Fit Fly:基于并行仿真的互联创新案例研究

Neil McGlohon, Noah Wolfe, M. Mubarak, C. Carothers
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引用次数: 1

摘要

为了满足高性能计算(HPC)互连对百亿亿级性能的需求,许多系统架构师正在转向模拟结果,以准确可靠地预测未来技术的性能。使用各种基准测试工具(包括合成工作负载和应用程序跟踪)测试全面的网络,可以在不需要物理构建测试网络的情况下,对最有前途的想法提供关键的见解。虽然很灵活,但是这种方法的计算时间非常密集。我们通过使用大规模的、乐观的并行模拟来解决这个时间复杂性的挑战,最终导致更快的HPC网络架构创新。在本文中,我们通过一个现实世界的网络设计案例研究来证明这种创新能力。具体来说,我们模拟并比较了四种极端规模的互连:Dragonfly、Megafly、Slim Fly和Slim Fly网络拓扑的新双轨双平面变体。我们提出Slim Fly的这种新变体,称为Fit Fly,以展示如何通过并行模拟实现互连创新和评估-超越了通过分析方法可能实现的范围。我们使用CODES互连仿真框架验证并比较了该模型与各种网络设计。通过在并行环境中运行大规模模拟,我们能够快速生成可靠的性能结果,帮助网络设计人员在下一代高性能网络设计上取得突破性进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fit Fly: A Case Study on Interconnect Innovation through Parallel Simulation
To meet the demand for exascale-level performance from high-performance computing (HPC) interconnects, many system architects are turning to simulation results for accurate and reliable predictions of the performance of prospective technologies. Testing full-scale networks with a variety of benchmarking tools, including synthetic workloads and application traces, can give crucial insight into what ideas are most promising without needing to physically construct a test network. While flexible, however, this approach is extremely compute time intensive. We address this time complexity challenge through the use of large-scale, optimistic parallel simulation that ultimately leads to faster HPC network architecture innovations. In this paper we demonstrate this innovation capability through a real-world network design case study. Specifically, we have simulated and compared four extreme-scale interconnects: Dragonfly, Megafly, Slim Fly, and a new dual-rail-dual-plane variation of the Slim Fly network topology. We present this new variant of Slim Fly, dubbed Fit Fly, to show how interconnect innovation and evaluation---beyond what is possible through analytic methods---can be achieved through parallel simulation. We validate and compare the model with various network designs using the CODES interconnect simulation framework. By running large-scale simulations in a parallel environment, we are able to quickly generate reliable performance results that can help network designers break ground on the next generation of high-performance network designs.
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