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 IBM 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.

翻译：目前气候变化需要快速和准确的天气和气候模型。然而,当解决大规模天气预测模拟时,最先进的CPU和GPU的执行因业绩有限和能源消耗量高而受到影响。这些执行主要是复杂的不规则记忆存取模式和低计算强度,给加速速度带来根本性挑战。为了克服这些挑战,我们提议使用一个具有高带宽内存(HBM)的可重新配置结构来评估近模加速的使用情况。我们侧重于作为天气预测模型基本内核的复合固态。我们利用高层次合成技术开发了NERO,这是一个基于FPGA+HBM的加速器,通过IBM OCAPI(开放焦加速器处理器处理器)连接到IBM POWER9主机系统(OWER9)的快速加速器。我们的实验结果表明,NERO比一个16个核心的模型POWER9系统(HER9)高出5.3x和12.7x,同时运行两个不同的化合物内核内核内核。NERO通过12x和35的高温预测系统,将能源消耗量在GFSFL1的高温10。