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Automated Performance Prediction of Message Passing Parallel ProgramsAs the trend toward massively parallel processing continues, the need for tools that can predict scalability trends becomes greater. While high level languages Eke HPF have come into greater use, explicit message-passing programs proliferate, and will probably do so for some time, thanks to the onslaught of standards such as MPI. Much work has been done to create simple models that represent important characteristics of parallel programs, such as latency, network contention, and communication volume. But many of these methods still require a substantial manual effort to represent an application in the model's format. The YAPP ("Yet Another Performance Predictor") tool is an attempt to automate the formation of first-order expressions for completion time, with a minimum of programmer assistance. The content of this paper is as follows: First, we explore the implementation details of YAPP, and illustrate with examples some of the reasons that automatic prediction is difficult. In the following sections, we present the results of four applications, using execution traces on the Intel i860, analyze the error in YAPP's predictions, explain the limitations of our implementation, and mention possible future additions. In particular, we illustrate techniques used to identify pipeline communication patterns, and demonstrate how compiler analysis and regression are combined to automate the prediction process.
Document ID
20020009755
Acquisition Source
Ames Research Center
Document Type
Other
Authors
Block, Robert J.
(Illinois Univ. Urbana, IL United States)
Mehra, Pankaj
(RECOM Technologies, Inc. Moffett Field, CA United States)
Sarukkai, Sekhar
(RECOM Technologies, Inc. Moffett Field, CA United States)
Woodrow, Thomas
Date Acquired
August 20, 2013
Publication Date
January 1, 1994
Subject Category
Computer Programming And Software
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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