Conference proceeding
An application-aware data replacement policy for interactive large-scale scientific visualization
2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp.1216-1225
05/2017
DOI: 10.1109/IPDPSW.2017.16
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.
Details
- Title: Subtitle
- An application-aware data replacement policy for interactive large-scale scientific visualization
- Creators
- Lina Yu - Univ. of Nebraska-Lincoln, Lincoln, NE, USAHongfeng Yu - Univ. of Nebraska-Lincoln, Lincoln, NE, USAHong Jiang - Univ. of Texas at Arlington, Arlington, TX, USAJun Wang - Univ. of Iowa, Iowa City, IA, USA
- Resource Type
- Conference proceeding
- Publication Details
- 2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp.1216-1225
- DOI
- 10.1109/IPDPSW.2017.16
- Publisher
- IEEE
- Language
- English
- Date published
- 05/2017
- Academic Unit
- Electrical and Computer Engineering; Civil and Environmental Engineering; Physics and Astronomy; Chemical and Biochemical Engineering
- Record Identifier
- 9984106174202771
Metrics
28 Record Views