Improving performance of iterative methods by lossy checkponting

Dingwen Tao, Sheng Di, Xin Liang, Zizhong Chen, Franck Cappello

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

30 Scopus citations

Abstract

Iterative methods are commonly used approaches to solve large, sparse linear systems, which are fundamental operations for many modern scientific simulations. When the large-scale iterative methods are running with a large number of ranks in parallel, they have to checkpoint the dynamic variables periodically in case of unavoidable fail-stop errors, requiring fast I/O systems and large storage space. To this end, significantly reducing the checkpointing overhead is critical to improving the overall performance of iterative methods. Our contribution is fourfold. (1) We propose a novel lossy checkpointing scheme that can significantly improve the checkpointing performance of iterative methods by leveraging lossy compressors. (2) We formulate a lossy checkpointing performance model and derive theoretically an upper bound for the extra number of iterations caused by the distortion of data in lossy checkpoints, in order to guarantee the performance improvement under the lossy checkpointing scheme. (3) We analyze the impact of lossy checkpointing (i.e., extra number of iterations caused by lossy checkpointing files) for multiple types of iterative methods. (4) We evaluate the lossy checkpointing scheme with optimal checkpointing intervals on a high-performance computing environment with 2,048 cores, using a well-known scientific computation package PETSc and a state-of-the-art checkpoint/restart toolkit. Experiments show that our optimized lossy checkpointing scheme can significantly reduce the fault tolerance overhead for iterative methods by 23%∼70% compared with traditional checkpointing and 20%∼58% compared with lossless-compressed checkpointing, in the presence of system failures.

Original languageEnglish
Title of host publicationHPDC 2018 - Proceedings of the 2018 International Symposium on High-Performance Parallel and Distributed Computing
Pages52-65
Number of pages14
ISBN (Electronic)9781450357852
DOIs
StatePublished - Jun 11 2018
Event27th ACM International Symposium on High-Performance Parallel and Distributed Computing, HPDC 2018 - Tempe, United States
Duration: Jun 11 2018Jun 15 2018

Publication series

NameHPDC 2018 - Proceedings of the 2018 International Symposium on High-Performance Parallel and Distributed Computing

Conference

Conference27th ACM International Symposium on High-Performance Parallel and Distributed Computing, HPDC 2018
Country/TerritoryUnited States
CityTempe
Period6/11/186/15/18

Bibliographical note

Publisher Copyright:
© 2018 Association for Computing Machinery.

Keywords

  • Checkpoint/Restart
  • Iterative Methods
  • Lossy Compression
  • Numerical Linear Algebra
  • Performance Optimization
  • Resilience

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software

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