TITLE:
Parallelization of Diagnostics for Climate Model Development
AUTHORS:
Jim McEnerney, Sasha Ames, Cameron Christensen, Charles Doutriaux, Tony Hoang, Jeff Painter, Brian Smith, Zeshawn Shaheen, Dean Williams
KEYWORDS:
Climate Diagnostics, Parallel, MPI, SPARK
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.9 No.5,
May
24,
2016
ABSTRACT: 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.