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GPU accelerated atmospheric chemical kinetics in the ECHAM/MESSy (EMAC) Earth system model

Michail Alvanos, Theodoros Christoudias
The Cyprus Institute, PO Box 27456, 1645 Nicosia, Cyprus
Geoscientific Model Development, Discussion Papers, 2017

@article{alvanos2017accelerated,

   title={GPU accelerated atmospheric chemical kinetics in the ECHAM/MESSy (EMAC) Earth system model},

   author={Alvanos, Michail and Christoudias, Theodoros},

   year={2017}

}

This paper presents an application of GPU accelerators in Earth system modelling. We focus on atmospheric chemical kinetics, one of the most computationally intensive tasks in climate-chemistry model simulations. We developed a software package that automatically generates CUDA kernels to numerically integrate atmospheric chemical kinetics in the global climate model ECHAM/MESSy Atmospheric Chemistry (EMAC), used to study climate change and air quality scenarios. A source-to-source compiler outputs a CUDA compatible kernel, by parsing the FORTRAN code generated by the Kinetic PreProcessor (KPP) general analysis tool. All Rosenbrock methods that are available in the KPP numerical library are supported. Performance evaluation, using Fermi and Pascal CUDA-enabled GPU accelerators shows achieved speedups of 4.5x and 22.4x respectively of the kernel execution time. A node-to-node real-world production performance comparison shows a 1.75x speed-up over the non-accelerated application using the KPP 3-stage Rosenbrock solver. We provide a detailed description of the code optimizations used to improve the performance including memory optimizations, control code simplification, and reduction of idle time. The accuracy and correctness of the accelerated implementation are evaluated by comparing to the CPU-only version of the application. The relative difference is found to be less than 0.00005% when comparing the output of the accelerated kernel the CPU-only code, within the target level of relative accuracy (relative error tolerance) of 0.1%. The approach followed, including the computational workload division and the developed GPU solver code can potentially be used as the basis for hardware acceleration of numerous geoscientific models that rely on KPP for atmospheric chemical kinetics applications.
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