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. In
this second part of a twopart
work, theoretical and
experimental results are compared for two parallel
clusters with different system scale and network
connectivity. The first part studied performance
sensitivity to important parameters for problem configurations
and parallel schemes, using performance
metrics such as memory usage, load balancing,
and parallel efficiency. Here 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 design
considerations and analysis techniques generalize to
the transformation of other global search algorithms
into effective large scale parallel optimization tools
