NERO：一个用于天气预报建模的近高带宽存储器加速器
NERO: A Near High-Bandwidth Memory Stencil Accelerator for Weather Prediction Modeling
摘要：
持续的气候变化需要快速准确的天气和气候建模。然而，在解决大规模天气预测模拟时，最先进的CPU和GPU实现受到性能有限和高能耗的影响。这些实现主要是由复杂的不规则的内存访问模式和低算术强度，对加速提出了根本性的挑战。为了克服这些挑战，我们提出并评估了使用近内存加速使用高带宽内存（HBM）的可重构结构。我们专注于复合stenosis是天气预报模型中的基本内核。通过使用高层次的综合技术，我们开发NERO，FPGA+ HBM为基础的加速器连接通过IBM的CAPI 2（相干加速器处理器接口）到IBM的POWER 9主机系统。我们的实验结果表明，NERO在运行两种不同的复合模板内核时，性能分别是16核POWER 9系统的4.2倍和8.3倍。对于相同的两个内核，NERO在POWER 9系统上的能耗分别降低了22倍和29倍，能效分别为1.5 GFLOPS/W和17.3 GFLOPS/W。我们的结论是，采用近记忆加速解决方案的天气预测建模是有前途的手段，以实现高性能和高能源效率。
英文摘要：
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 CAPI2 (Coherent Accelerator Processor Interface) to an IBM POWER9 host system. Our experimental results show that NERO outperforms a 16-core POWER9 system 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 kernels over the POWER9 system with an energy efficiency of 1.5 GFLOPS/Watt and 17.3 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
