Data and workload distribution in a multithreaded architecture

Document Type

Article

Publication Date

2-1-1997

Abstract

Matching data distribution to workload distribution is important in improving the performance of distributed-memory multiprocessors. While data and workload distribution can be tailored to fit a particular problem to a particular distributed-memory architecture, it is often difficult to do so for various reasons including complexity of address computation, runtime data movement, and irregular resource usage. This report presents our study on multithreading for distributed-memory multiprocessors. Specifically, we investigate the effects of multithreading ondatadistribution andworkloaddistribution withvariable, thread granularity. Various types of workload distribution strategies are defined along with thread granularity. Several types of data distribution strategies are investigated. These include row-wise cyclic,k-way partial-row cyclic, and blocked distribution. To investigate the performance of multithreading, two problems are selected: highly sequential Gaussian elimination with partial pivoting and highly parallel matrix multiplication. Execution results on the 80-processor EM-4 distributed-memory multiprocessor indicate that multithreading can off set the loss due to the mismatch between data distribution and workload distribution even for sequential and irregular problems while giving high absolute performance. © 1997 Academic Press.

Identifier

0031068502 (Scopus)

Publication Title

Journal of Parallel and Distributed Computing

External Full Text Location

https://doi.org/10.1006/jpdc.1996.1262

ISSN

07437315

First Page

256

Last Page

264

Issue

2

Volume

40

Grant

JOVE NAG8 1114-2

Fund Ref

National Aeronautics and Space Administration

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