PERFORMANCE MODELING AND ANALYSIS OF A MASSIVELY

Modeling and analysis techniques are used to investigate the performance of a massively parallel version of DIRECT, a global search algorithm widely used in multidis-ciplinary design optimization applications. Several high-dimensional benchmark functions and real world problems are used to test the design effectiveness under various problem structures. In this second part of a two-part work, theoretical and experimental results are compared for two parallel clusters with different system scales and network connectivities. The first part studied performance sensitiv-ity to important parameters for problem configurations and parallel schemes, using performance metrics such as mem-ory usage, load balancing, and parallel efficiency. Here linear regression models are used to characterize two major over-head sources, interprocessor communication and proces-sor idleness, and also applied to the isoefficiency functions in scalability analysis. For a variety of high-dimensional prob-lems 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 effec-tive large-scale parallel optimization tools. Key words: DIRECT, global search algorithms, isoeffi-ciency, load balancing, parallel optimization, perform-ance modeling, scalability analysis