Newsgroups: comp.parallel,comp.sys.super
From: dbader@Glue.umd.edu (David Bader)
Subject: ANNOUNCEMENT: PRACTICAL PARALLEL ALGORITHMS FOR DYNAMIC DATA REDISTRIBUTION, MEDIAN FINDING, AND SELECTION
Organization: Institute for Advanced Computer Studies (UMIACS), Univ of Maryland, College Park
Date: 6 Jul 1995 10:46:20 -0400
Message-ID: <3tgsvs$3hf@mocha.eng.umd.edu>

ANNOUNCEMENT:

--------------------------------------------------------------------
Practical Parallel Algorithms for Dynamic Data Redistribution, 
                                  Median Finding, and Selection
--------------------------------------------------------------------

We have released our technical report entitled ``Practical Parallel
Algorithms for Dynamic Data Redistribution, Median Finding, and
Selection,'' by David A. Bader and Joseph Ja'Ja'. Technical Report
Number: CS-TR-3494 and UMIACS-TR-95-74. Institute for Advanced
Computer Studies (UMIACS), and the Department of Electrical
Engineering, University of Maryland, College Park, July 1995.

The paper is available in HTML and PostScript format via WWW:

http://www.umiacs.umd.edu/~dbader

or via anonymous ftp to:

ftp://ftp.cs.umd.edu/pub/papers/papers/3494/3494.ps.Z

If you prefer a hardcopy, please reply to this message and send me
your mailing address.

ABSTRACT:
   A common statistical problem is that of finding the median element
in a set of data. This paper presents a fast and portable parallel
algorithm for finding the median given a set of elements distributed
across a parallel machine. In fact, our algorithm solves the general
selection problem that requires the determination of the element of
rank $i$, for an arbitrarily given integer $i$. Practical algorithms
needed by our selection algorithm for the dynamic redistribution of
data are also discussed. Our general framework is a single-address
space, distributed memory programming model that is enhanced by a set
of communication primitives. We use efficient techniques for
distributing, coalescing, and load balancing data as well as efficient
combinations of task and data parallelism. The algorithms have been
coded in Split-C and run on a variety of platforms, including the
Thinking Machines CM-5, IBM SP-1 and SP-2, Cray Research T3D, Meiko
Scientific CS-2, Intel Paragon, and workstation clusters. Our
experimental results illustrate the scalability and efficiency of our
algorithms across different platforms and improve upon all the related
experimental results known to the authors. In particular, our portable
selection code beats the reported machine-specific NAS IS benchmarks
on the CM-5, SP-2, and T3D. More efficient implementations of the
communication primitives will likely result in even faster execution
times.

---
David A. Bader 
Electrical Engineering Department
A.V. Williams Building
University of Maryland
College Park, MD 20742
Office: 301-405-6755   
FAX:    301-314-9658

Internet: dbader@eng.umd.edu
WWW:      http://www.umiacs.umd.edu/~dbader




