PROBLEM SOLVING ENVIRONMENTS FOR
SCIENTIFIC COMPUTING
John R. Rice
Department of Computer Science
Purdue University
W. Lafayette, IN 47907, USA
A brief overview of the goals and structures of problem
solving environments (PSEs) is given. This is followed by a more
focused discussion of the problems of parallel computing in PSEs.
The current methodologies for parallel computing are too complex
and too specialized to be accessible for most engineers and
scientists. Thus a PSE for scientific computing must hide most
or all of the considerations of load balancing, parallelization
of algorithms, distributed memory, message passing, etc. The
techniques for hiding or automating these methodologies are
reviewed, along with their current and future potential for suc-
cess. A second approach for parallel computing in scientific
PSEs is to use an intuitively understandable paradigm based on
collaborating software systems and software agents. This
approach is still untested and it still requires that many spe-
cialized techniques of parallelism be hidden by the PSEs. But it
might provide a natural and effective way for users to describe
or specify the global parallelism in applications. If this is
successful then the automation of the lower level parallelization
tasks is much more feasible.
( slides )
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