  Ongoing climate change calls for fast and accurate weather and climate
modeling. However, when solving large-scale weather prediction simulations,
state-of-the-art CPU and GPU implementations suffer from limited performance
and high energy consumption. These implementations are dominated by complex
irregular memory access patterns and low arithmetic intensity that pose
fundamental challenges to acceleration. To overcome these challenges, we
propose and evaluate the use of near-memory acceleration using a reconfigurable
fabric with high-bandwidth memory (HBM). We focus on compound stencils that are
fundamental kernels in weather prediction models. By using high-level synthesis
techniques, we develop NERO, an FPGA+HBM-based accelerator connected through
OCAPI (Open Coherent Accelerator Processor Interface) to an IBM POWER9 host
system. Our experimental results show that NERO outperforms a 16-core POWER9
system by 5.3x and 12.7x when running two different compound stencil kernels.
NERO reduces the energy consumption by 12x and 35x for the same two kernels
over the POWER9 system with an energy efficiency of 1.61 GFLOPS/Watt and 21.01
GFLOPS/Watt. We conclude that employing near-memory acceleration solutions for
weather prediction modeling is promising as a means to achieve both high
performance and high energy efficiency.
