3 papers
cs.DC2026
Flint: Compiler Enabled Cluster-Free Design Space Exploration for Distributed ML
Jinsun Yoo, Meghan Cowan, Zheng Du +3
Design space exploration for future distributed Machine Learning systems suffers from a lack of readily available workload representation that enables flexible exploration across t…
cs.NI2025
Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning
Jinsun Yoo, ChonLam Lao, Lianjie Cao +4
This paper lays the foundation for Genie, a testing framework that captures the impact of real hardware network behavior on ML workload performance, without requiring expensive GPU…
cs.DC2024
Towards a Standardized Representation for Deep Learning Collective Algorithms
Jinsun Yoo, William Won, Meghan Cowan +4
The explosion of machine learning model size has led to its execution on distributed clusters at a very large scale. Many works have tried to optimize the process of producing coll…