부산대학교 도서관

Monte Carlo uncertainty analysis, model calibration and optimization applications in hydrology, usually involve a very large number of forward transient model solutions, often resulting in computational bottlenecks. Parallel processing can significantly reduce overall simulation time, benefiting from the architecture of modern computers. This work investigates system performance using two realistic flow and transport modeling scenarios, applied to various modeling hardware, to provide information on the expected performance of parallel simulations and inform investment decisions. We investigate how performance, measured in terms of speedup and efficiency, changes with increasing number of parallel processes. We conclude that the maximum performance achieved by parallelization can range from 40% to 100% of the theoretical limit, with the lower increases associated with multi‐CPU servers. The number of parallel processes required to maximize performance is application dependent, and in contrast to common practice, often needs to be significantly larger than the total number of system CPU cores. Further testing is required to better understand how the physical problem being simulated affects the optimal number of parallel processes needed. Finally, when laptops are considered for modeling applications, careful consideration should be given not only to the specifications but also to the intended use designated by the manufacturer. [ABSTRACT FROM AUTHOR]