Article

Functional Partitioning to Optimize End-to-End Performance on Many-core Architectures

Authors:

Sudharshan S. Vazhkudai,

Christian Engelmann,

Galen ShipmanAuthors Info & Claims

SC '10: Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis

Pages 1 - 12

https://doi.org/10.1109/SC.2010.28

Published: 13 November 2010 Publication History

Abstract

Scaling computations on emerging massive-core supercomputers is a daunting task, which coupled with the significantly lagging system I/O capabilities exacerbates applications' end-to-end performance. The I/O bottleneck often negates potential performance benefits of assigning additional compute cores to an application. In this paper, we address this issue via a novel functional partitioning (FP) runtime environment that allocates cores to specific application tasks -- checkpointing, de-duplication, and scientific data format transformation -- so that the deluge of cores can be brought to bear on the entire gamut of application activities. The focus is on utilizing the extra cores to support HPC application I/O activities and also leverage solid-state disks in this context. For example, our evaluation shows that dedicating 1 core on an oct-core machine for checkpointing and its assist tasks using FP can improve overall execution time of a FLASH benchmark on 80 and 160 cores by 43.95% and 41.34%, respectively.

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cover image ACM Conferences

SC '10: Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis

November 2010

634 pages

ISBN:9781424475599

Conference Chair:
Barry V. Hess

Sponsors

SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE-CS: Computer Society

Publisher

IEEE Computer Society

United States

Publication History

Published: 13 November 2010

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SC '10

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IEEE-CS

SC '10: International Conference for High Performance Computing, Networking, Storage and Analysis

November 13 - 19, 2010

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SC '10 Paper Acceptance Rate 51 of 253 submissions, 20%;

Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

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