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Characterizing Network Requirements for GPU API Remoting in AI Applications
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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.
Forward citations
Cited by 2 Pith papers
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AgileOS: A GPU Operating System Layer for Protected CUDA Services
AgileOS virtualizes CUDA at the library boundary using client shims and a trusted worker that owns the real context, plus PTX-injected guards to separate user and protected memory ranges.
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Gleam: Adaptive Network-Efficient CUDA API Remoting for Cross-Device GPU Sharing over LANs
Gleam makes remote CUDA GPU calls over a LAN up to 24x more efficient by caching model weights, running API calls asynchronously, and scheduling around network and GPU contention.
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