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About this paper
Yifei Xia, Fangcheng Fu, Hao Yuan, Hanke Zhang, Xupeng Miao, Yijun Liu, Suhan Ling, Jie Jiang and 1 more
Peking University researchers developed TridentServe, a dynamic, stage-level serving system for Diffusion Pipelines that intelligently adapts resource allocation. The system improves Service Level Objective attainment by up to 6.6x and reduces P95 latency by up to 4.8x compared to static pipeline-level baselines, while eliminating Out-Of-Memory errors for large generative models.
Key insights
- Diffusion Pipelines exhibit profound heterogeneity across their stages (Encode, Diffuse, Decode) and also across diverse user requests, which current serving systems fail to address efficiently.
- Dynamic, fine-grained resource management at the stage-level, coupled with co-optimization of model placement and request dispatching, is crucial for maximizing efficiency and robustness for generative vision tasks.
- Seamless, no-downtime model re-deployment ("Adjust-on-Dispatch") and proactive tensor push mechanisms are essential for adaptive serving without incurring significant overheads.
Summary by alphaXiv.
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