2025

[IJHPCA'25] Adrian P. Dieguez, Seth Ockerman, Tristan Aikman, Younghyun Cho, Yang Liu, Khaled Z. Ibrahim, "Parallelizing Autotuning for HPC Applications: Unveiling the Potential of the Speculation Strategy in Bayesian Optimization", in International Journal of High Performance Computing Applications, 2025.

[MLArchSys'25] Chianing Wang and Younghyun Cho, "Empirical Training Time Prediction for LLM Fine-Tuning Using Scaling Laws", in Workshop on ML for Computer Architecture and Systems (MLArchSys @ISCA 2025).

[DAC'25] Jason Han, Nicholas S. DiBrita, Younghyun Cho, Hengrui Luo and Tirthak Patel, "Fast Amplitude Embedding for Quantum Machine Learning using Classical Data", in Design Automation Conference (DAC) 2025.

[SIMAX'25] 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", SIAM Journal on Matrix Analysis and Applications (SIMAX), 2025. (preprint version: arXiv:2308.15720).

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", International Journal of High Performance Computing Applications, 2024. (early version pre-print: arXiv:2109.07563).

[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. (early version pre-print: arXiv:2206.01409).

2023 and older

[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.

[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.

[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

[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.

[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.