Boyang Li
bli70@hawk.iit.edu
Stuart Bldg 007B
Department of Computer Science
Illinois Institute of Technology
10 W 31st, Chicago, IL 60616
I will get Ph.D. degree from the computer science department of Illinois Institute of Technology in August 2023. I am advised by Prof. Zhiling Lan and Prof. Michael E. Papka. My research interests focus on resource management on high performance computing systems.
Education:
Chicago, IL, US Ph.D in Computer Science Illinois Institute of Technology
Potsdam, NY, US M.S in Electrical Engineering Clarkson University
Tianjin, China B.S in Electronic Information Engineering Tianjin University
Skills:
C/C++, Python, Java
Vim, Git
Linux, shell
HTML, Javascript, CSS, MySQL
Intership:
Argonne National Lab Research Aid ALCF Group Advisor: Sudheer Chunduri 2018.6-2018.8
Argonne National Lab Research Aid ALCF Group Advisor: Kevin Harms 2019.5-2019.8
Argonne National Lab Research Aid ALCF Group Advisor: Eric Pershey 2020.5-2020.7
Publication:
Li, Boyang, Sudheer Chunduri, Kevin Harms, Yuping Fan, and Zhiling Lan. "The effect of system utilization on application performance variability." In Proceedings of the 9th International Workshop on Runtime and Operating Systems for Supercomputers, pp. 11-18. 2019.
Li, Boyang, Yuping Fan, Matthew Dearing, Zhiling Lan, Paul Rich, William Allcock, and Michael Papka. "MRSch: Multi-Resource Scheduling for HPC." In 2022 IEEE International Conference on Cluster Computing (CLUSTER), pp. 47-57. IEEE, 2022.
Li, Boyang, Yuping Fan, Michael E. Papka, and Zhiling Lan. "Encoding for Reinforcement Learning Driven Scheduling." In Workshop on Job Scheduling Strategies for Parallel Processing, pp. 68-87. Cham: Springer Nature Switzerland, 2022.
Fan, Yuping, Boyang Li, Dustin Favorite, Naunidh Singh, Taylor Childers, Paul Rich, William Allcock, Michael E. Papka, and Zhiling Lan. "Dras: Deep reinforcement learning for cluster scheduling in high performance computing." IEEE Transactions on Parallel and Distributed Systems 33, no. 12 (2022): 4903-4917.
Project:
Small: Toward Smart HPC through Active Learning and Intelligent Scheduling
IRON: Reducing Workload Interference on Massively Parallel Platforms
MINT: Multi-resource INtelligenT management of hybrid workloads