Dissertation/Thesis Abstract

Improving Scalability of Parallel Unstructured Mesh-Based Adaptive Workflows
by Smith, Cameron Walter, Ph.D., Rensselaer Polytechnic Institute, 2017, 124; 10269567
Abstract (Summary)

High performance parallel adaptive simulations operating on leadership class systems are constructed from multiple pieces of software developed over many years. As increasingly complex systems are deployed new methods must be created to extract performance and scalability. This thesis addresses two key scalability limitations for unstructured mesh based simulations.

Attaining simulation performance at ever higher concurrency levels requires increased performance of transformations within each procedure, as well as the transfer of data between procedures.

Controlling the transformations requires distributing the work evenly across the processors while executing efficient data transfers requires local operations that avoid shared or contended resources. This thesis addresses these requirements through multi-criteria load balancing procedures and in-memory data transfer techniques.

Partition improvement methods defined in this work enable improved application strong scaling on over one million processors through careful control of the balancing requirements. Applied to a computational fluid dynamics simulation running on 524,288 processes with 1.2 billion elements these methods reduce the time of the dominant computational step by up to 28% versus the best existing methods.

The scalable data transfer requirement is addressed through an in-memory functional coupling that avoids the high cost of fileystem access. The methods developed are applied to two adaptive simulations in which the time required for information exchange is reduced by over an order of magnitude versus file based couplings. Three additional simulations for industrial applications are then provided that highlight an in-memory coupling and the automation of key simulation processes.

Indexing (document details)
Advisor: Shephard, Mark S.
Commitee: Bloomfield, Max O., Carrothers, Christopher D., Cutler, Barbara, Sahni, Onkar
School: Rensselaer Polytechnic Institute
Department: Computer Science
School Location: United States -- New York
Source: DAI-B 78/12(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Engineering, Computer science
Keywords: Adaptive, Mesh-Based, Scalability, Unstructured, Workflows
Publication Number: 10269567
ISBN: 9780355077445
Copyright © 2019 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy
ProQuest