NPAC Technical Report SCCS-479
An Interpretive Framework for Application Performance Prediction
M Parashar, S Hariri, T Haupt, G Fox
Submitted April 21 1993
Abstract
The last few decades have seen an impressive developments in every
aspect of parallel computing technology; viz. processing and storage
technology, interconnect technology and software technology. Although
these systems incorporate large amount of computing power, they are
not general enough to efficiently support today's
computation-intensive problems (e.g., the Grand Challenges), that
warrant multiple computational models and levels of parallelism. We
believe that the future of parallel computing lies in the integration
of the plethora of "specialized" architectures into a single
Heterogeneous High Performance Computing (HHPC) environment that
allows them to cooperate in solving complex problems. Software
development in any Parallel/Distributed environment is a non-trivial
process and requires a thorough understanding of the application and
the architecture. Evaluation tools form a critical part of any
software development environment. These tools enable the developer to
visualize the effect of various design choices on the performance of
the application, to study scalability of the application with system
and problem size and to investigate the effects of changes in system
run-time status and its configuration on the application execution. The
objective of t his paper is to propose an interpretive model for a
source driven performance prediction framework which can meet
challenges presented by an HHPC environment. The model provides a
comprehensive characterization methodology to abstract and parametrize
the behavior of the application and the computing
environment. Interpretative techniques are then used to predict the
performance of the abstracted application on the abstracted computing
environment. A prototype performance prediction framework has been
developed for the iPSC/860 using the proposed interpretative
model. Numerical results obtained on this system are presented. These
results confirm the potential of interpretative performance prediction
techniques and their applicability to an HHPC environment.