Scalable and Fast Lazy Persistency on GPUs

Ardhi Wiratama Baskara Yudha, Keiji Kimura, Huiyang Zhou, Yan Solihin

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

GPUs applications, including many scientific and machine learning applications, increasingly demand larger memory capacity. NVM is promising higher density compared to DRAM and better future scaling potentials. Long running GPU applications can benefit from NVM by exploiting its persistency, allowing crash recovery of data in memory. In this paper, we propose mapping Lazy Persistency (LP) to GPUs and identify the design space of such mapping. We then characterize LP performance on GPUs, varying the checksum type, reduction method, use of locking, and hash table designs. Armed with insights into the performance bottlenecks, we propose a hash table-less method that performs well on hundreds and thousands of threads, achieving persistency with nearly negligible (2.1%) slowdown for a variety of representative benchmarks. We also propose a directive-based programming language support to simplify programming effort for adding LP to GPU applications.

本文言語English
ホスト出版物のタイトルProceedings - 2020 IEEE International Symposium on Workload Characterization, IISWC 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ252-263
ページ数12
ISBN(電子版)9781728176451
DOI
出版ステータスPublished - 2020 10
イベント16th IEEE International Symposium on Workload Characterization, IISWC 2020 - Virtual, Beijing, China
継続期間: 2020 10 272020 10 29

出版物シリーズ

名前Proceedings - 2020 IEEE International Symposium on Workload Characterization, IISWC 2020

Conference

Conference16th IEEE International Symposium on Workload Characterization, IISWC 2020
国/地域China
CityVirtual, Beijing
Period20/10/2720/10/29

ASJC Scopus subject areas

  • ハードウェアとアーキテクチャ
  • 情報システムおよび情報管理

フィンガープリント

「Scalable and Fast Lazy Persistency on GPUs」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル