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Title:Meta-Balancer: automated load balancing based on application behavior
Author(s):Gopalakrishnan Menon, Harshitha Menon
Advisor(s):Kale, Laxmikant V.
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Load Balancing
Abstract:With the dawn of petascale, and with exascale in the near future, it has become significantly difficult to write parallel applications that fully exploit the processing power, and scale to large systems. Load imbalance, both computationally and communication induced, presents itself as one of the important challenges in achieving scalability and high performance. Problem sizes and system sizes have become so large that manually handling the imbalance in dynamic applications, and finding an optimum distribution of load has become a herculean task. Charm++~\cite provides the user with a run time system that performs dynamic load balancing. To enable Charm++ to perform load balancing in an efficient manner, the user takes certain decisions such as when to load balance and which strategy to use, and informs the Charm++ run-time system of these decisions. Many a times, taking these important decisions involve hand tuning each application by observing various runs of the application. In this thesis, a Meta-Balancer which relieves the user from the effort of making the load balancing related decisions, is presented. The Meta-Balancer is a part of the Charm++ load balancing framework. It identifies the characteristics of the application, and based on the principle of persistence and the accrued information, makes load balancing related decisions. We study the performance of the Meta-Balancer in the context of leanmd mini application. We also evaluate the Meta-Balancer in the context of micro benchmarks such as kNeighbor and jacobi2D. We also present several new load balancing strategies, that have been incorporated into Charm++, and study their impact on the performance of applications. These new strategies are: 1)RefineSwapLB, which is a refinement based load balancing strategy, 2)CommAwareRefineLB, which is a communication aware refinement strategy, 3)ScotchRefineLB, which is a refinement based graph partitioning strategy using Scotch, a graph partitioner, and 4) ZoltanLB, which is a multicast aware load balancing strategy using Zoltan, a hypergraph partitioner.
Issue Date:2012-05-22
Rights Information:Copyright 2012 Harshitha Menon Gopalakrishnan Menon
Date Available in IDEALS:2012-05-22
Date Deposited:2012-05

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