Parallel Distributed Hydrology Soil Vegetation Model (DHSVM) using global arrays

The Distributed Hydrology Soil Vegetation Model (DHSVM) code was parallelized for distributed memory computers using the Global Arrays (GA) programming model. To analyze parallel performance, DHSVM was used to simulate the hydrology in two river basins of significant size located in the northwest co...

Full description

Saved in:
Bibliographic Details
Published in:Environmental modelling & software : with environment data news Vol. 122; no. C; p. 104533
Main Authors: Perkins, William A., Duan, Zhuoran, Sun, Ning, Wigmosta, Mark S., Richmond, Marshall C., Chen, Xiaodong, Leung, L. Ruby
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.12.2019
Elsevier Science Ltd
Elsevier
Subjects:
Arrays
Basins
Canada
Clearwater River basin
Columbia River
Columbia River basin
Computer simulation
Computers
Distributed hydrology model
Distributed memory
High-performance computing
Hydrology
Parallel computing
Parallel processing
River basins
Rivers
soil
Soils
Two dimensional models
United States
Vegetation
Watershed hydrology
watersheds
weather
Weather forecasting
Columbia River
Canada
United States
Watershed hydrology
Parallel computing
Columbia River basin
Distributed hydrology model
Clearwater River basin
High-performance computing
ISSN:1364-8152, 1873-6726
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The Distributed Hydrology Soil Vegetation Model (DHSVM) code was parallelized for distributed memory computers using the Global Arrays (GA) programming model. To analyze parallel performance, DHSVM was used to simulate the hydrology in two river basins of significant size located in the northwest continental United States and southwest Canada at 90 m resolution: the (1) Clearwater (25,000 km2) and (2) Columbia (668,000 km2) River basins. Meteorological forcing applied to both basins was dynamically down-scaled from a regional reanalysis using the Weather Research and Forecasting (WRF) model and read into DHSVM as 2D maps for each time step. Parallel code speedup was significant. Run times for 1-year simulations were reduced by an order of magnitude for both test basins. A maximum parallel speedup of 105 was attained with 480 processors while simulating the Columbia River basin. Speedup was limited by input-dominated tasks, particularly the input of meteorological forcing data. •The well established Distributed Hydrology Vegetation Soil Model (DHSVM) was parallelized for distributed memory platforms.•The Global Arrays (GA) partitioned global address space (PGAS) library for distributed arrays was used for inter-process communication.•Parallel DHSVM was used to simulate the hydrology of two large river basins, with areas of 25,000 and 668,000 square kilometers, at a 90 meter resolution.•Maximum parallel speed up of 105 was measured using 480 processors with the Columbia River basin simulation.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
USDOE
AC02-05CH11231
ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2019.104533