Parallelization of the NASA Goddard Cumulus Ensemble Model for Massively Parallel Computing

Abstract

Massively parallel computing, using a message passing interface (MPI), has been implemented into a three-dimensional version of the Goddard Cumulus Ensemble (GCE) model. The implementation uses the domainresemble concept to design a code structure for both the whole domain and sub-domains after decomposition. Instead of inserting a group of MPI related statements into the model routine, these statements are packed into a single routine. In other words, only a single call statement to the model code is utilized once in a place, thus there is minimal impact on the original code. Therefore, the model is easily modified and/or managed by the model developers and/or users, who have little knowledge of massively parallel computing.The model decomposition is highly flexible such that the entire model domain can be sliced into any number of partial domains in one-or twodimensional decomposition. Data exchange is through a halo-region, which is overlaid with neighboring partial domains. A halo-region is also known as a ghost-cell region. For reproducibility purposes, transposing data among tasks into different decompositions is required, such as Fourier transform and full domain summation.Terr. Atmos. Ocean. Sci., Vol. 18, No. 3, August 2007 594 The well-behaved performance of the implemented codes with anelastic and compressible versions on three different computing platforms indicates a successful implementation. The parallelization of both versions has speedup of 99% for up to 256 tasks. The anelastic version has better speedup and efficiency because its numerical algorithm is preferred by the parallelization than that of the compressible version.

Characteristics of vertical velocity in marine stratocumulus: comparison of large eddy simulations with observations

“…We chose the three-dimensional parallel version of the Goddard cloud ensemble model (GCE) for this study (Juang et al 2007, Tao et al 2003, Tao and Simpson 1993. Turbulence is parameterized via a 1.5-order closure scheme; the microphysical parameterization of clouds is a single-moment bulk scheme; and broadband shortwave-and longwave-radiative transfer modules are interactively coupled with cloud fields.…”

Improving NASA's Multiscale Modeling Framework for Tropical Cyclone Climate Study

“…The GCE itself has been implemented with a 2D domain decomposition using message-passing interface version 1 (MPI-1) with good parallel efficiency. 14 Thus, an ideal solution for the course-grain parallelism implemented in the MMF is to run more copies of GCEs with higher CPU counts in parallel, while still keeping the option of taking advantage of the fine-grain parallelism inside the GCE.…”

A New Parallel Domain-Decomposed Chebyshev Collocation Method for Atmospheric and Oceanic Modeling

“…This property still holds for the domain-decomposition simulations. It should be pointed out that the method used here cannot achieve the "reproducibility" of parallel computing (i.e., Juang et al 2007). Due to the irregular grid points, the Chebyshev method will not achieve identical results for parallel domain-decomposition computation (Kopriva 1986(Kopriva , 1989(Kopriva , 1996Kopriva and Kolias 1996).…”