, Brazil
,
Francisco Dantas
UERN, Brazil
,
Thais Batista
UFRN, Brazil

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SBC: Brazilian Computer Society

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 October 2020

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Conselho Nacional de Desenvolvimento Científico e Tecnológico

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SBLP '20

SBLP '20: 24th Brazilian Symposium on Programming Languages

October 19 - 20, 2020

Natal, Brazil

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Overall Acceptance Rate 22 of 50 submissions, 44%

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Cited By

Ni YDu XSong LXiao RYe PWang J(2024)A Two-Stage LLVM Option Sequence Optimization Method to Minimize Energy ConsumptionSwarm and Evolutionary Computation10.1016/j.swevo.2024.10159188(101591)Online publication date: Jul-2024
https://doi.org/10.1016/j.swevo.2024.101591
Damasio TCanesche MPacheco VFaustino AQuintao Pereira F(2022)Impacto de Ofuscadores e Otimizadores de Código na Acurácia de Classificadores de ProgramasProceedings of the XXVI Brazilian Symposium on Programming Languages10.1145/3561320.3561322(68-75)Online publication date: 6-Oct-2022
https://dl.acm.org/doi/10.1145/3561320.3561322
Jain SAndaluri YVenkataKeerthy SUpadrasta R(2022)POSET-RL: Phase ordering for Optimizing Size and Execution Time using Reinforcement Learning2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)10.1109/ISPASS55109.2022.00012(121-131)Online publication date: May-2022
https://doi.org/10.1109/ISPASS55109.2022.00012
do Rosario Vda Silva AZanella ANapoli OBorin E(2022)Fast selection of compiler optimizations using performance prediction with graph neural networksConcurrency and Computation: Practice and Experience10.1002/cpe.686935:17Online publication date: 16-Mar-2022
https://doi.org/10.1002/cpe.6869
Faustino ABorin EPereira FNápoli ORosário V(2021)New Optimization Sequences for Code-Size Reduction for the LLVM Compilation InfrastructureProceedings of the 25th Brazilian Symposium on Programming Languages10.1145/3475061.3475085(33-40)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1145/3475061.3475085
Queiroz Junior NFaustino da Silva A(2021)A Graph-based Model for Building Optimization SequencesProceedings of the 25th Brazilian Symposium on Programming Languages10.1145/3475061.3475083(92-99)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1145/3475061.3475083
Faustino A(2021)Graphs based on IR as Representation of CodeProceedings of the 25th Brazilian Symposium on Programming Languages10.1145/3475061.3475063(75-82)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1145/3475061.3475063
da Silva AKind Bde Souza Magalhães JRocha JGuimarães BPereira FLee J(2021)AnghaBenchProceedings of the 2021 IEEE/ACM International Symposium on Code Generation and Optimization10.1109/CGO51591.2021.9370322(378-390)Online publication date: 27-Feb-2021
https://dl.acm.org/doi/10.1109/CGO51591.2021.9370322

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