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Title:Intelligent runtime tuning of parallel applications with control points
Author(s):Dooley, Isaac J.
Director of Research:Kale, Laxmikant V.
Doctoral Committee Chair(s):Kale, Laxmikant V.
Doctoral Committee Member(s):Heath, Michael T.; Zilles, Craig; Jefferson, David
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Parallel Programming
Automatic Performance Tuning
Control Points
Performance Tuning
Distributed-memory parallel programming
Abstract:The tuning of parallel programs on large distributed-memory machines today is usually a costly, and often extensive, manual process. Automatic tuning techniques can help reduce this manual burden. This dissertation investigates the utility of a new class of automatic tuning methods for large-scale parallel programs whereby each program exposes information about its behavior to the runtime system. This behavioral information enables a tuning framework to quickly find appropriate ways to reconfigure or steer the application towards better performance. This dissertation describes both new automatic tuning mechanisms within a parallel runtime system, and a new framework that automatically reconfigures the behavior or structure of the program through one or more control points. Control points are a novel type of tunable parameter provided by an application wherein it exposes tunable knobs and information about the behavioral effects expected to occur as each knob is varied in each direction. This behavioral information associated with each control point allows tuning algorithms to identify the direction in which a control point should be adjusted to fix observed performance problems. Multiple application case studies show that control points are useful mechanisms for dynamically reconfiguring applications to improve their performance. In these case studies, individual control points are examined to investigate how they can adjust diverse application behaviors including computational grain sizes, the amount of work offloaded to accelerators, the mapping of tasks to processors, the frequency of load balancing, and a communication throttling parameter.
Issue Date:2011-01-14
Rights Information:Copyright 2010 Isaac Dooley. Portions copyright 2010 IEEE. Reprinted, with permission, from Proceedings of 17th Annual International Conference on High Performance Computing (HiPC), and 12th Workshop on Advances in Parallel and Distributed Computing Models (APDCM) This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the products or services of the University of Illinois at Urbana Champaign. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to choosing to view this material, you agree to all provisions of the copyright laws protecting it.
Date Available in IDEALS:2011-01-14
Date Deposited:December 2

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