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FAST: A Functional Algorithm Simulation Testbed (Thesis)

Report ID:
May 1994
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Substantial progress in Parallel Scientific Computation will emerge
from improvements in the effectiveness of current parallel machines,
the development of new scientific algorithms, and the employment of
more powerful multiprocessors. Successfully addressing these issues in
a cost-effective way requires extensive experimentation.
Nevertheless, the large computational complexity of scientific
applications and the high cost of multiprocessor systems, make the
quantitative and qualitative analyses of parallel workloads a
difficult and costly endeavor.In this thesis, I address some of the issues involved in the modeling
and evaluation of parallel scientific computations. More
specifically, I introduce Functional Algorithm Simulation, that is,
simulation without performing the bulk of numerical calculations
involved in the applications studied. Functional Algorithm Simulation
is applicable in the evaluation of algorithms simulating complex
systems, for which the core-set of time-consuming calculations and
data-exchanges can be determined from input information, before the
actual computations take place. To assess the principles of
Functional Algorithm Simulation I built the {em Functional Algorithm
Simulation Testbed (FAST)/}, a software prototype system for
approximately simulating the parallel execution of such algorithms on
uniprocessor workstations. FAST has been used to evaluate parallel
executions of three interesting and important scientific algorithms:
SIMPLE, a Computational Fluid Dynamics code; the Fast Multipole
Method, and a modified version of the Barnes-Hut algorithm, which
solve the N-Body problem and have applications in Computational
Molecular Dynamics and Astrophysics. Experimentation with FAST shows
that approximate simulation can give valid and useful results. FAST
enables us to study parallel executions of much larger problem sizes
and more processors than those reported so far, with modest computing
resources. Also, it allows us to collect detailed information
characterizing parallel executions on various message-passing
architectures, analyze the effects of communication overhead to
parallel performance, and study the scalability of parallel

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