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Parallelization of Diagnostics for Climate Model Development

Journal of Software Engineering and Applications 2016年第5期9卷 199-207页

作者： Jim McEnerney Sasha Ames cameron christensen Charles Doutriaux Tony Hoang Jeff Painter Brian Smith Zeshawn Shaheen Dean Williams Lawrence Livermore National Laboratory Livermore California USA University of Utah Salt Lake City Utah USA Oak Ridge National Laboratory Oak Ridge Tennessee USA

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

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