@inproceedings{25d44345508c4191908133d9217b8ea1,
title = "Optimization and parallel load balancing of the MPAS atmosphere weather and climate code",
abstract = "MPAS (Model for Prediction Across Scales) Atmosphere is a highly scalable application for global weather and climate simulations. It uses an unstructured Voronoi mesh in the horizontal dimensions, thereby avoiding problems associated with traditional rectilinear grids, and deploys a subset of the atmospheric physics used in WRF. In this paper, we describe work that was done to improve the overall performance of the software: serial optimization of the dynamical core and threadlevel load balancing of the atmospheric physics. While the overall reductions were modest for standard benchmarks, we expect that the contributions will become more important with the eventual addition of atmospheric chemistry or when running at larger scale.",
keywords = "Atmospheric physics, Climate modeling, Parallel computing, Performance optimization",
author = "Sinkovits, \{Robert S.\} and Duda, \{Michael G.\}",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; Conference on Diversity, Big Data, and Science at Scale, XSEDE 2016 ; Conference date: 17-07-2016 Through 21-07-2016",
year = "2016",
month = jul,
day = "17",
doi = "10.1145/2949550.2949575",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
booktitle = "Proceedings of XSEDE 2016",
address = "United States",
}