文章基本信息

标题：Parallelization of Diagnostics for Climate Model Development
本地全文：下载
作者：Jim McEnerney ; Sasha Ames ; Cameron Christensen 等
期刊名称：Journal of Software Engineering and Applications
印刷版ISSN：1945-3116
电子版ISSN：1945-3124
出版年度：2016
卷号：09
期号：05
页码：199-207
DOI：10.4236/jsea.2016.95016
语种：English
出版社：Scientific Research Publishing
摘要：The parallelization of the diagnostics for climate research has been an important goal in the performance testing and improvement of the diagnostics for the Department of Energy’s (DOE’s) Accelerated Climate Modeling for Energy (ACME) project [1]. The primary mission of the ACME project is to build and test the next-generation Earth system model for current and future generations of computing systems operated by the DOE office of science computing facilities, including the envisioned exascale systems foreseen in the early part of the next decade. As part of the underpinning workflow environment, a diagnostics, model metrics, and intercomparison Python framework, called UVC Metrics was created to aid in testing and production execution of the model. This framework builds on common methods and similar metrics to accommodate and diagnose individual component models, such as atmosphere, land, ocean, sea ice, and land ice. This paper reports on initial parallelization of UVC Metrics for the atmosphere model component using two popular frameworks: MPI and SPARK. A timing study is presented to assess the performance of each method in which significant improvement was achieved for both frameworks despite I/O contentions with NFS. The advantages and disadvantages of each framework are also presented.
关键词：Climate Diagnostics;Parallel;MPI;SPARK