Performance Prediction for Large-Scale Heterogeneous Platforms

Ryota Yasudo, Ana L. Varbanescu, Jose G.F. Coutinho, Wayne Luk, Hideharu Amano

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

Abstract

This paper presents an approach for analysing, modelling and predicting application performance of large-scale heterogeneous platforms. Our approach combines analytical and statistical modelling techniques, and aims to: (1) identify and characterise code regions that are the most promising candidates to benefit from acceleration; (2) provide statistical models that predict application behaviour for unobserved inputs; and (3) predict performance gain with different system architectures.

Original languageEnglish
Title of host publicationProceedings - 26th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages1
ISBN (Electronic)9781538655221
DOIs
Publication statusPublished - 2018 Sep 7
Event26th Annual IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2018 - Boulder, United States
Duration: 2018 Apr 292018 May 1

Other

Other26th Annual IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2018
CountryUnited States
CityBoulder
Period18/4/2918/5/1

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Keywords

  • Heterogeneous platforms
  • Large-scale distributed systems
  • Performance modelling

ASJC Scopus subject areas

  • Artificial Intelligence
  • Hardware and Architecture
  • Software

Cite this

Yasudo, R., Varbanescu, A. L., Coutinho, J. G. F., Luk, W., & Amano, H. (2018). Performance Prediction for Large-Scale Heterogeneous Platforms. In Proceedings - 26th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2018 [8457669] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FCCM.2018.00054