Analysis of FastEddy Model Data on GPUs

Authors: Shay Liu (Indiana University) and Supreeth Suresh, Cena Miller, and Jeremy Sauer (National Center for Atmospheric Research (NCAR))

Abstract: Data analysis of atmospheric model outputs is often embarrassingly parallel and compute intensive, and is traditionally done on central processing units (CPUs). FastEddy is a General Purpose Graphical Processing Units (GPGPU) -based Large Eddy Simulation (LES) atmospheric model developed at NCAR-Research Application Laboratory that solves fundamental dynamical equations and computes turbulence at a high resolution, producing large datasets that reside on GPUs. To reduce the amount of data movement from GPUs to CPUs for analysis, and to reduce the need for end-users to learn complex GPU programming using CUDA, this work explores performing the analysis on GPUs using only Python libraries.

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