A divide-and-conquer parallel computing scheme for the optimization analysis of tribological systems

The trend of using commercial products and open source packages to construct a scalable computer cluster for distributed computing to minimize the execution time of numerical optimization has long been expected. However, in the tribology field progress has been slow due to the complexity of parallel coding and the lack of easy-to-implement parallel algorithms. This study presents an optimization analysis of constrained problems by using a divide-and-conquer scheme suitable for parallel computation. A porous air bearing model of moderate computational load is used to illustrate the optimization procedure. In the optimization process, the design space is subdivided and each of the subdivisions is dealt with by Taguchi's Design of Experiments to achieve the local optimum. The global optimum is then determined when all the local optima are obtained. Two task-assignment strategies in the cluster computing are implemented and discussed. Reasonable speedup and parallel efficiency were obtained for the highly uneven task-load calculations. The approach does not require the knowledge of parallel programming techniques associated with message passing libraries. The presented scheme has high portability, low cost of evaluation process, and algorithm-machine scalability, which should be an easy-to-implement and efficient tool for many tribological studies.