Dr. Pei-Hung Lin is a computer scientist in the Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory (LLNL). His research expertise spans compiler optimizations, parallel programming models, and the integration of machine learning techniques into high-performance computing (HPC) workflows. He received his Ph.D. in Computer Science from the University of Minnesota.

Research Experience

Before joining LLNL, Dr. Lin contributed to multiple National Science Foundation (NSF)–funded projects that advanced compiler analyses and performance portability research. Since joining LLNL in 2013, he has contributed to Department of Energy (DOE)–funded initiatives aimed at enhancing the performance, portability, and correctness of large-scale scientific applications. As a core developer of the ROSE compiler framework, he spearheads developments in source-to-source transformations, static analysis, and automated code generation.

In recent years, Dr. Lin’s work has focused on leveraging large language models (LLMs) to automate and accelerate HPC code optimization, translation between programming languages, and intelligence-driven performance tuning. He has organized and chaired workshops on software correctness and AI-driven tools at major HPC conferences and regularly presents tutorials on integrating machine learning with compiler research.

Dr. Lin also actively mentors early-career researchers and summer interns, fostering collaboration between HPC and AI communities. His leadership and technical contributions continue to shape next-generation tools for scalable, efficient scientific computing.

Ph.D. Computer Science, University of Minnesota, Twin Cities, Minnesota

• “HPC-GPT: Integrating Large Language Model for High-Performance Computing.” Xianzhong Ding, Le Chen, Murali Emani, Chunhua Liao, Pei-Hung Lin, Tristan Vanderbruggen, Zhen Xie, Alberto E. Cerpa, Wan Du. In Proceedings of the SC ’23 Workshops (High Performance Computing, Networking, Storage and Analysis Workshops) – Denver, CO, 2023
• “Data Race Detection Using Large Language Models.” Le Chen, Xianzhong Ding, Murali Emani, Tristan Vanderbruggen, Pei-Hung Lin, Chunhua Liao. In Proceedings of the SC ’23 Workshops (High Performance Computing, Networking, Storage and Analysis Workshops) – Denver, CO, 2023
• “LM4HPC: Towards Effective Language Model Application in High-Performance Computing.” Le Chen, Pei-Hung Lin, Tristan Vanderbruggen, Chunhua Liao, Murali Emani, Bronis R. de Supinski. International Workshop on OpenMP (IWOMP 2023) – Bristol, UK, 2023
• “Creating a Dataset Supporting Translation Between OpenMP Fortran and C++ Code.” Bin Lei, Caiwen Ding, Le Chen, Pei-Hung Lin, Chunhua Liao. IEEE High Performance Extreme Computing Conference (HPEC 2023) – Waltham, MA, 2023
• “Towards Seamless Management of AI Models in High-Performance Computing.” Sixing Yu, Murali Emani, Chunhua Liao, Pei-Hung Lin, Tristan Vanderbruggen, Xipeng Shen, Ali Jannesari. AAAI Workshop on AI to Accelerate Science and Engineering – Washington, D.C., 2023
• “Generating and Analyzing Program Call Graphs Using Ontology.” Ethan Dorta, Yonghong Yan, Chunhua Liao. (With contributions by Pei-Hung Lin) Workshop on Programming and Performance Visualization Tools (SC’22 Workshop) – Dallas, TX, 2022
• “Early Experience with Transformer-Based Similarity Analysis for DataRaceBench.” Winson Chen, Tristan Vanderbruggen, Pei-Hung Lin, Chunhua Liao, Murali Emani. 6th Intl. Workshop on Software Correctness for HPC Applications (Correctness 2022) – Dallas, TX, 2022
• “Interactive NLU-Powered Ontology-Based Workflow Synthesis for FAIR Support of HPC.” Zifan Nan, Mithil Dave, Xipeng Shen, Chunhua Liao, Tristan Vanderbruggen, Pei-Hung Lin, Murali Emani. IEEE/ACM 9th Intl. Workshop on HPC User Support Tools (HUST 2022) – Dallas, TX, 2022
• “Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study.” Pei-Hung Lin, Chunhua Liao, Winson Chen, Tristan Vanderbruggen, Murali Emani, Hailu Xu. 1st Workshop on Trustable and Ethical ML for HPC (TransAI Conference) – Laguna Hills, CA, 2022
• “Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelines.” Patrick Flynn, Tristan Vanderbruggen, Chunhua Liao, Pei-Hung Lin, Murali Emani, Xipeng Shen. 2nd Intl. Workshop on Software Architecture and Machine Learning – 2022
• “HPCFAIR: Enabling FAIR AI for HPC Applications.” Gaurav Verma, Murali Emani, Chunhua Liao, Pei-Hung Lin, Tristan Vanderbruggen, Xipeng Shen, Barbara Chapman. IEEE/ACM Workshop on Machine Learning in HPC Environments (MLHPC 2021) – 2021
• “HPC Ontology: Towards a Unified Ontology for Managing Training Datasets and AI Models for High-Performance Computing.” Chunhua Liao, Pei-Hung Lin, Gaurav Verma, Tristan Vanderbruggen, Murali Emani, Zifan Nan, Xipeng Shen. IEEE/ACM Workshop on Machine Learning in HPC Environments (MLHPC 2021) – 2021
• “High-Precision Evaluation of Both Static and Dynamic Tools Using DataRaceBench.” Wenhao Wu, Chunhua Liao, Stephen F. Siegel, Pei-Hung Lin. 5th Intl. Workshop on Software Correctness for HPC Applications (Correctness 2021) – St. Louis, MO, 2021
• “DataRaceBench: A Benchmark Suite for Systematic Evaluation of Data Race Detection Tools.” Chunhua Liao, Pei-Hung Lin, Joshua Asplund, Markus Schordan, Ian Karlin. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC’17) – Denver, CO, 2017
• “Supporting Multiple Accelerators in High-Level Programming Models.” Yonghong Yan, Pei-Hung Lin, Chunhua Liao, Bronis R. de Supinski, Daniel J. Quinlan. 6th International Workshop on Programming Models and Applications for Multicores and Manycores (PMAM 2015) – San Francisco, CA, 2015
• “Verification of Polyhedral Optimizations with Constant Loop Bounds in Finite State Space Computations.” Markus Schordan, Pei-Hung Lin, Daniel Quinlan, Louis-Noël Pouchet. 6th Intl. Symposium on Leveraging Applications of Formal Methods (ISoLA 2014) – LNCS 8803, pp.493–508, 2014
• “Revisiting Loop Fusion in the Polyhedral Framework.” Sanyam Mehta, Pei-Hung Lin, Pen-Chung Yew. ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP 2014) – Orlando, FL, 2014
• “Simulating Turbulent Mixing from Richtmyer–Meshkov and Rayleigh–Taylor Instabilities in Converging Geometries using Moving Cartesian Grids.” Paul R. Woodward, J. Jayaraj, P.-H. Lin, et al. Developments in Computational Science (DECDC) – Livermore, CA, 2012
• “A Study of Performance Portability Using Piecewise-Parabolic Method (PPM) Gas Dynamics Applications.” Pei-Hung Lin, J. Jayaraj, P. R. Woodward, Pen-Chung Yew. International Conference on Computational Science (ICCS 2012) – Omaha, NE, 2012
• “The Late-Time Dynamics of the Single-Mode Rayleigh–Taylor Instability.” P. Ramaprabhu, G. Dimonte, P. R. Woodward, C. Fryer, G. Rockefeller, K. Muthuraman, P.-H. Lin, J. Jayaraj. Physics of Fluids, 24(7):074107, 2012
• “A Study of the Performance of Multifluid PPM Gas Dynamics on CPUs and GPUs.” Pei-Hung Lin, J. Jayaraj, P. R. Woodward. Symposium on Application Accelerators in HPC (SAAHPC 2011) – Urbana, IL, 2011
• “A Code Transformation Framework for Scientific Applications on Structured Grids.” Pei-Hung Lin, J. Jayaraj, P. R. Woodward, Pen-Chung Yew. University of Minnesota Technical Report (UMN CSE Dept. TR 11-021), September 2011
• “A Strategy for Automatically Generating High-Performance CUDA Code for a GPU Accelerator from a Specialized Fortran Code Expression.” Pei-Hung Lin, Jagan Jayaraj, Paul Woodward. Symposium on Application Accelerators in HPC (SAAHPC 2010) – Poster, 2010
• “Boosting the Performance of Computational Fluid Dynamics Codes for Interactive Supercomputing.” Paul R. Woodward, J. Jayaraj, P.-H. Lin, P.-C. Yew, et al. International Conference on Computational Science (ICCS 2010) – Amsterdam, 2010
• “Interactive Supercomputing Enabled by Cell Processor Accelerators.” Paul R. Woodward, Jagan Jayaraj, Pei-Hung Lin, W. Dai. Symposium on Application Accelerators in HPC (SAAHPC 2010) – Urbana, IL, 2010
• “First Experience of Compressible Gas Dynamics Simulation on the Los Alamos Roadrunner Machine.” P. R. Woodward, J. Jayaraj, P.-H. Lin, W. Dai. Concurrency and Computation: Practice and Experience, 21(17):2160–2175, 2009
• “Moving Scientific Codes to IBM Cell Processor and Other Multicore Microprocessor CPUs.” P. R. Woodward, J. Jayaraj, P.-H. Lin, P.-C. Yew, et al. IEEE Computing in Science & Engineering, 10(6):16–25, Nov/Dec 2008

• SC17 Best Paper Finalist (2017): Co-authored the DataRaceBench benchmark suite paper recognized as a finalist for the Best Paper Award at the SC17 Supercomputing conference.

• IEEE HPEC Outstanding Student Paper Award (2023): Co-author of “Creating a Dataset for HPC Code Translation using LLMs,” honored at IEEE HPEC 2023.