Loop then task: Hybridizing OpenMP parallelism to improve load balancing and memory efficiency in continental-scale longest flow path computation

Abstract

This study presents a new OpenMP parallel algorithm for Memory-Efficient Longest Flow Path (MELFP) computation for large-scale hydrologic analysis. MELFP hybridizes loop-based and task-based parallelism to improve load balancing and eliminates intermediate read-write matrices to optimize memory usage. Its performance remained insensitive to the threshold parameter for switching from looping to tasking. Compared to the benchmark algorithm, MELFP achieved a 66 % reduction in computation time while increasing CPU utilization by 33 %. Its 79 % lower peak memory usage enables processing larger datasets. These results suggest that MELFP is a fast and memory-efficient solution for longest flow path computations across a large number of watersheds, particularly in high-performance computing environments where rapid execution is prioritized over lower CPU utilization. MELFP’s additional ability to compute longest flow paths for individual subwatersheds provides added benefits for detailed and localized hydrologic modeling.