Demonstrates load balancing and efficiency in the Community Atmospheric Model across computing systems, implying broader applicability.
Community models for global climate research, such as the Community Atmospheric Model, must perform well on a variety of computing systems. Supporting diverse research interests, these computationally demanding models must be efficient for a range of problem sizes and processor counts. In this paper we describe the data structures and associated infrastructure developed for the physical parameterizations that allow the Community Atmospheric Model to be tuned for vector or non-vector systems, to provide load balancing while minimizing communication overhead, and to exploit the optimal mix of distributed Message Passing Interface (MPI) processes and shared OpenMP threads.
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Worley et al. (2005) studied this question.
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