{"title":"避免回溯和烧毁输入:使用 OpenMP 进行 CONUS 规模流域划分","authors":"Huidae Cho","doi":"10.1016/j.envsoft.2024.106244","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106244"},"PeriodicalIF":4.8000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Avoid backtracking and burn your inputs: CONUS-scale watershed delineation using OpenMP\",\"authors\":\"Huidae Cho\",\"doi\":\"10.1016/j.envsoft.2024.106244\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":310,\"journal\":{\"name\":\"Environmental Modelling & Software\",\"volume\":\"183 \",\"pages\":\"Article 106244\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2024-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Modelling & Software\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1364815224003050\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Modelling & Software","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1364815224003050","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
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.
期刊介绍:
Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.