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A Performance and Scalability Analysis of the BlueGene/L Architecture

Authors:

Fabrizio PetriniAuthors Info & Claims

SC '04: Proceedings of the 2004 ACM/IEEE conference on Supercomputing

Page 41

https://doi.org/10.1109/SC.2004.8

Published: 06 November 2004 Publication History

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Abstract

Based on a set of measurements done on the 512-node 500MHz prototype and early results on a 2048 node 700MHz BlueGene/L machine at IBM Watson, we present a performance and scalability analysis of the architecture from low-level characteristics to large-scale applications. In addition, we present predictions using our models for the performance of two representative applications from the ASC² workload on the full BlueGene/L configuration of 64K nodes. We have compared the measured values for several of the benchmarks in our suite against the predicted numbers from our performance models. In general, the error bars were relatively low. A comparison between the performance of BlueGene/L and the ASCI Q, the largest supercomputer in the US, is presented, also based on our predictive performance models.

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2. http://www.llnl.gov/asci/platforms/bluegenel/resources.html

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5. Performance and Scalability Analysis of Teraflop-Scale Parallel Architectures Using Multidimensional Wavefront Applications. Adolfy Hoisie, Olaf Lubeck, Harvey Wasserman, "The International Journal of High Performance Computing Applications, Sage Science Press, Volume 14, Number 4, Winter 2000.

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6. Predictive Performance and Scalability Modeling of a Large-Scale Application, Darren J. Kerbyson, Hank J. Alme, Adolfy Hoisie, Fabrizio Petrini, Harvey J. Wasserman, and Michael Gittings, in Proc. of IEEE/ACM SC2001, Denver, November 2001.

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Chung ISainath TRamabhadran BPicheny MGunnels JAustel VChauhari UKingsbury BDamkroger TDongarra J(2014)Parallel deep neural network training for big data on blue gene/QProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1109/SC.2014.66(745-753)Online publication date: 16-Nov-2014
https://dl.acm.org/doi/10.1109/SC.2014.66
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Published In

SC '04: Proceedings of the 2004 ACM/IEEE conference on Supercomputing

November 2004

724 pages

ISBN:0769521533

General Chair:
Jeff Huskamp

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IEEE Computer Society

United States

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Published: 06 November 2004

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SC '04

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SC '04: International Conference for High Performance Computing, Networking, Storage and Analysis

November 6 - 12, 2004

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SC '04 Paper Acceptance Rate 60 of 200 submissions, 30%;

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

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Chung ISainath TRamabhadran BPicheny MGunnels JAustel VChauhari UKingsbury BDamkroger TDongarra J(2014)Parallel deep neural network training for big data on blue gene/QProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1109/SC.2014.66(745-753)Online publication date: 16-Nov-2014
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Holder ACarothers C(2011)Investigating the memory characteristics of a massively parallel time warp kernelProceedings of the Winter Simulation Conference10.5555/2431518.2431869(2953-2964)Online publication date: 11-Dec-2011
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Gong CLiu JGong ZQin JXie J(2010)Optimizing sweep3d for graphic processor unitProceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I10.1007/978-3-642-13119-6_36(416-426)Online publication date: 21-May-2010
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Optimization of MPI collective communication on BlueGene/L systems

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Optimization of MPI collective communication on BlueGene/L systems

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Performance and scalability of MPI on PC clusters: Performances

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