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Ongoing climate change calls for fast and accurate weather and climatemodeling. However, when solving large-scale weather prediction simulations,state-of-the-art CPU and GPU implementations suffer from limited performanceand high energy consumption. These implementations are dominated by complexirregular memory access patterns and low arithmetic intensity that posefundamental challenges to acceleration. To overcome these challenges, wepropose and evaluate the use of near-memory acceleration using a reconfigurablefabric with high-bandwidth memory (HBM). We focus on compound stencils that arefundamental kernels in weather prediction models. By using high-level synthesistechniques, we develop NERO, an FPGA+HBM-based accelerator connected throughIBM CAPI2 (Coherent Accelerator Processor Interface) to an IBM POWER9 hostsystem. Our experimental results show that NERO outperforms a 16-core POWER9system by 4.2x and 8.3x when running two different compound stencil kernels.NERO reduces the energy consumption by 22x and 29x for the same two kernelsover the POWER9 system with an energy efficiency of 1.5 GFLOPS/Watt and 17.3GFLOPS/Watt. We conclude that employing near-memory acceleration solutions forweather prediction modeling is promising as a means to achieve both highperformance and high energy efficiency.

NERO: A Near High-Bandwidth Memory Stencil Accelerator for Weather Prediction Modeling