Newsgroups: comp.parallel
From: kaddoura@top.cis.syr.edu (Maher Nouhad Kaddoura)
Subject: Papers on Data Parallel Computing on Adaptive and Nonuniform Environments
Organization: Syracuse University, CIS Dept.
Date: 28 Feb 1995 21:01:43 GMT
Message-ID: <3j4pb0$rkg@usenet.srv.cis.pitt.edu>

I would like to announce the availability of papers describing 
our work on Data-Parallel Computing on Adaptive and Nonuniform
Environments.  We expect the results to be of interest
to researchers in the development of applications, runtime support,
and compilers for network of workstations.

The abstracts are reproduced below.  Please drop me a note if you
would like to receive a copy of the papers.  The papers are also
available (via Mosaic) at http://www.cis.syr.edu/people/ranka/stance.html

Maher kaddoura

1) Array Decompositions for Nonuniform Computational Environments

Two-dimensional arrays are useful in a large variety of scientific 
and engineering applications. Parallelization of these applications 
requires the decomposition of array elements among different machines.
Several data-decomposition techniques have been studied in the literature 
for machines with uniform computational power. In this paper we develop
new methods for decomposing arrays into a cluster of machines with
nonuniform computational power. Simulation results show that our methods 
provide superior decomposition over naive schemes.

2) Mapping Unstructured Computational Graphs for Adaptive and
Nonuniform Computational Environments

In this paper we study the problem of mapping a large class of
irregular and loosely synchronous data-parallel applications in a
nonuniform and adaptive computational environment. The computational
structure of these applications can be described in terms of a 
computational graph, where nodes of the graph represent computational 
tasks and edges describe the communication between tasks.

Parallelization of these applications on nonuniform computational
environments requires partitioning the graph among the processors in 
such fashion that the computation load on each node is proportional to 
its computational power, while communication is minimized.
We discuss the applicability of current methods for graph partitioning 
for  such environments. For an adaptive computational environment,
the partitioning of the graph needs to be updated as the environment 
adapts, hence most algorithms described in the literature are 
computationally prohibitive.  We discuss novel strategies that allow 
for fast remapping.

3) Runtime Support for Parallelization of Data-Parallel Applications 
   on Adaptive and Nonuniform Computational Environments

In this paper we discuss the runtime support required for the
parallelization of unstructured data-parallel applications on nonuniform
and adaptive environments. The approach presented is reasonably general
and is applicable to a wide variety of regular as well as irregular
applications. We present performance results for the solution of an 
unstructured mesh on a cluster of heterogeneous workstations.

