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2nd PDSW-DISCS@SC 2017: Denver, CO, USA

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Kathryn M. Mohror, Brent Welch:
Proceedings of the 2nd Joint International Workshop on Parallel Data Storage & Data Intensive Scalable Computing Systems, PDSW-DISCS@SC 2017, Denver, CO, USA, November 13, 2017. ACM 2017, ISBN 978-1-4503-5134-8

Scalability of storage systems

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  - conf/sc/IonkovM017
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Latchesar Ionkov, Carlos Maltzahn, Michael Lang:
Optimized scatter/gather data operations for parallel storage. 1-6
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  - conf/sc/ZhengAKGCSGG17
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Qing Zheng, George Amvrosiadis, Saurabh Kadekodi, Garth A. Gibson, Charles D. Cranor, Bradley W. Settlemyer, Gary Grider, Fan Guo:
Software-defined storage for fast trajectory queries using a deltaFS indexed massive directory. 7-12
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  - conf/sc/DorierDPR17
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Matthieu Dorier, Matthieu Dreher, Tom Peterka, Robert B. Ross:
CoSS: proposing a contract-based storage system for HPC. 13-18

Improving storage system performance

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  - conf/sc/LawsonUMTLLWK17
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Margaret Lawson, Craig D. Ulmer, Shyamali Mukherjee, Gary Templet, Jay F. Lofstead, Scott Levy, Patrick M. Widener, Todd Kordenbrock:
Empress: extensible metadata provider for extreme-scale scientific simulations. 19-24
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  - conf/sc/ShafferT17
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Tim Shaffer, Douglas Thain:
Taming metadata storms in parallel filesystems with metaFS. 25-30
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Tyler Stocksdale, Mu-Tien Chang, Hongzhong Zheng, Frank Mueller:
Architecting HBM as a high bandwidth, high capacity, self-managed last-level cache. 31-36

Understanding I/O performance

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Feiyi Wang, Hyogi Sim, Cameron Harr, Sarp Oral:
Diving into petascale production file systems through large scale profiling and analysis. 37-42
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Christopher S. Daley, Prabhat, Sudip S. Dosanjh, Nicholas J. Wright:
Performance analysis of emerging data analytics and HPC workloads. 43-48
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Arnab Kumar Paul, Steven Tuecke, Ryan Chard, Ali Raza Butt, Kyle Chard, Ian T. Foster:
Toward scalable monitoring on large-scale storage for software defined cyberinfrastructure. 49-54
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Glenn K. Lockwood, Wucherl Yoo, Suren Byna, Nicholas J. Wright, Shane Snyder, Kevin Harms, Zachary Nault, Philip H. Carns:
UMAMI: a recipe for generating meaningful metrics through holistic I/O performance analysis. 55-60

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