101 citations · 166 across the 11 of their papers we have counts for
6 papers · 1 filter
Coral: Cost-Efficient Multi-LLM Serving over Heterogeneous Cloud GPUs
Yixuan Mei, Zikun Li, Zixuan Chen +5
The landscape of large language models (LLMs) has become increasingly fragmented, with no single model dominating the field. Meanwhile, cloud providers offer a diverse array of mid…
A System for Microserving of LLMs
Hongyi Jin, Ruihang Lai, Charlie F. Ruan +5
The recent advances in LLMs bring a strong demand for efficient system support to improve overall serving efficiency. As LLM inference scales towards multiple GPUs and even multipl…
Atlas: Hierarchical Partitioning for Quantum Circuit Simulation on GPUs (Extended Version)
Mingkuan Xu, Shiyi Cao, Xupeng Miao +2
This paper presents techniques for theoretically and practically efficient and scalable Schrödinger-style quantum circuit simulation. Our approach partitions a quantum circuit into…
Helix: Serving Large Language Models over Heterogeneous GPUs and Network via Max-Flow
Yixuan Mei, Yonghao Zhuang, Xupeng Miao +3
This paper introduces Helix, a distributed system for high-throughput, low-latency large language model (LLM) serving in heterogeneous GPU clusters. The key idea behind Helix is to…
Parcae: Proactive, Liveput-Optimized DNN Training on Preemptible Instances
Jiangfei Duan, Ziang Song, Xupeng Miao +5
Deep neural networks (DNNs) are becoming progressively large and costly to train. This paper aims to reduce DNN training costs by leveraging preemptible instances on modern clouds,…
SpotServe: Serving Generative Large Language Models on Preemptible Instances
Xupeng Miao, Chunan Shi, Jiangfei Duan +4
The high computational and memory requirements of generative large language models (LLMs) make it challenging to serve them cheaply. This paper aims to reduce the monetary cost for…