Application Performance Modeling via Tensor Completion

Edward Hutter, Edgar Solomonik

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

Abstract

Performance tuning, software/hardware co-design, and job sched-uling are among the many tasks that rely on models to predict application performance. We propose and evaluate low-rank tensor decomposition for modeling application performance. We discretize the input and configuration domains of an application using reg-ular grids. Application execution times mapped within grid-cells are averaged and represented by tensor elements. We show that low-rank canonical-polyadic (CP) tensor decomposition is effective in approximating these tensors. We further show that this decom-position enables accurate extrapolation of unobserved regions of an application's parameter space. We then employ tensor completion to optimize a CP decomposition given a sparse set of observed exe-cution times. We consider alternative piecewise/grid-based models and supervised learning models for six applications and demon-strate that CP decomposition optimized using tensor completion offers higher prediction accuracy and memory-efficiency for high-dimensional performance modeling.

Original languageEnglish (US)
Title of host publicationSC '23: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis
PublisherAssociation for Computing Machinery
ISBN (Electronic)9798400701092
DOIs
StatePublished - Nov 12 2023
Event2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023 - Denver, United States
Duration: Nov 12 2023Nov 17 2023

Publication series

NameInternational Conference for High Performance Computing, Networking, Storage and Analysis, SC
ISSN (Print)2167-4329
ISSN (Electronic)2167-4337

Conference

Conference2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023
Country/TerritoryUnited States
CityDenver
Period11/12/2311/17/23

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Software

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