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本文为美国加州大学伯克利分校(作者:Sagar Karandikar)的硕士论文,共51页。
本文提出了一种开放源代码的仿真平台FireSim,它将FPGA加速模拟硅验证RTL设计与可扩展分布式网络仿真相结合,实现了大规模集群的循环精确微体系结构仿真。与以往FPGA加速仿真工具不同,FireSim运行在Amazon EC2 F1上,这是一个公共云FPGA平台,它极大地提高了可用性,提供了弹性使用,并降低了基于FPGA的大规模实验成本。
本文介绍了FireSim的设计与实现,并说明了它如何提供足够的性能,以实现大规模运行的现代应用程序,从而完成真正的硬件软件协同设计。例如,我们演示了自动生成和部署的一个目标集群,每个节点具有16GB DRAM,节点之间由200 Gbit/s网络互连,延迟为2微秒,模拟速度为3.4MHz处理器时钟速率(实时速度小于1000x)。
总之,FireSim实例化模拟4096核和16 TB内存,每秒运行≈140亿指令,并以每模拟小时仅≈100美元的总成本仿真了价值1280万美元的FPGA。我们提供了几个例子,说明FireSim如何在大规模设计中探索各种研究方向,包括高带宽、低延迟的网络建模、集成各种产品和专用数据中心节点的任意RTL设计、以及对各种数据中心组织进行建模,如重用扩展FireSim基础设施,以实现快速、大规模并行的精确单节点微体系结构实验。
We present FireSim, an open-source simulation platform that enables cycle-exact microarchitectural simulation of large scale-out clusters by combining FPGA-accelerated simulation of silicon-proven RTL designs with a scalable, distributed network simulation. Unlike prior FPGA-accelerated simulation tools, FireSim runs on Amazon EC2 F1, a public cloud FPGA platform, which greatly improves usability, provides elasticity, and lowers the cost of largescale FPGA-based experiments. We describe the design and implementation of FireSim and show how it can provide sufficient performance to run modern applications at scale, to enable true hardware-software co-design. As an example, we demonstrate automatically generating and deploying a target cluster of 1,024 3.2 GHz quad-core server nodes, each with 16 GB of DRAM, interconnected by a 200 Gbit/s network with 2 microsecond latency, which simulates at a 3.4 MHz processor clock rate (less than 1,000x slowdown over real-time). In aggregate, this FireSim instantiation simulates 4,096 cores and 16 TB of memory, runs ≈14 billion instructions per second, and harnesses 12.8 million dollars worth of FPGAs—at a total cost of only ≈$100 per simulation hour to the user. We present several examples to show how FireSim can be used to explore various research directions in warehouse-scale machine design, including modeling networks with high-bandwidth and low-latency, integrating arbitrary RTL designs for a variety of commodity and specialized datacenter nodes, and modeling a variety of datacenter organizations, as well as reusing the scale-out FireSim infrastructure to enable fast, massively parallel cycle-exact single-node microarchitectural experimentation.
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