@inproceedings{be170cd1cb7d4a4eb0f441f21721364e,
title = "An application-aware data replacement policy for interactive large-scale scientific visualization",
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.",
keywords = "I/O optimization, data replacement, large-scale data, scientific visualization",
author = "Lina Yu and Hongfeng Yu and Hong Jiang and Jun Wang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 31st IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017 ; Conference date: 29-05-2017 Through 02-06-2017",
year = "2017",
month = jun,
day = "30",
doi = "10.1109/IPDPSW.2017.16",
language = "English (US)",
series = "Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1216--1225",
booktitle = "Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017",
}