15 citations · 18 across the 5 of their papers we have counts for
5 papers
Lumos: Efficient Performance Modeling and Estimation for Large-scale LLM Training
Mingyu Liang, Hiwot Tadese Kassa, Wenyin Fu +3
Training LLMs in distributed environments presents significant challenges due to the complexity of model execution, deployment systems, and the vast space of configurable strategie…
Mystique: Enabling Accurate and Scalable Generation of Production AI Benchmarks
Mingyu Liang, Wenyin Fu, Louis Feng +5
Building large AI fleets to support the rapidly growing DL workloads is an active research topic for modern cloud providers. Generating accurate benchmarks plays an essential role…
End-to-End Application Cloning for Distributed Cloud Microservices with Ditto
Mingyu Liang, Yu Gan, Yueying Li +4
We present Ditto, an automated framework for cloning end-to-end cloud applications, both monolithic and microservices, which captures I/O and network activity, as well as kernel op…
Sage: Leveraging ML to Diagnose Unpredictable Performance in Cloud Microservices
Yu Gan, Mingyu Liang, Sundar Dev +2
Cloud applications are increasingly shifting from large monolithic services, to complex graphs of loosely-coupled microservices. Despite their advantages, microservices also introd…
Sage: Using Unsupervised Learning for Scalable Performance Debugging in Microservices
Yu Gan, Mingyu Liang, Sundar Dev +2
Cloud applications are increasingly shifting from large monolithic services to complex graphs of loosely-coupled microservices. Despite the advantages of modularity and elasticity…