Analyzing the Performance of Graph Neural Networks with Pipe Parallelism. - Matthew T. Dearing; Xiaoyan Wang (CoRR, 2020)

MRSch: Multi-Resource Scheduling for HPC. - Boyang Li; Yuping Fan; Matthew T. Dearing; Zhiling Lan; Paul Rich; William E. Allcock; Michael E. Papka (CLUSTER, 2022)

Opportunities for Retrieval and Tool Augmented Large Language Models in Scientific Facilities. - Michael H. Prince; Henry Chan; Aikaterini Vriza; Tao Zhou; Varuni K. Sastry; Matthew T. Dearing; Ross J. Harder; Rama K. Vasudevan; Mathew J. Cherukara (CoRR, 2023)

LASSI: An LLM-Based Automated Self-Correcting Pipeline for Translating Parallel Scientific Codes. - Matthew T. Dearing; Yiheng Tao; Xingfu Wu; Zhiling Lan; Valerie Taylor 001 (CLUSTER Workshops, 2024)

Deep Learning Surrogate Models for Network Simulation. - Matthew T. Dearing (SIGSIM-PADS, 2024)

MRSch: Multi-Resource Scheduling for HPC. - Boyang Li; Yuping Fan; Matthew T. Dearing; Zhiling Lan; Paul Richy; William Allcocky; Michael E. Papka (CoRR, 2024)

Benchmarking large language models for materials synthesis: the case of atomic layer deposition. - Angel Yanguas-Gil; Matthew T. Dearing; Jeffrey W. Elam; Jessica C. Jones; Sungjoon Kim; Adnan Mohammad; Chi Thang Nguyen; Bratin Sengupta (CoRR, 2024)

Generative AI Uses and Risks for Knowledge Workers in a Science Organization. - Kelly B. Wagman; Matthew T. Dearing; Marshini Chetty (CHI, 2025)

Leveraging LLMs to Automate Energy-Aware Refactoring of Parallel Scientific Codes. - Matthew T. Dearing; Yiheng Tao; Xingfu Wu; Zhiling Lan; Valerie Taylor 001 (CoRR, 2025)

Extracting Practical, Actionable Energy Insights from Supercomputer Telemetry and Logs. - Melanie Cornelius; Greg Cross; Shilpika Shilpika; Matthew T. Dearing; Zhiling Lan (CoRR, 2025)