A High-performance temporal-spatial discretization method for the parallel computing of river basins

Abstract

The distributed basin model (DBM) has become one of the most effective tools in river basin studies. In order to overcome the efficiency bottleneck of DBM, an effective parallel-computing method, named temporal-spatial discretization method (TSDM), is proposed. In space, TSDM adopts the sub-basin partitioning manner to the river basin. Compared to the existing sub-basin-based parallel methods, more computable units can be supplied, organized and dispatched using TSDM. Through the characteristic of the temporal-spatial dual discretization, TSDM is capable of exploiting the river-basin parallelization degree to the maximum extent and obtaining higher computing performance. A mathematical formula assessing the maximum speedup ratio (MSR) of TSDM is provided as well. TSDM is independent of the implementation of any physical models and is preliminarily tested in the Lhasa River basin with 1-year rainfall-runoff process simulated. The MSR acquired in the existing traditional way is 7.98. Comparatively, the MSR using TSDM equals to 15.04 under the present limited computing resources, which appears to still have potential to keep increasing. The final results demonstrate the effectiveness and applicability of TSDM.