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Optimization of Shared High-Performance Reconfigurable Computing Resources

Optimization of Shared High-Performance Reconfigurable Computing Resources Optimization of Shared High-Performance Recon gurable Computing Resources MELISSA C. SMITH, Clemson University GREGORY D. PETERSON, University of Tennessee In the eld of high-performance computing, systems harboring recon gurable devices, such as eldprogrammable gate arrays (FPGAs), are gaining more widespread interest. Such systems range from supercomputers with tightly coupled recon gurable hardware to clusters with recon gurable devices at each node. The use of these architectures for scienti c computing provides an alternative for computationally demanding problems and has advantages in metrics, such as operating cost/performance and power/performance. However, performance optimization of these systems can be challenging even with knowledge of the system ™s characteristics. Our analytic performance model includes parameters representing the recon gurable hardware, application load imbalance across the nodes, background user load, basic message-passing communication, and processor heterogeneity. In this article, we provide an overview of the analytical model and demonstrate its application for optimization and scheduling of high-performance recon gurable computing (HPRC) resources. We examine cost functions for minimum runtime and other optimization problems commonly found in shared computing resources. Finally, we discuss additional scheduling issues and other potential applications of the model. Categories and Subject Descriptors: C.4 [Computer Systems Organization]: Performance of Systems http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Embedded Computing Systems (TECS) Association for Computing Machinery

Optimization of Shared High-Performance Reconfigurable Computing Resources

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2012 by ACM Inc.
ISSN
1539-9087
DOI
10.1145/2220336.2220348
Publisher site
See Article on Publisher Site

Abstract

Optimization of Shared High-Performance Recon gurable Computing Resources MELISSA C. SMITH, Clemson University GREGORY D. PETERSON, University of Tennessee In the eld of high-performance computing, systems harboring recon gurable devices, such as eldprogrammable gate arrays (FPGAs), are gaining more widespread interest. Such systems range from supercomputers with tightly coupled recon gurable hardware to clusters with recon gurable devices at each node. The use of these architectures for scienti c computing provides an alternative for computationally demanding problems and has advantages in metrics, such as operating cost/performance and power/performance. However, performance optimization of these systems can be challenging even with knowledge of the system ™s characteristics. Our analytic performance model includes parameters representing the recon gurable hardware, application load imbalance across the nodes, background user load, basic message-passing communication, and processor heterogeneity. In this article, we provide an overview of the analytical model and demonstrate its application for optimization and scheduling of high-performance recon gurable computing (HPRC) resources. We examine cost functions for minimum runtime and other optimization problems commonly found in shared computing resources. Finally, we discuss additional scheduling issues and other potential applications of the model. Categories and Subject Descriptors: C.4 [Computer Systems Organization]: Performance of Systems

Journal

ACM Transactions on Embedded Computing Systems (TECS)Association for Computing Machinery

Published: Jul 1, 2012

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