Profile
Senior Researcher at Microsoft, studying scalable AI and large-scale GPU systems. Received Ph.D. in Electrical Engineering from KAIST in February 2022, advised by Prof. KyoungSoo Park.
Research Interest
Artificial Intelligence, scalable networked systems, system performance optimization, GPU systems
Education
Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
Ph.D. (Integrated M.S.-Ph.D. Program), Electrical Engineering
Feb 2017 — Feb 2022
M.S. student, Electrical Engineering
Feb 2016 — Jan 2017
B.S., Electrical Engineering (major) and Computer Science (minor)
Feb 2012 — Jan 2016
Professional Experience
Microsoft Research, Vancouver, BC, Canada
Senior Researcher
Jan 2024 — Present
Microsoft Research, Beijing, China
Senior Researcher, Networking Infrastructure Group
Dec 2023 — Dec 2023
Researcher 2, Networking Infrastructure Group
Mar 2022 — Nov 2023
Research Intern, Networking Research Group
Jul 2019 — Sep 2019 ; Dec 2018 — Feb 2019
Publications
MSCCL++: Rethinking GPU Communication Abstractions for AI Inference
Changho Hwang, Peng Cheng, Roshan Dathathri, Abhinav Jangda, Saeed Maleki, Madan Musuvathi, Olli Saarikivi, Aashaka Shah, Ziyue Yang, Sreevatsa Anantharamu, Mahdieh Ghazimirsaeed, Jithin Jose, Binyang Li, Caio Rocha, Qinghua Zhou
In the ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)
Pittsburgh, USA, March 2026 (to appear)
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference
Ranggi Hwang, Jianyu Wei, Shijie Cao, Changho Hwang, Xiaohu Tang, Ting Cao, and Mao Yang
In the 51st International Symposium on Computer Architecture (ISCA)
Buenos Aires, Argentina, June 2024
Tutel: Adaptive Mixture-of-Experts at Scale
Changho Hwang, Wei Cui, Yifan Xiong, Ziyue Yang, Ze Liu, Han Hu, Zilong Wang, Rafael Salas, Jithin Jose, Prabhat Ram, Joe Chau, Peng Cheng, Fan Yang, Mao Yang, and Yongqiang Xiong
In the 6th Conference on Machine Learning and Systems (MLSys)
Miami, FL, June 2023
ARK: GPU-driven Code Execution for Distributed Deep Learning
Changho Hwang, KyoungSoo Park, Ran Shu, Xinyuan Qu, Peng Cheng, and Yongqiang Xiong
In the 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI)
Boston, MA, April 2023
Elastic Resource Sharing for Distributed Deep Learning
Changho Hwang, Taehyun Kim, Sunghyun Kim, Jinwoo Shin, and KyoungSoo Park
In the 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI)
Virtual Event, April 2021
Confident Multiple Choice Learning
Kimin Lee, Changho Hwang, KyoungSoo Park, and Jinwoo Shin
In the 34th International Conference on Machine Learning (ICML)
Sydney, Australia, August 2017
APUNet: Revitalizing GPU as Packet Processing Accelerator
Younghwan Go, Muhammad Jamshed, YoungGyoun Moon, Changho Hwang, and KyoungSoo Park
In the 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI)
Boston, MA, March 2017
Preprints
Alchemist: Towards the Design of Efficient Online Continual Learning System
Yuyang Huang, Yuhan Liu, Haryadi S. Gunawi, Beibin Li, Changho Hwang
arXiv
March 2025
Workshops & Posters
Immediate Communication for Distributed AI Tasks
Jihao Xin, Seongjong Bae, KyoungSoo Park, Marco Canini, Changho Hwang
In the 2nd Workshop on Hot Topics in System Infrastructure (HotInfra)
Austin, TX, November 2024
Towards GPU-driven Code Execution for Distributed Deep Learning (Awarded Best Paper)
Changho Hwang, KyoungSoo Park, Ran Shu, Xinyuan Qu, Peng Cheng, and Yongqiang Xiong
In the 3rd Machine Learning for Computer Architecture and Systems (MLArchSys@ISCA)
New York City, NY, June 2022
Accelerating GNN Training with Locality-Aware Partial Execution (Awarded Best Paper)
Taehyun Kim, Changho Hwang, KyoungSoo Park, Zhiqi Lin, Peng Cheng, Youshan Miao, Lingxiao Ma, and Yongqiang Xiong
In the 12th ACM SIGOPS Asia-Pacific Workshop on Systems (APSys)
Virtual Event, August 2021
A Case for Two-stage Inference with Knowledge Caching
Geonha Park, Changho Hwang, and KyoungSoo Park
In the 3rd International Workshop on Embedded and Mobile Deep Learning (EMDL@MobiSys)
Seoul, Republic of Korea, June 2019
Efficient Resource Sharing for Distributed Deep Learning
Changho Hwang, Taehyun Kim, Kyuho Son, Jinwoo Shin, and KyoungSoo Park
In the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI) — poster
Carlsbad, CA, October 2018
Awards