Characterizing Network Requirements for GPU API Remoting in AI Applications

Tianxia Wang, Zhuofu Chen, Xingda Wei, Jinyu Gu, Rong Chen, Haibo Chen
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Abstract

GPU remoting is a promising technique for supporting AI applications. Networking plays a key role in enabling remoting. However, for efficient remoting, the network requirements in terms of latency and bandwidth are unknown. In this paper, we take a GPU-centric approach to derive the minimum latency and bandwidth requirements for GPU remoting, while ensuring no (or little) performance degradation for AI applications. Our study including theoretical model demonstrates that, with careful remoting design, unmodified AI applications can run on the remoting setup using commodity networking hardware without any overhead or even with better performance, with low network demands.
鉴定人工智能应用中 GPU API Remoting 的网络要求
GPU 远程控制是支持人工智能应用的一项前景广阔的技术。然而,要实现高效远程,网络在延迟和带宽方面的要求是未知的。在本文中,我们采用了一种以 GPU 为中心的方法来推导 GPU 远程通信的最低延迟和带宽要求,同时确保人工智能应用不会(或几乎不会)出现性能下降。我们的研究(包括理论模型)表明,通过精心的远程设计,未经修改的人工智能应用可以在使用商品网络硬件的远程设置上运行,而不会产生任何开销,甚至可以在网络需求较低的情况下获得更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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