45 citations · 52 across the 19 of their papers we have counts for
3 papers · 1 filter
HPAC-ML: A Programming Model for Embedding ML Surrogates in Scientific Applications
Zane Fink, Konstantinos Parasyris, Praneet Rathi +3
Recent advancements in Machine Learning (ML) have substantially improved its predictive and computational abilities, offering promising opportunities for surrogate modeling in scie…
Performance-Aligned LLMs for Generating Fast Code
Daniel Nichols, Pranav Polasam, Harshitha Menon +3
Optimizing scientific software is a difficult task because codebases are often large and complex, and performance can depend upon several factors including the algorithm, its imple…
Taking GPU Programming Models to Task for Performance Portability
Joshua H. Davis, Pranav Sivaraman, Joy Kitson +5
Portability is critical to ensuring high productivity in developing and maintaining scientific software as the diversity in on-node hardware architectures increases. While several…