Performance Estimation for Exascale Reconfigurable Dataflow Platforms

Ryota Yasudo, Jose Coutinho, Ana Varbanescu, Wayne Luk, Hideharu Amano, Tobias Becker

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

The next generation high-performance computing platforms will need to support exascale computing. A promising path in achieving exascale is to embrace heterogeneity and specialised computing in the form of reconfigurable accelerators. However, assessing the feasibility of heterogeneous exascale systems requires fast and accurate performance prediction. This paper proposes PERKS, a novel performance estimation frame-work for reconfigurable dataflow platforms (RDPs). PERKS uses machine and application parameters to build an analytical model for predicting the performance of multi-Accelerator systems. Moreover, model calibration is automatic, making the model flexible and usable for different machine configurations and applications. Our experimental results demonstrate that PERKS can predict the performance of current workloads and RDPs with an accuracy above 95%. We also demonstrate how the modelling scales to exascale workloads and exascale platforms.

本文言語English
ホスト出版物のタイトルProceedings - 2018 International Conference on Field-Programmable Technology, FPT 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ317-320
ページ数4
ISBN(電子版)9781728102139
DOI
出版ステータスPublished - 2018 12
イベント17th International Conference on Field-Programmable Technology, FPT 2018 - Naha, Okinawa, Japan
継続期間: 2018 12 102018 12 14

出版物シリーズ

名前Proceedings - 2018 International Conference on Field-Programmable Technology, FPT 2018

Conference

Conference17th International Conference on Field-Programmable Technology, FPT 2018
CountryJapan
CityNaha, Okinawa
Period18/12/1018/12/14

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Hardware and Architecture

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