Affiliations: Department of Computer and Information Sciences,
University of Delaware, Newark, DE 19716; e-mail: agrawal@cis.udel.edu | UMIACS and Department of Computer Science, University
of Maryland, College Park, MD 20742; e-mail:
{edjlali,gagan,als,humphrie,saltz}@cs.umd.edu
Abstract: For better utilization of computing resources, it is important to
consider parallel programming environments in which the number of available
processors varies at run-time. In this article, we discuss run-time
support for data-parallel programming in such an adaptive environment.
Executing programs in an adaptive environment requires redistributing data when
the number of processors changes, and also requires determining new loop bounds
and communication patterns for the new set of processors. We have
developed a run-time library to provide this support. We discuss how the
run-time library can be used by compilers of high-performance Fortran
(HPF)-like languages to generate code for an adaptive environment. We present
performance results for a Navier-Stokes solver and a multigrid template run on
a network of workstations and an IBM SP-2. Our experiments show that
if the number of processors is not varied frequently, the cost of data
redistribution is not significant compared to the time required for the actual
computation. Overall, our work establishes the feasibility of
compiling HPF for a network of nondedicated workstations, which are likely to
be an important resource for parallel programming in the future.
