Matthew T. Dearing

According to our database, Matthew T. Dearing authored at least 10 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Extracting Practical, Actionable Energy Insights from Supercomputer Telemetry and Logs.

Melanie Cornelius, Greg Cross, Shilpika, Matthew T. Dearing, Zhiling Lan

CoRR, May, 2025

Leveraging LLMs to Automate Energy-Aware Refactoring of Parallel Scientific Codes.

Matthew T. Dearing, Yiheng Tao, Xingfu Wu, Zhiling Lan, Valerie Taylor

CoRR, May, 2025

Generative AI Uses and Risks for Knowledge Workers in a Science Organization.

Kelly B. Wagman, Matthew T. Dearing, Marshini Chetty

Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 2025

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

MRSch: Multi-Resource Scheduling for HPC.

Boyang Li, Yuping Fan, Matthew T. Dearing, Zhiling Lan, Paul Richy, William Allcocky, Michael E. Papka

CoRR, 2024

Deep Learning Surrogate Models for Network Simulation.

Matthew T. Dearing

Proceedings of the 38th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, 2024

LASSI: An LLM-Based Automated Self-Correcting Pipeline for Translating Parallel Scientific Codes.

Matthew T. Dearing, Yiheng Tao, Xingfu Wu, Zhiling Lan, Valerie Taylor

Proceedings of the IEEE International Conference on Cluster Computing, 2024

2023

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

2022

MRSch: Multi-Resource Scheduling for HPC.

Boyang Li, Yuping Fan, Matthew T. Dearing, Zhiling Lan, Paul Rich, William E. Allcock, Michael E. Papka

Proceedings of the IEEE International Conference on Cluster Computing, 2022

2020

Analyzing the Performance of Graph Neural Networks with Pipe Parallelism.

Matthew T. Dearing, Xiaoyan Wang

CoRR, 2020