Paper Info
Title
Online data analysis and reduction: An important Co-design motif for extreme-scale computers
Abstract
A growing disparity between supercomputer computation speeds and I/O rates means that it is rapidly becoming infeasible to analyze supercomputer application output only after that output has been written to a file system. Instead, data-generating applications must run concurrently with data reduction and/or analysis operations, with which they exchange information via high-speed methods such as interprocess communications. The resulting parallel computing motif, online data analysis and reduction (ODAR), has important implications for both application and HPC systems design. Here we introduce the ODAR motif and its co-design concerns, describe a co-design process for identifying and addressing those concerns, present tools that assist in the co-design process, and present case studies to illustrate the use of the process and tools in practical settings.
Year
DOI
Venue
2021
10.1177/10943420211023549
INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS
Keywords
DocType
Volume
Data analysis, in situ, exascale computing, online data analysis and reduction
Journal
35
Issue
ISSN
Citations 
6
1094-3420
1
PageRank 
References 
Authors
0.35
0
28
Name
Order
Citations
PageRank
Foster Ian1229382663.24
Mark Ainsworth211220.59
Julie Bessac310.35
Franck Cappello43775251.47
Jong Choi5144.62
Sheng Di673755.88
Zichao Di710.35
Ali M Gok810.35
Hanqi Guo933823.06
Kevin A Huck1010.35
Christopher Kelly1110.35
S. Klasky128212.77
Kerstin Kleese van Dam1391.64
Xin Liang1442.07
Kshitij Mehta15115.58
Manish Parashar163876343.30
Tom Peterka1753149.78
Line Pouchard1810.35
Tong Shu1910.35
Ozan Tugluk20203.04
Hubertus van Dam2110.35
Lipeng Wan2253.79
Matthew Wolf2310.35
Justin M. Wozniak2446435.32
W. Xu253015.94
Igor Yakushin2611.70
Shinjae Yoo2717720.65
Todd S. Munson2824530.95