Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales

Date

2017-01-01

Author

Foster, Ian
Ainsworth, Mark
Allen, Bryce
Bessac, Julie
Cappello, Franck
Choi, Jong Youl
Constantinescu, Emil
Davis, Philip E.
Di, Sheng
Di, Wendy
Guo, Hanqi
Klasky, Scott
Van Dam, Kerstin Kleese
Kurc, Tahsin
Liu, Qing
Malik, Abid
Mehta, Kshitij
Mueller, Klaus
Munson, Todd
Ostouchov, George
Parashar, Manish
Peterka, Tom
Pouchard, Line
Tao, Dingwen
Tuğluk, Ozan
Wild, Stefan
Wolf, Matthew
Wozniak, Justin M.
Xu, Wei
Yoo, Shinjae

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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A growing disparity between supercomputer computation speeds and I/O rates makes it increasingly infeasible for applications to save all results for offline analysis. Instead, applications must analyze and reduce data online so as to output only those results needed to answer target scientific question(s). This change in focus complicates application and experiment design and introduces algorithmic, implementation, and programming model challenges that are unfamiliar to many scientists and that have major implications for the design of various elements of supercomputer systems. We review these challenges and describe methods and tools that we are developing to enable experimental exploration of algorithmic, software, and system design alternatives.

URI

https://hdl.handle.net/11511/116501

Collections

Graduate School of Applied Mathematics, Conference / Seminar