Treffer: Avoid backtracking and burn your inputs: CONUS-scale watershed delineation using OpenMP.

The Memory-Efficient Watershed Delineation (MESHED) parallel algorithm is introduced for Contiguous United States (CONUS)-scale hydrologic modeling. Delineating tens of thousands of watersheds for a continental-scale study can not only be computationally intensive, but also be memory-consuming. Existing algorithms require separate input and output data stores. However, as the number of watersheds to delineate and the resolution of input data grow significantly, the amount of memory required for an algorithm also quickly increases. MESHED uses one data store for both input and output by destructing input data as processed and a node-skipping depth-first search to further reduce required memory. For 1000 watersheds in Texas, MESHED performed 95 % faster than the Central Processing Unit (CPU) benchmark algorithm using 33 % less memory. In a scaling experiment, it delineated 100,000 watersheds across the CONUS in 13.64 s. Given the same amount of memory, MESHED can solve 50 % larger problems than the CPU benchmark algorithm can. [Display omitted] • A memory-efficient watershed delineation algorithm was introduced. • The new algorithm uses a node-skipping depth-first search to save memory. • Both input and output data are stored in a shared matrix to reduce required memory. • It performed 95% faster than its CPU benchmark algorithm using 33% less memory. • It can solve 50% larger problems than what the CPU benchmark algorithm can handle. [ABSTRACT FROM AUTHOR]

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