Newsgroups: comp.parallel
From: cwou@top.cis.syr.edu (Chao-Wei Ou)
Subject: Papers on Parallel and Incremental Graph Partitioning
Organization: Syracuse University CIS Dept.
Date: 24 Feb 1995 22:17:04 GMT
Message-ID: <3ivv95$ddg@usenet.srv.cis.pitt.edu>

I would like to announce the availability of papers describing our
work on parallel graph partitioning and incremental graph
partitioning.  We expect the results to be of interest to researchers
in the area of parallel algorithm design, development of irregular
applications on parallel machines as well as network of workstations, 
and compiler developers for data parallel languages.

The abstracts are reproduced below.  Please drop me a note if you
would like to receive a copy of the papers.

Thanks.

Chao-Wei Ou

Email :       cwou@nova.npac.syr.edu
URL :         http://www.npac.syr.edu/users/cwou/homepage/cwou.html

--------------------------------------

TITLE: Architecture-Independent Locality Improving Transformations
     of  Computational Graphs Embedded in k-Dimensions

Chao-Wei Ou, Manoj Gunwani and Sanjay Ranka
Syracuse University

Abstract:

A large number of data-parallel applications can be represented as  
computational graphs from the perspective of parallel computing. The nodes 
of these graphs represent tasks that can be executed concurrently, while 
the edges represent the interactions between them. Further, the 
computational graphs derived from many applications are such that the 
vertices correspond to two- or three-dimensional coordinates, and the 
interaction between computations is limited to vertices that are 
physically proximate.

In this paper we show that  graphs with these properties can be transformed 
into simple architecture-independent representations that encapsulate the 
locality in these graphs. This representation allows a fast mapping of the
computational graph onto the underlying architecture at the time of execution.
This is necessary for environments where available computational
resources can be determined only at the time of execution or that change
during execution.

TITLE: Fast and Parallel Mapping Algorithms For Irregular Problems

Chao-Wei Ou, Sanjay Ranka, and Geoffrey Fox
Syracuse University
Abstract:

In this paper, we develop simple index based graph partitioning techniques.  
We show that our methods are very fast, easily parallelizable and produce 
good quality mappings. These properties  make them useful for parallelization
of a number of irregular and adaptive applications.

TITLE: Parallel Incremental Graph Partitioning Using Linear Programming

Chao-Wei Ou and  Sanjay Ranka
Syracuse University

Abstract:

Partitioning graphs into equally large groups of nodes while minimizing 
the number of edges between different groups is an extremely important 
problem in parallel computing.  For instance,  efficiently parallelizing 
several scientific and engineering applications requires the partitioning 
of data or tasks among processors such that the computational load on each
node is roughly the same, while communication is minimized. Obtaining exact 
solutions is computationally intractable, since graph-partitioning is an 
NP-complete.

For a large class of irregular and adaptive data parallel applications
(such as adaptive meshes), the computational structure changes from one
phase to another in an incremental fashion. In incremental graph-partitioning
problems the partitioning of the graph needs to be updated as the graph
changes over time; a small number of nodes or edges may be added or deleted
at any given instant.

In this paper we use a linear programming-based method to solve the
incremental graph partitioning problem. All the steps used by our method
are inherently parallel and hence our approach can be easily parallelized. 
By using an initial solution for the graph partitions derived from 
recursive spectral bisection-based methods, our methods can achieve 
repartitioning at considerably lower cost than can be obtained by applying 
recursive spectral bisection from scratch. Further, the quality of the 
partitioning achieved is comparable to that achieved by applying recursive
spectral bisection to the incremental graphs from scratch.

TITLE: Parallel Remapping Algorithms for Adaptive Problems

Chao-Wei Ou and  Sanjay Ranka
Syracuse University

Abstract:

In this paper we present fast parallel algorithms for remapping a class 
of irregular and adaptive problems on coarse-grained distributed memory 
machines. We show that the remapping of these applications, using simple 
index-based mapping algorithm, can be reduced to sorting a nearly sorted 
list of integers or merging an unsorted list of integers with a sorted 
list of integers.  By using the algorithms we have developed, the remapping 
of these problems can be achieved at a fraction of the cost of mapping from 
scratch.  Experimental results are presented on the CM-5.

Title: Mapping  Unstructured Computational Graphs for Nonuniform and Adaptive
Computational Environments

Maher Kaddoura, Chao-Wei Ou, and Sanjay Ranka
Syracuse University

Abstract:

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.

