4 papers
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…
Coordinated Power Management on Heterogeneous Systems
Zhong Zheng, Zhiling Lan, Xingfu Wu +2
Performance prediction is essential for energy-efficient computing in heterogeneous computing systems that integrate CPUs and GPUs. However, traditional performance modeling method…
Generalizing Scaling Laws for Dense and Sparse Large Language Models
Md Arafat Hossain, Xingfu Wu, Valerie Taylor +1
Despite recent advancements of large language models (LLMs), optimally predicting the model size for LLM pretraining or allocating optimal resources still remains a challenge. Seve…
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…