Modeling and analysis techniques are used to investigate
the performance of a massively parallel version
of DIRECT, a global search algorithm widely used
in multidisciplinary design optimization applications.
Several highdimensional
benchmark functions and
real world problems are used to test the design effectiveness
under various problem structures. Theoretical
and experimental results are compared for two
parallel clusters with different system scale and network
connectivity. The present work aims at studying
the performance sensitivity to important parameters
for problem configurations, parallel schemes,
and system settings. The performance metrics
include the memory usage, load balancing, parallel
efficiency, and scalability. An analytical bounding
model is constructed to measure the load balancing
performance under different schemes. Additionally,
linear regression models are used to characterize
two major overhead sources—interprocessor communication
and processor idleness, and also applied
to the isoefficiency functions in scalability analysis.
For a variety of highdimensional
problems and large
scale systems, the massively parallel design has
achieved reasonable performance. The results of
the performance study provide guidance for efficient
problem and scheme configuration. More importantly,
the generalized design considerations and
analysis techniques are beneficial for transforming
many global search algorithms to become effective
large scale parallel optimization tools