Abstract
Offline batch inference is gaining popularity as a cost-effective solution for latency-insensitive tasks, such as model evaluation and data curation. As the latency objective is highly relaxed, maximizing throughput is the primary goal in offline inference. Previous studies focused solely on throughput optimization within a batch. However, the diverse resource demands (compute-intensive vs. memory-intensive) across a wide range of applications make these approaches less effective, as imbalanced resource demands between batches restrict optimization opportunities.
Our insight is to create batches with mixed compute- and memory-intensive requests through request reordering to maximize resource overlapping. However, such a request schedule can conflict with the schedule that maximizes prefix sharing, a widely-used performance optimization, causing suboptimal inference throughput. In this paper, we first build a performance model to analyze request resource demands. Based on it, we design BlendServe, which harmonizes both resource overlapping and prefix sharing to maximize throughput. BlendServe organizes all requests using a resource-aware prefix tree and proposes a dual scanning algorithm to obtain the request schedule. Our evaluation on various models and workloads shows that BlendServe can achieve up to 90% of the optimal throughput.
Kan Zhu 朱侃
PhD Student (Computer Science)
I am Kan Zhu, a fourth year PhD student at University of Washington’s Paul G. Allen School of Computer Science and Engineering.