面向智能网络的AI感知GPU原生报文处理技术

中文摘要 AI 辅助

将内联人工智能（AI）模型集成到关键网络基础设施中，根本上受限于CPU介导的报文处理带来的高延迟和同步开销。传统GPU卸载方案及近期的CPU旁路框架虽试图缩小这一差距，但仍受限于专有生态系统，或仍依赖主机CPU及粗粒度批处理来协调有状态遥测与AI流水线执行。本文提出AGP这一新型框架，将GPU从被动加速器提升为主要数据路径控制器，探究当GPU自主掌控完整的报文到推理流水线时，架构会发生何种变化。通过实现GPU原生报文处理、无竞争的GPU内有状态聚合，以及用于持续AI推理的持久 mega-kernel，AGP将CPU排除在关键路径之外。评估表明，原生GPU内报文处理可维持线速吞吐量，同时能效提升超6.1倍；此外，集成入侵检测系统（IDS）压力测试消除了传统CPU-GPU同步开销，端到端延迟降低7.9倍（p99延迟最高降低35倍），且仅使用约2%的GPU线程容量，就使全系统推理吞吐量提升9.3倍。

英文摘要

The integration of inline Artificial Intelligence (AI) models into critical network infrastructure is fundamentally bottlenecked by the high latency and synchronization overhead of CPU-mediated packet processing. While legacy GPU offload and recent CPU-bypass frameworks attempt to bridge this gap, they remain trapped in proprietary ecosystems or still rely on the host CPU and coarse-grained batching to coordinate stateful telemetry and AI pipeline execution. In this paper, we propose AGP, a novel framework that promotes the GPU from a passive accelerator to a primary data-path controller, answering what changes architecturally when the GPU autonomously owns the complete packet-to-inference pipeline. By enabling GPU-native packet processing, contention-free in-GPU stateful aggregation, and a persistent mega-kernel for continuous AI inference, AGP removes the CPU from the critical path. Our evaluation demonstrates that native in-GPU packet processing sustains line-rate throughput while being over 6.1x more power-efficient. Furthermore, our integrated Intrusion Detection System (IDS) stress test eliminates the legacy CPU-GPU synchronization tax, reducing end-to-end latency by 7.9x (up to 35x p99) and accelerating whole-system inference throughput by 9.3x using only ~2% of the GPU's thread capacity.

发表机构

• Wayne State University（韦恩州立大学）
• University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）

机构由 AI 辅助整理，请以论文原文为准。