Scaling modeling and simulation on high-performance computing clusters. (February 2020)

• Record Type: Journal Article
• Authors: Mikailov, Mike; Qiu, Junshan; Luo, Fu-Jyh; Whitney, Stephen; Petrick, Nicholas
• Is Part Of: Simulation. Volume 96:Number 2(2020)
• Page Start: 221
• Page End: 232
• Publication Date: 2020-02
• Subjects: High-performance computing -- modeling -- simulation -- Markov chain Monte Carlo -- parallelization -- message passing interface -- multi-threading -- array jobs -- grid engine -- cluster -- scaling -- parallel merge -- parallel input/output -- embarrassingly parallel; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549719878249
• Languages: English
• ISSNs: 0037-5497

Abstract

Large-scale modeling and simulation (M&S) applications that do not require run-time inter-process communications can exhibit scaling problems when migrated to high-performance computing (HPC) clusters if traditional software parallelization techniques, such as POSIX multi-threading and the message passing interface, are used. A comprehensive approach for scaling M&S applications on HPC clusters has been developed and is called "computation segmentation." The computation segmentation is based on the built-in array job facility of job schedulers. If used correctly for appropriate applications, the array job approach provides significant benefits that are not obtainable using other methods. The parallelization illustrated in this paper becomes quite complex in its own right when applied to extremely large M&S tasks, particularly due to the need for nested loops. At the United States Food and Drug Administration, the approach has provided unsurpassed efficiency, flexibility, and scalability for work that can be performed using embarrassingly parallel algorithms.