论文标题
Nero：天气预测建模的近乎宽带的记忆模板加速器
NERO: A Near High-Bandwidth Memory Stencil Accelerator for Weather Prediction Modeling
论文作者
论文摘要
持续的气候变化需要快速准确的天气和气候建模。但是，在解决大规模天气预测模拟时，最先进的CPU和GPU实施的性能有限和能耗高。这些实现以复杂的不规则记忆访问模式和低算术强度为主导，这对加速构成了基本挑战。为了克服这些挑战，我们提出和评估使用具有高带宽内存（HBM）的可重构织物（HBM）的近内存加速度的使用。我们专注于在天气预测模型中是基本内核的复合模具。通过使用高级合成技术，我们开发了Nero，这是一种基于FPGA+HBM的加速器，该加速器通过IBM CAPI2（相干加速器处理器界面）连接到IBM Power9主机系统。我们的实验结果表明，运行两个不同的化合物模板内核时，Nero的表现要优于4.2倍和8.3倍的16核Power9系统。在Power9系统上，Nero将能源消耗降低了22倍和29倍，能源效率为1.5 GFLOPS/WATT和17.3 GFLOPS/WATT。我们得出的结论是，使用近内存加速度解决方案进行天气预测建模是有望成为实现高性能和高能源效率的一种手段。
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.
