服务具有大内存足迹的链式结构作业：面向大型基础模型服务
Serving Chain-structured Jobs with Large Memory Footprints with Application to Large Foundation Model Serving
Tingyang Sun, Ting He, I-Hong Hou
AI 速览：针对大内存足迹的链式结构作业服务，提出“服务器链组合”问题，通过块放置与缓存分配建模流水线并行下的大模型服务，并给出可扩展且具性能保证的算法。
As a current trend in Artificial Intelligence (AI), large foundation models are increasingly employed as the core of AI services. However, even after training, serving such models at scale remains a challenging task due to their heavy resource footprints, particularly in terms of GPU memory. While recent works revealed unique characteristics of systems serving foundation models that distinguish them from traditional distributed computing systems, there is still a lack of fundamental understanding of the underlying system management problems. This work aims at addressing this gap by extracting a novel problem of "server chain composition" via block placement and cache allocation for serving chainstructured jobs with large memory footprints, which models a fundamental problem in serving large foundation models through pipeline parallelism. After showing the NP-hardness of the optimal solution, the focus is turned to developing scalable algorithms with guaranteed performance under state-of-the-art load balancing. Application of the proposed solution to a distributed large language model (LLM) serving system shows significant reduction of response times compared to state-of-the-art solutions.