Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Analyzing scalability of parallel algorithms and architectures
Journal of Parallel and Distributed Computing - Special issue on scalability of parallel algorithms and architectures
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Towards an Accurate Model for Collective Communications
International Journal of High Performance Computing Applications
Performance Modeling and Tuning Strategies of Mixed Mode Collective Communications
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
High performance RDMA-based MPI implementation over infiniBand
International Journal of Parallel Programming - Special issue I: The 17th annual international conference on supercomputing (ICS'03)
Deterministic parallel global parameter estimation for a model of the budding yeast cell cycle
Journal of Global Optimization
Design and implementation of a massively parallel version of DIRECT
Computational Optimization and Applications
Adjusting process count on demand for petascale global optimization
Parallel Computing
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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 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 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 high-dimensional 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.