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Title:Optimization of communication intensive applications on HPC networks
Author(s):Jain, Nikhil
Director of Research:Kale, Laxmikant V.
Doctoral Committee Chair(s):Kale, Laxmikant V.
Doctoral Committee Member(s):Gropp, William D.; Torrellas, Josep; Panda, Dhabaleswar K.
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Network
Communication
Parallel computing
Applications
Simulation
Modeling
Prediction
Machine learning
Topology-aware mapping
Abstract:Communication is a necessary but overhead inducing component of parallel programming. Its impact on application design and performance is due to several related aspects of a parallel job execution: network topology, routing protocol, suitability of algorithm being used to the network, job placement, etc. This thesis is aimed at developing an understanding of how communication plays out on networks of high performance computing systems and exploring methods that can be used to improve communication performance of large scale applications. Broadly speaking, three topics have been studied in detail in this thesis. The first of these topics is task mapping and job placement on practical installations of torus and dragonfly networks. Next, use of supervised learning algorithms for conducting diagnostic studies of how communication evolves on networks is explored. Finally, efficacy of packet-level simulations for prediction-based studies of communication performance on different networks using different network parameters is analyzed. The primary contribution of this thesis is development of scalable diagnostic and prediction methods that can assist in the process of network designing, adapting applications to future systems, and optimizing execution of applications on existing systems. These meth- ods include a supervised learning approach, a functional modeling tool (called Damselfly), and a PDES-based packet level simulator (called TraceR), all of which are described in this thesis.
Issue Date:2016-02-12
Type:Thesis
URI:http://hdl.handle.net/2142/90472
Rights Information:Copyright 2016, Nikhil Jain.
Date Available in IDEALS:2016-07-07
Date Deposited:2016-05


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