Generating OpenCL C kernels from OpenACC. - Tristan Vanderbruggen; John Cavazos (IWOCL, 2014)

Parallelization of Machine Learning Applied to Call Graphs of Binaries for Malware Detection. - Robert Searles; Lifan Xu; William Killian; Tristan Vanderbruggen; Teague Forren; John Howe; Zachary Pearson; Corey Shannon; Joshua Simmons; John Cavazos (PDP, 2017)

Directive-based tile abstraction to distribute loops on accelerators. - Tristan Vanderbruggen; John Cavazos; Chunhua Liao; Daniel J. Quinlan (GPGPU@PPoPP, 2017)

Tool Integration for Source-Level Mixed Precision. - Michael O. Lam; Tristan Vanderbruggen; Harshitha Menon; Markus Schordan (CORRECTNESS@SC, 2019)

HPC-MixPBench: An HPC Benchmark Suite for Mixed-Precision Analysis. - Konstantinos Parasyris; Ignacio Laguna; Harshitha Menon; Markus Schordan; Daniel Osei-Kuffuor; Giorgis Georgakoudis; Michael O. Lam; Tristan Vanderbruggen (IISWC, 2020)

XPlacer: Automatic Analysis of Data Access Patterns on Heterogeneous CPU/GPU Systems. - Peter Pirkelbauer; Pei-Hung Lin; Tristan Vanderbruggen; Chunhua Liao (IPDPS, 2020)

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 (MLHPC@SC, 2021)

HPCFAIR: Enabling FAIR AI for HPC Applications. - Gaurav Verma; Murali Emani; Chunhua Liao; Pei-Hung Lin; Tristan Vanderbruggen; Xipeng Shen; Barbara M. Chapman (MLHPC@SC, 2021)

Early Experience with Transformer-Based Similarity Analysis for DataRaceBench. - Winson Chen; Tristan Vanderbruggen; Pei-Hung Lin; Chunhua Liao; Murali Emani (Correctness@SC, 2022)

Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelines. - Patrick J. Flynn; Tristan Vanderbruggen; Chunhua Liao; Pei-Hung Lin; Murali Emani; Xipeng Shen (ECSA (Tracks and Workshops), 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 (HUST@SC, 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 (TransAI, 2022)

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 (CoRR, 2022)

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 (IWOMP, 2023)

Data Race Detection Using Large Language Models. - Le Chen; Xianzhong Ding; Murali Emani; Tristan Vanderbruggen; Pei-Hung Lin; Chunhua Liao (SC Workshops, 2023)

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 Cerpa; Wan Du (SC Workshops, 2023)

Structured Thoughts Automaton: First Formalized Execution Model for Auto-Regressive Language Models. - Tristan Vanderbruggen; Chunhua Liao; Peter Pirkelbauer; Pei-Hung Lin (CoRR, 2023)