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Exploring Application Performance on Emerging Hybrid-Memory Supercomputers
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
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2017 (English)In: Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016, Institute of Electrical and Electronics Engineers (IEEE), 2017, 473-480 p., 7828415Conference paper (Refereed)
Abstract [en]

Next-generation supercomputers will feature more hierarchical and heterogeneous memory systems with different memory technologies working side-by-side. A critical question is whether at large scale existing HPC applications and emerging data-analytics workloads will have performance improvement or degradation on these systems. We propose a systematic and fair methodology to identify the trend of application performance on emerging hybrid-memory systems. We model the memory system of next-generation supercomputers as a combination of 'fast' and 'slow' memories. We then analyze performance and dynamic execution characteristics of a variety of workloads, from traditional scientific applications to emerging data analytics to compare traditional and hybrid-memory systems. Our results show that data analytics applications can clearly benefit from the new system design, especially at large scale. Moreover, hybrid-memory systems do not penalize traditional scientific applications, which may also show performance improvement.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. 473-480 p., 7828415
Keyword [en]
Hybrid-memory system, Large-scale applications, Performance characterization
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:kth:diva-208452DOI: 10.1109/HPCC-SmartCity-DSS.2016.0074ISI: 000401700900063ScopusID: 2-s2.0-85013674475ISBN: 9781509042968 OAI: oai:DiVA.org:kth-208452DiVA: diva2:1107176
Conference
18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016, Sydney, Australia, 12 December 2016 through 14 December 2016
Note

QC 20170609

Available from: 2017-06-09 Created: 2017-06-09 Last updated: 2017-06-20Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • modern-language-association-8th-edition
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  • de-DE
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