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Journal Articles
- 2022
[j5]Hailu Xu
, Pei-Hung Lin, Murali Emani, Liting Hu, Chunhua Liao:
XUnified: A Framework for Guiding Optimal Use of GPU Unified Memory. IEEE Access 10: 82614-82625 (2022)- 2020
- [j4]Ramesh Pankajakshan, Pei-Hung Lin, Björn Sjögreen:
Porting a 3D seismic modeling code (SW4) to CORAL machines. IBM J. Res. Dev. 64(3/4): 17:1-17:11 (2020) - 2016
- [j3]Pei-Hung Lin, Paul R. Woodward:
Transforming the multifluid PPM algorithm to run on GPUs. J. Parallel Distributed Comput. 93-94: 56-65 (2016) - 2009
- [j2]Paul R. Woodward, Jagan Jayaraj, Pei-Hung Lin, William Dai:
First experience of compressible gas dynamics simulation on the Los Alamos roadrunner machine. Concurr. Comput. Pract. Exp. 21(17): 2160-2175 (2009) - 2008
- [j1]Paul R. Woodward, Jagan Jayaraj, Pei-Hung Lin, Pen-Chung Yew:
Moving Scientific Codes to Multicore Microprocessor CPUs. Comput. Sci. Eng. 10(6): 16-25 (2008)
Conference and Workshop Papers
- 2024
- [c35]Chia-Chen Hsu, Chun-Lin Huang, Chao-Lin Lee, Jenq-Kuen Lee, Pei-Hung Lin:
The Rewriting of DataRaceBench Benchmark for OpenCL Program Validations. ICPP Workshops 2024: 15-22 - [c34]Naveed Sekender, Pei-Hung Lin, Chunhua Liao:
Finding Equivalent OpenMP Fortran and C/C++ Code Snippets Using Large Language Models. IWOMP 2024: 143-160 - 2023
- [c33]Bin Lei, Caiwen Ding, Le Chen, Pei-Hung Lin, Chunhua Liao:
Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C++. HPEC 2023: 1-7 - [c32]Le Chen, Pei-Hung Lin, Tristan Vanderbruggen, Chunhua Liao, Murali Emani, Bronis R. de Supinski:
LM4HPC: Towards Effective Language Model Application in High-Performance Computing. IWOMP 2023: 18-33 - [c31]Le Chen, Xianzhong Ding, Murali Emani, Tristan Vanderbruggen, Pei-Hung Lin, Chunhua Liao:
Data Race Detection Using Large Language Models. SC Workshops 2023: 215-223 - [c30]Xianzhong Ding, Le Chen, Murali Emani, Chunhua Liao, Pei-Hung Lin, Tristan Vanderbruggen, Zhen Xie, Alberto Cerpa, Wan Du:
HPC-GPT: Integrating Large Language Model for High-Performance Computing. SC Workshops 2023: 951-960 - 2022
- [c29]Winson Chen, Tristan Vanderbruggen, Pei-Hung Lin, Chunhua Liao, Murali Emani:
Early Experience with Transformer-Based Similarity Analysis for DataRaceBench. Correctness@SC 2022: 45-53 - [c28]Patrick J. Flynn, Tristan Vanderbruggen, Chunhua Liao, Pei-Hung Lin, Murali Emani, Xipeng Shen:
Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelines. ECSA (Tracks and Workshops) 2022: 402-417 - [c27]Zifan Nan, Mithil Dave, Xipeng Shen, Chunhua Liao, Tristan Vanderbruggen, Pei-Hung Lin, Murali Emani:
Interactive NLU-Powered Ontology-Based Workflow Synthesis for FAIR Support of HPC. HUST@SC 2022: 29-40 - [c26]Brandon Nesterenko, Qing Yi, Pei-Hung Lin, Chunhua Liao, Brandon Runnels:
Modeling optimization of stencil computations via domain-level properties. PMAM@PPoPP 2022: 35-44 - [c25]Pei-Hung Lin, Chunhua Liao, Winson Chen, Tristan Vanderbruggen, Murali Emani, Hailu Xu:
Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study. TransAI 2022: 128-134 - 2021
- [c24]Pei-Hung Lin, Chunhua Liao:
High-Precision Evaluation of Both Static and Dynamic Tools using DataRaceBench. Correctness@SC 2021: 1-8 - [c23]Wenwen Wang, Pei-Hung Lin:
Does it matter?: OMPSanitizer: an impact analyzer of reported data races in OpenMP programs. ICS 2021: 40-51 - [c22]Gaurav Verma, Murali Emani, Chunhua Liao, Pei-Hung Lin, Tristan Vanderbruggen, Xipeng Shen, Barbara M. Chapman:
HPCFAIR: Enabling FAIR AI for HPC Applications. MLHPC@SC 2021: 58-68 - [c21]Chunhua Liao, Pei-Hung Lin, Gaurav Verma, Tristan Vanderbruggen, Murali Emani, Zifan Nan, Xipeng Shen:
HPC Ontology: Towards a Unified Ontology for Managing Training Datasets and AI Models for High-Performance Computing. MLHPC@SC 2021: 69-80 - 2020
- [c20]Peter Pirkelbauer, Pei-Hung Lin, Tristan Vanderbruggen, Chunhua Liao:
XPlacer: Automatic Analysis of Data Access Patterns on Heterogeneous CPU/GPU Systems. IPDPS 2020: 997-1007 - 2019
- [c19]Pei-Hung Lin, Chunhua Liao, Markus Schordan, Ian Karlin:
Exploring Regression of Data Race Detection Tools Using DataRaceBench. CORRECTNESS@SC 2019: 11-18 - [c18]Ian Karlin, Yoonho Park, Bronis R. de Supinski, Peng Wang, Bert Still, David Beckingsale, Robert Blake, Tong Chen, Guojing Cong, Carlos H. A. Costa, Johann Dahm, Giacomo Domeniconi, Thomas Epperly, Aaron Fisher, Sara Kokkila Schumacher, Steven H. Langer, Hai Le, Eun Kyung Lee, Naoya Maruyama, Xinyu Que, David F. Richards, Björn Sjögreen, Jonathan Wong, Carol S. Woodward, Ulrike Meier Yang, Xiaohua Zhang, Bob Anderson, David Appelhans, Levi Barnes, Peter D. Barnes Jr., Sorin Bastea, David Böhme, Jamie A. Bramwell, James M. Brase, José R. Brunheroto, Barry Chen, Charway R. Cooper, Tony Degroot, Robert D. Falgout, Todd Gamblin, David J. Gardner, James N. Glosli, John A. Gunnels, Max P. Katz, Tzanio V. Kolev, I-Feng W. Kuo, Matthew P. LeGendre, Ruipeng Li, Pei-Hung Lin, Shelby Lockhart, Kathleen McCandless, Claudia Misale, Jaime H. Moreno, Rob Neely, Jarom Nelson, Rao Nimmakayala, Kathryn M. O'Brien, Kevin O'Brien, Ramesh Pankajakshan, Roger Pearce, Slaven Peles, Phil Regier, Steven C. Rennich, Martin Schulz, Howard Scott, James C. Sexton, Kathleen Shoga, Shiv Sundram, Guillaume Thomas-Collignon, Brian Van Essen, Alexey Voronin, Bob Walkup, Lu Wang, Chris Ward, Hui-Fang Wen, Daniel A. White, Christopher Young, Cyril Zeller, Edward Zywicz:
Preparation and optimization of a diverse workload for a large-scale heterogeneous system. SC 2019: 32:1-32:17 - [c17]Hailu Xu, Murali Emani, Pei-Hung Lin, Liting Hu, Chunhua Liao:
Machine Learning Guided Optimal Use of GPU Unified Memory. MCHPC@SC 2019: 64-70 - 2018
- [c16]Pei-Hung Lin, Chunhua Liao, Markus Schordan, Ian Karlin:
Runtime and Memory Evaluation of Data Race Detection Tools. ISoLA (2) 2018: 179-196 - [c15]Chunhua Liao, Pei-Hung Lin, Markus Schordan, Ian Karlin:
A Semantics-Driven Approach to Improving DataRaceBench's OpenMP Standard Coverage. IWOMP 2018: 189-202 - [c14]Fangke Ye, Markus Schordan, Chunhua Liao, Pei-Hung Lin, Ian Karlin, Vivek Sarkar:
Using Polyhedral Analysis to Verify OpenMP Applications are Data Race Free. CORRECTNESS@SC 2018: 42-50 - [c13]Larisa Stoltzfus, Murali Emani, Pei-Hung Lin, Chunhua Liao:
Data Placement Optimization in GPU Memory Hierarchy using Predictive Modeling. MCHPC@SC 2018: 45-49 - [c12]Md Abdullah Shahneous Bari, Larisa Stoltzfus, Pei-Hung Lin, Chunhua Liao, Murali Emani, Barbara M. Chapman:
Is Data Placement Optimization Still Relevant on Newer GPUs? PMBS@SC 2018: 83-96 - 2017
- [c11]Chunhua Liao, Pei-Hung Lin, Joshua Asplund, Markus Schordan, Ian Karlin:
DataRaceBench: a benchmark suite for systematic evaluation of data race detection tools. SC 2017: 11 - [c10]Markus Schordan, Jan Hückelheim, Pei-Hung Lin, Harshitha Menon:
Verifying the Floating-Point Computation Equivalence of Manually and Automatically Differentiated Code. CORRECTNESS@SC 2017: 34-41 - 2016
- [c9]Pei-Hung Lin, Qing Yi, Daniel J. Quinlan, Chunhua Liao, Yongqing Yan:
Automatically Optimizing Stencil Computations on Many-Core NUMA Architectures. LCPC 2016: 137-152 - 2015
- [c8]Pei-Hung Lin, Chunhua Liao, Daniel J. Quinlan, Stephen M. Guzik:
Experiences of Using the OpenMP Accelerator Model to Port DOE Stencil Applications. IWOMP 2015: 45-59 - [c7]Yonghong Yan, Pei-Hung Lin, Chunhua Liao, Bronis R. de Supinski, Daniel J. Quinlan:
Supporting multiple accelerators in high-level programming models. PMAM@PPoPP 2015: 170-180 - [c6]Chunhua Liao, Pei-Hung Lin, Daniel J. Quinlan, Yue Zhao, Xipeng Shen:
Enhancing domain specific language implementations through ontology. WOLFHPC@SC 2015: 3:1-3:9 - 2014
- [c5]Jagan Jayaraj, Pei-Hung Lin, Paul R. Woodward, Pen-Chung Yew:
CFD Builder: A Library Builder for Computational Fluid Dynamics. IPDPS Workshops 2014: 1029-1038 - [c4]Markus Schordan, Pei-Hung Lin, Daniel J. Quinlan, Louis-Noël Pouchet:
Verification of Polyhedral Optimizations with Constant Loop Bounds in Finite State Space Computations. ISoLA (2) 2014: 493-508 - [c3]Sanyam Mehta, Pei-Hung Lin, Pen-Chung Yew:
Revisiting loop fusion in the polyhedral framework. PPoPP 2014: 233-246 - 2012
- [c2]Pei-Hung Lin, Jagan Jayaraj, Paul R. Woodward, Pen-Chung Yew:
A Study of Performance Portability Using Piecewise-Parabolic Method (PPM) Gas Dynamics Applications. ICCS 2012: 1988-1991 - 2010
- [c1]Paul R. Woodward, Jagan Jayaraj, Pei-Hung Lin, Pen-Chung Yew, Michael R. Knox, James B. S. G. Greensky, Anthony Nowatski, Karl Stoffels:
Boosting the performance of computational fluid dynamics codes for interactive supercomputing. ICCS 2010: 2055-2064
Informal and Other Publications
- 2023
- [i11]Tristan Vanderbruggen, Chunhua Liao, Peter Pirkelbauer, Pei-Hung Lin:
Structured Thoughts Automaton: First Formalized Execution Model for Auto-Regressive Language Models. CoRR abs/2306.10196 (2023) - [i10]Le Chen, Pei-Hung Lin, Tristan Vanderbruggen, Chunhua Liao, Murali Emani, Bronis R. de Supinski:
LM4HPC: Towards Effective Language Model Application in High-Performance Computing. CoRR abs/2306.14979 (2023) - [i9]Bin Lei, Caiwen Ding, Le Chen, Pei-Hung Lin, Chunhua Liao:
Creating a Dataset for High-Performance Computing Code Translation: A Bridge Between HPC Fortran and C++. CoRR abs/2307.07686 (2023) - [i8]Le Chen, Xianzhong Ding, Murali Emani, Tristan Vanderbruggen, Pei-Hung Lin, Chunhua Liao:
Data Race Detection Using Large Language Models. CoRR abs/2308.07505 (2023) - [i7]Le Chen, Wenhao Wu, Stephen F. Siegel, Pei-Hung Lin, Chunhua Liao:
DataRaceBench V1.4.1 and DataRaceBench-ML V0.1: Benchmark Suites for Data Race Detection. CoRR abs/2308.08473 (2023) - [i6]Bin Lei, Pei-Hung Lin, Chunhua Liao, Caiwen Ding:
Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought. CoRR abs/2308.08614 (2023) - [i5]Bin Lei, Sheng Lin, Pei-Hung Lin, Chunhua Liao, Caiwen Ding:
Towards Zero Memory Footprint Spiking Neural Network Training. CoRR abs/2308.08649 (2023) - [i4]Xianzhong Ding, Le Chen, Murali Emani, Chunhua Liao, Pei-Hung Lin, Tristan Vanderbruggen, Zhen Xie, Alberto E. Cerpa, Wan Du:
HPC-GPT: Integrating Large Language Model for High-Performance Computing. CoRR abs/2311.12833 (2023) - 2022
- [i3]Patrick J. Flynn, Tristan Vanderbruggen, Chunhua Liao, Pei-Hung Lin, Murali Emani, Xipeng Shen:
Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelines. CoRR abs/2208.05596 (2022) - [i2]Pei-Hung Lin, Chunhua Liao, Winson Chen, Tristan Vanderbruggen, Murali Emani, Hailu Xu:
Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study. CoRR abs/2211.02092 (2022) - [i1]Sixing Yu, Murali Emani, Chunhua Liao, Pei-Hung Lin, Tristan Vanderbruggen, Xipeng Shen, Ali Jannesari:
Towards Seamless Management of AI Models in High-Performance Computing. CoRR abs/2212.06352 (2022)
Coauthor Index
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