An application-aware data replacement policy for interactive large-scale scientific visualization

Lina Yu, Hongfeng Yu, Hong Jiang, Jun Wang

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

1 Scopus citations

Abstract

The unprecedented amounts of data generated from large scientific simulations impose a grand challenge in data analytics, and I/O simply becomes a major performance bottleneck. To address this challenge, we present an application-aware I/O optimization technique in support of interactive large-scale scientific visualization. We partition a scientific data into blocks, and carefully place data blocks in a memory hierarchy according to a characterization of data access patterns of user visualization operations. We conduct an empirical study to explore the parameter space to derive optimal solutions. We use real-world large-scale simulation datasets to demonstrate the effectiveness of our approach.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1216-1225
Number of pages10
ISBN (Electronic)9781538634080
DOIs
Publication statusPublished - Jun 30 2017
Event31st IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017 - Orlando, United States
Duration: May 29 2017Jun 2 2017

Publication series

NameProceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017

Other

Other31st IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017
CountryUnited States
CityOrlando
Period5/29/176/2/17

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Keywords

  • I/O optimization
  • data replacement
  • large-scale data
  • scientific visualization

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications
  • Information Systems

Cite this

Yu, L., Yu, H., Jiang, H., & Wang, J. (2017). An application-aware data replacement policy for interactive large-scale scientific visualization. In Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017 (pp. 1216-1225). [7965175] (Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IPDPSW.2017.16