ExeGPT: Constraint-Aware Resource Scheduling for LLM Inference
Hyungjun Oh, K.J. Kim, Jaemin Kim, Sungkyun Kim, Junyeol Lee, Du-Seong Chang, Jiwon Seo · 2024
This paper presents ExeGPT, a distributed system designed for constraint-aware LLM inference. ExeGPT finds and runs with an optimal execution schedule to maximize inference throughput while satisfying a given latency constraint. By leveraging the distribution of input and output sequences, it effectively allocates resources and determines optimal execution configurations, including batch sizes and partial tensor parallelism. We also introduce two scheduling strategies based on Round-Robin Allocation and Workload-Aware Allocation policies, suitable for different NLP workloads.