Accelerating Big Data Infrastructure and Applications (Ongoing Collaboration)

Kevin Brown, Tianqi Xu, Keita Iwabuchi, Kento Sato, Adam Moody, Kathryn Mohror, Nikhil Jain, Abhinav Bhatele, Martin Schulz, Roger Pearce, Maya Gokhale, Satoshi Matsuoka

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

2 Zitate (Scopus)

Abstract

High-performance computing (HPC) systems are increasingly being used for data-intensive, or 'Big Data', workloads. However, since traditional HPC workloads are compute-intensive, the HPC-Big Data convergence has created many challenges with optimizing data movement and processing on modern supercomputers. Our collaborative work addresses these challenges using a three-pronged approach: (i) measuring and modeling extreme-scale I/O workloads, (ii) designing a low-latency, scalable, on-demand burst-buffer solution, and (iii) optimizing graph algorithms for processing Big Data workloads. We describe the three areas of our collaboration and report on their respective developments.

OriginalspracheEnglisch
TitelProceedings - IEEE 37th International Conference on Distributed Computing Systems Workshops, ICDCSW 2017
Redakteure/-innenJoao E. Ferreira, Teruo Higashino, Aibek Musaev
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten343-347
Seitenumfang5
ISBN (elektronisch)9781538632925
DOIs
PublikationsstatusVeröffentlicht - 13 Juli 2017
Extern publiziertJa
Veranstaltung37th IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2017 - Atlanta, USA/Vereinigte Staaten
Dauer: 5 Juni 20178 Juni 2017

Publikationsreihe

NameProceedings - IEEE 37th International Conference on Distributed Computing Systems Workshops, ICDCSW 2017

Konferenz

Konferenz37th IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2017
Land/GebietUSA/Vereinigte Staaten
OrtAtlanta
Zeitraum5/06/178/06/17

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