Solving Mesoscale Atmospheric Dynamics Using a Reconfigurable Dataflow Architecture

Abstract

FPGA-based reconfigurable dataflow engines provide a novel architecture to achieve breakthroughs in both time and energy to solution in numerical simulations. This article presents an efficient dataflow methodology for solving the Euler atmospheric dynamic equations, an essential step for mesoscale atmospheric simulation. The authors present customizable optimizations such as hybrid decomposition, algorithmic offsetting, customizable window buffer, and mixed-precision arithmetic. Combining algorithmic and architectural optimizations, they map a complex Euler stencil kernel into a single FPGA chip and develop a long streaming pipeline that can perform 956 mixed-precision operations per cycle. They also fully optimize the Euler performance over different traditional processors and accelerators based on multicore and many-core architectures. Their dataflow design outperforms traditional multicore and many-core counterparts in both time and energy to solution. This work demonstrates the promising potential of employing dataflow architectures in numerical simulations to overcome some of the major constraints facing mainstream processors and accelerators.