Younghyun Cho



Welcome to my webpage!

I am currently an Assistant Professor at Santa Clara University (SCU) in the department of computer science and engineering. Before joining SCU, I was a Postdoctoral Scholar at UC Berkeley. I obtained my Ph.D. from Seoul National University in 2020.

My research lies in the areas of parallel and high-performance computing (HPC). I am currently focusing on various kinds of performance engineering issues on high performance computers for various HPC codes and emering ML and quantum computing workloads, through automatic performance tuning, parallel and distributed infrastructure/framework, performance profiling and analysis techniques.

I am leading the SCU's Systems & Performance Research Lab.

Contact: younghyun.cho@scu.edu
[GitHub] [Linkedin] [Google Scholar] [CV]

Last update: August 22, 2024.

Publications

(all publications including full journal/conference papers, pre-prints, and workshop/poster presentations)

2024
  • [IJHPCA'24] Hengrui Luo, Younghyun Cho, James W Demmel, Igor Kozachenko, Xiaoye S Li, Yang Liu, "Non-smooth Bayesian Optimization in Tuning Scientific Applications", Accepted for International Journal of High Performance Computing Applications, 2024.
  • [JCGS'24] Hengrui Luo, Younghyun Cho, James W. Demmel, Xiaoye S. Li, and Yang Liu, "Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization", in Journal of Computational and Graphical Statistics, March 2024.

    • 2023
  • [arXiv-preprint'23] Younghyun Cho, James W Demmel, Michał Dereziński, Haoyun Li, Hengrui Luo, Michael W Mahoney, Riley J Murray, "Surrogate-based Autotuning for Randomized Sketching Algorithms in Regression Problems", arXiv preprint arXiv:2308.15720, August 2023.
  • [MLArchSys'23] Grace Dinh, Iniyaal Kannan Jegadesan Valsala, Hengrui Luo, Charles Hong, Younghyun Cho, James Demmel, Sherry Li, Yang Liu, "Sample-Efficient Mapspace Optimization for DNN Accelerators with Bayesian Learning", In Workshop on ML for Computer Architecture and Systems (MLArchSys @ISCA 2023)
  • [IPDPS'23] Younghyun Cho, James W. Demmel, Jacob King, Xiaoye S. Li, Yang Liu, and Hengrui Luo, "Harnessing the Crowd for Autotuning High-Performance Computing Applications", In 37th IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2023.

    • 2022
  • [UserGuide] Younghyun Cho, James W. Demmel, Grace Dinh, Xiaoye S. Li, Yang Liu, Hengrui Luo, Osni Marques, Wissam M. Sid-Lakhdar, "GPTune User Guide (package version: 4.0, release date: October 6, 2022)", https://github.com/gptune/GPTune/blob/master/Doc/GPTune_UsersGuide.pdf, October, 2022.
  • [arXiv-preprint'22] Hengrui Luo, Younghyun Cho, James W. Demmel, Xiaoye S. Li, and Yang Liu, Hybrid Models for Mixed Variables in Bayesian Optimization, arXiv preprint arXiv:2206.01409, June 2022.
  • [SC-Poster'22] Mohammad Zaeed, Tanzima Islam, Younghyun Cho, Xiaoye S. Li, Hengrui Luo, Yang Liu, "Analysis and Visualization of Important Performance Counters To Enhance Interpretability of Autotuner Output", In The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC) (Poster presentation), 2022
  • [PPoPP'22] Younghyun Cho, Jiyeon Park, Florian Negele, Changyeon Jo, Thomas R. Gross, and Bernhard Egger. "Dopia: Online Parallelism Management for Integrated CPU/GPU Architectures." In 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), 2022

    • 2021
  • [arXiv-preprint'21] Hengrui Luo, James W. Demmel, Younghyun Cho, Xiaoye S. Li, and Yang Liu, Non-smooth Bayesian Optimization in Tuning Problems, arXiv preprint arXiv:2109.07563, September 2021.
  • [MCSoC'21] Younghyun Cho, James W. Demmel, Xiaoye S. Li, Yang Liu, and Hengrui Luo, "Enhancing Autotuning Capability with a History Database", In IEEE 14th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), 2021.

    • 2020 and older
  • [JPDC'20] Reza Entezari-Maleki, Younghyun Cho, and Bernhard Egger, "Evaluation of Memory Performance in NUMA Architectures using Stochastic Reward Nets", In Journal of Parallel and Distributed Computing (JPDC), October 2020.
  • [Ph.D. Thesis] Younghyun Cho, "Parallelism Management for Co-Located Parallel Applications, Seoul National University, August 2020.
  • [TPDS'20] Younghyun Cho, Surim Oh, and Bernhard Egger, "Performance Modeling of Parallel Loops on Multi-Socket Platforms using Queueing Systems", In IEEE Transactions on Parallel and Distributed Systems (TPDS), February 2020.
  • [PACT'18a] Younghyun Cho, Camilo A. Celis Guzman, and Bernhard Egger, "Maximizing System Utilization via Parallelism Management for Co-Located Parallel Applications", In Proceedings of the 2018 International Conference on Parallel Architectures and Compilation Techniques, Limassol, Cyprus, November 2018.
  • [PACT'18b] Younghyun Cho, Florian Negele, Seohong Park, Bernhard Egger, and Thomas R. Gross, "On-The-Fly Workload Partitioning for Integrated CPU/GPU Architectures", In Proceedings of the 2018 International Conference on Parallel Architectures and Compilation Techniques, Limassol, Cyprus, November 2018.
  • [PACT-Poster'17] Younghyun Cho, Camilo A. Celis Guzman, and Bernhard Egger, "POSTER: Improving NUMA System Efficiency with a Utilization-based Co-scheduling", In Proceedings of the 2017 International Conference on Parallel Architectures and Compilation Techniques, Portland, USA, September 2017
  • [MULTIPROG'17] Younghyun Cho, Surim Oh, and Bernhard Egger, "Cooperative Parallel Runtimes for Multicores", Presented at the 10th International Workshop on Programmability and Architectures for Heterogeneous Multicores, Stockholm, Sweden, Januray 2017
  • [MULTIPROG'17] Camilo A. Celis Guzman, Younghyun Cho, and Bernhard Egger, "SnuMAP: an Open-source Trace Profiler for Manycore Systems", Presented at the 10th International Workshop on Programmability and Architectures for Heterogeneous Multicores, Stockholm, Sweden, Januray 2017
  • [PACT'16] Younghyun Cho, Surim Oh, and Bernhard Egger, "Online Scalability Characterization of Data-parallel Programs on Many Cores", In Proceedings of the 26th International Conference on Parallel Architectures and Compilation Techniques, Haifa, Israel, September 2016.
  • [CATC'16] Surim Oh, Younghyun Cho, and Bernhard Egger, "Efficient Resource Management for Many-cores with Centralized L2 Caches using Distributed Control Processors", Presented at the 7th Compiler, Architectures and Tools Conference, Haifa, Israel, September 2016.
  • [JSSPP'16] Younghyun Cho, Surim Oh, and Bernhard Egger, "Adaptive Space-shared Scheduling for Shared-memory Parallel Programs", Presented at the 20th Workshop on Job Scheduling Strategies for Parallel Processing, Chicago, USA, May 2016. In Lecture Notes in Computer Science (LNCS), Volumne 10353, pp. 158-177, July 2016.
  • [TC'16] Bernhard Egger, Eunbyung Park, Younghyun Cho, Changyeon Jo, and Jaejin Lee, "Efficient Checkpointing of Live Virtual Machines", In IEEE Transactions on Computers (TC), Volume 65, Issue 10, pp. 3041 - 3054, Januray 2016.

    • Teaching

  • As an assistant professor at Santa Clara University
    • CSEN319, Parallel Computing, Fall 2024
    • CSEN122, Computer Architecture, Spring 2024
    • CSEN20, Introduction to Embedded Systems, Fall 2023, Winter 2024
  • As teaching assistant roles at Seoul National University
    • 4190.570, Advanced Compiler Construction, Fall 2018
    • 4190.409, Compilers, Fall 2017, Fall 2014
    • 4190.203, System Programming, Fall 2013, Spring 2013

    • Activities

  • Organizing committee: LCTES 2023 (publicity chair and artifact evaluation co-chair)
  • Program committee: IPDPS 2022, LCTES 2023, GrAPL 2024
  • Artifact evaluation committee: PPoPP 2018, PPoPP 2019, LCTES 2019.
  • Award:
    • [SnuMAP] Younghyun Cho, Camilo A. Celis Guzman, Heesik Shin, Surim Oh, 2nd Prize, in Open Source Software World Challenge 2016, Seoul, South Korea, December 2016. [GitHub]