Dissertation Defense - University of Houston
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Dissertation Defense

In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

Shweta Jha

will defend her dissertation

Performance Tuning and Modeling of Communication in Parallel Applications


The goal of high performance computing is executing very large problems in least amount of time, typically by deploying parallelization techniques. However, introducing parallelization to an application also introduces synchronization and communication overhead, which in turn creates a performance bottleneck. Performance modeling and tuning can be used to predict and ease this bottleneck to improve the overall performance of the application.

There are two aspects of an application which can be improved from performance point of view, namely, computational section and the communication section.The time spent in communication operations is a major factor in determining the scalability of parallel applications. Tuning the parameters of a communication library can be used to adapt its characteristics to a particular platform, minimizing the communication time of an application. On the other hand performance modeling can be used to predict the performance using the network and application attributes.

The goal of this dissertation is to improve the performance a parallel application by performance tuning and performance modeling. Specifically, we introduce the notion of a personalized MPI library, highlighting the necessity and the methodology according to which each application needs to have a communication library tuned for the particular platform. Secondly, this dissertation contributes towards the theoretical understanding of impact and limitations of point-to-point communication performance on collective communication and the overall application. This study has been further extended to develop performance models for the communication aspect of collective I/O operations for one and two dimensional data decomposition, and for two file partitioning strategies, namely even and static partitioning.

Date: Thursday, April 11, 2017
Time: 11:00 AM 
Place: PGH 501D
Advisor: Dr. Edgar Gabriel

Faculty, students, and the general public are invited.