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Erschienen in:
An Introduction to Big Data, High Performance Computing, High-Throughput Computing, and Hadoop Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

3. Data Movement in Data-Intensive High Performance Computing

verfasst von : Pietro Cicotti, Sarp Oral, Gokcen Kestor, Roberto Gioiosa, Shawn Strande, Michela Taufer, James H. Rogers, Hasan Abbasi, Jason Hill, Laura Carrington

Erschienen in: Conquering Big Data with High Performance Computing

Verlag: Springer International Publishing

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Abstract

The cost of executing a floating point operation has been decreasing for decades at a much higher rate than that of moving data. Bandwidth and latency, two key metrics that determine the cost of moving data, have degraded significantly relative to processor cycle time and execution rate. Despite the limitation of sub-micron processor technology and the end of Dennard scaling, this trend will continue in the short-term making data movement a performance-limiting factor and an energy/power efficiency concern. Even more so in the context of large-scale and data-intensive systems and workloads. This chapter gives an overview of the aspects of moving data across a system, from the storage system to the computing system down to the node and processor level, with case study and contributions from researchers at the San Diego Supercomputer Center, the Oak Ridge National Laboratory, the Pacific Northwest National Laboratory, and the University of Delaware.

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Vorheriges Kapitel Using High Performance Computing for Conquering Big Data
Nächstes Kapitel Using Managed High Performance Computing Systems for High-Throughput Computing
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Metadaten
Titel
Data Movement in Data-Intensive High Performance Computing
verfasst von
Pietro Cicotti
Sarp Oral
Gokcen Kestor
Roberto Gioiosa
Shawn Strande
Michela Taufer
James H. Rogers
Hasan Abbasi
Jason Hill
Laura Carrington
Copyright-Jahr
2016
Buch
Conquering Big Data with High Performance Computing

Print ISBN: 978-3-319-33740-1

Electronic ISBN: 978-3-319-33742-5

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-33742-5_3

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