Title | ||
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UMAMI: a recipe for generating meaningful metrics through holistic I/O performance analysis. |
Abstract | ||
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I/O efficiency is essential to productivity in scientific computing, especially as many scientific domains become more data-intensive. Many characterization tools have been used to elucidate specific aspects of parallel I/O performance, but analyzing components of complex I/O subsystems in isolation fails to provide insight into critical questions: how do the I/O components interact, what are reasonable expectations for application performance, and what are the underlying causes of I/O performance problems? To address these questions while capitalizing on existing component-level characterization tools, we propose an approach that combines on-demand, modular synthesis of I/O characterization data into a unified monitoring and metrics interface (UMAMI) to provide a normalized, holistic view of I/O behavior.
We evaluate the feasibility of this approach by applying it to a month-long benchmarking study on two distinct large-scale computing platforms. We present three case studies that highlight the importance of analyzing application I/O performance in context with both contemporaneous and historical component metrics, and we provide new insights into the factors affecting I/O performance. By demonstrating the generality of our approach, we lay the groundwork for a production-grade framework for holistic I/O analysis.
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Year | DOI | Venue |
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2017 | 10.1145/3149393.3149395 | SC '17: The International Conference for High Performance Computing, Networking, Storage and Analysis
Denver
Colorado
November, 2017 |
DocType | ISBN | Citations |
Conference | 978-1-4503-5134-8 | 4 |
PageRank | References | Authors |
0.44 | 0 | 8 |
Name | Order | Citations | PageRank |
---|---|---|---|
Glenn K. Lockwood | 1 | 8 | 1.85 |
Wucherl Yoo | 2 | 24 | 2.59 |
Suren Byna | 3 | 24 | 10.25 |
Nicholas J. Wright | 4 | 408 | 27.79 |
Shane Snyder | 5 | 64 | 8.38 |
Kevin Harms | 6 | 195 | 13.06 |
Zachary Nault | 7 | 4 | 0.44 |
Philip H. Carns | 8 | 964 | 62.51 |