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 13.64s. Given the same amount of memory, MESHED can solve 50% larger problems than the CPU benchmark algorithm can.

中文翻译：

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避免回溯和消耗您的输入：使用 OpenMP 进行 CONUS 比例的流域划定


为美国本土 （CONUS） 规模的水文建模引入了节省内存的流域描绘 （MESHED） 并行算法。为大陆尺度的研究划定数以万计的流域不仅会占用大量计算资源，还会消耗大量内存。现有算法需要单独的输入和输出数据存储。但是，随着要描绘的流域数量和输入数据的分辨率显著增加，算法所需的内存量也会迅速增加。MESHED 通过在处理时销毁输入数据并跳过节点深度优先搜索来进一步减少所需的内存，从而将一个数据存储用于输入和输出。对于德克萨斯州的 1000 个流域，MESHED 的执行速度比中央处理器 （CPU） 基准算法快 95%，内存减少了 33%。在一项缩放实验中，它在 13.64 秒内划定了 CONUS 的 100,000 个流域。在内存量相同的情况下，MESHED 可以解决的问题比 CPU 基准测试算法大 50%。