GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation

Ondrej Sykora, Phitchaya Mangpo Phothilimthana, Charith Mendis, Amir Yazdanbakhsh

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

Abstract

Analytical hardware performance models yield swift estimation of desired hardware performance metrics. However, developing these analytical models for modern processors with sophisticated microarchitectures is an extremely laborious task and requires a firm understanding of target microarchitecture's internal structure. In this paper, we introduce GRANITE1, a new machine learning model that estimates the throughput of basic blocks across different microarchitectures. GRANITE uses a graph representation of basic blocks that captures both structural and data dependencies between instructions. This representation is processed using a graph neural network that takes advantage of the relational information captured in the graph and learns a rich neural representation of the basic block that allows more precise throughput estimation. Our results establish a new state-of-the-art for basic block performance estimation with an average test error of 6.9% across a wide range of basic blocks and microarchitectures for the x86-64 target. Compared to recent work, this reduced the error by 1.7% wile improving training and inference throughput by approximately 3.0×. In addition, we propose the use of multitask learning with independent multi-layer feed forward decoder networks. Our results show that this technique further improves precision of all learned models while significantly reducing per-microarchitecture training costs. We perform an extensive set of ablation studies and comparisons with prior work, concluding a set of methods to achieve high accuracy for basic block performance estimation.1GRANITE:

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE International Symposium on Workload Characterization, IISWC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages14-26
Number of pages13
ISBN (Electronic)9781665487986
DOIs
StatePublished - 2022
Event2022 IEEE International Symposium on Workload Characterization, IISWC 2022 - Austin, United States
Duration: Nov 6 2022Nov 8 2022

Publication series

NameProceedings - 2022 IEEE International Symposium on Workload Characterization, IISWC 2022

Conference

Conference2022 IEEE International Symposium on Workload Characterization, IISWC 2022
Country/TerritoryUnited States
CityAustin
Period11/6/2211/8/22

Keywords

  • graph neural network
  • machine learnig
  • microarchitecture
  • optimization
  • performance
  • performance model
  • throughput estimation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Hardware and Architecture

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