3 papers
cs.AI2026
Leveraging LLMs to Automate Energy-Aware Refactoring of Parallel Scientific Codes
Matthew T. Dearing, Yiheng Tao, Xingfu Wu +2
Large language models (LLMs) are increasingly used for generating parallel scientific codes, with a primary focus on generating functionally correct code. Recent work has focused o…
cs.DC2025
Extracting Practical, Actionable Energy Insights from Supercomputer Telemetry and Logs
Melanie Cornelius, Greg Cross, Shilpika Shilpika +2
As supercomputers grow in size and complexity, power efficiency has become a critical challenge, particularly in understanding GPU power consumption within modern HPC workloads. Th…
cs.SE2025
LASSI: An LLM-based Automated Self-Correcting Pipeline for Translating Parallel Scientific Codes
Matthew T. Dearing, Yiheng Tao, Xingfu Wu +2
This paper addresses the problem of providing a novel approach to sourcing significant training data for LLMs focused on science and engineering. In particular, a crucial challenge…