Newsgroups: comp.parallel
From: bart@cs.wisc.edu (Barton Miller)
Subject: New parallel tools tech report
Organization: U of Wisconsin CS Dept
Date: Mon, 27 Nov 1995 16:07:14 GMT
Message-ID: <49cnni$3hr@spool.cs.wisc.edu>

A new tech report is available from the Paradyn Parallel Performance Tools
project.  This report describes our recent work with tools for high-level
parallel languages.  You can fetch the paper at:

	ftp://grilled.cs.wisc.edu/technical_papers/nv2.ps.Z

Our project home page is:

	http://cs.wisc.edu/~paradyn

The abstract for the report follows:
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    Mapping Performance Data for High-Level and Data Views of Parallel Program
    Performance

    R. Bruce Irvin      Barton P. Miller
    rbi@informix.com    bart@cs.wisc.edu

    ABSTRACT

    Programs written in high-level parallel languages need profiling
    tools that provide performance data in terms of the semantics of
    the high-level language. But high-level performance data can be
    incomplete when the cause of a performance problem cannot be
    explained in terms of the semantics of the language. We also need
    the ability to view the performance of the underlying mechanisms
    used by the language and correlate the underlying activity to the
    language source code. The key techniques for providing these
    performance views is the ability to map low-level performance
    data up to the language abstractions.

    We identify the various kinds of mapping information that needs
    to be gathered to support multiple views of performance data and
    describe how we can mine mapping information from the compiler
    and run-time environment. We also describe how we use this
    information to produce performance data at the higher levels, and
    how we present this data in terms of both the code and parallel
    data structures.

    We have developed an implementation of these mapping techniques
    for the data parallel CM Fortran language running on the TMC CM-
    5. We have augmented the Paradyn Parallel Performance Tools with
    these mapping and high-level language facilities and used them to
    study several real data parallel Fortran (CM Fortran)
    applications. Our mapping and high-level language techniques
    allowed us to quickly understand these applications and modify
    them to obtain significant performance improvements.
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