HPAC-ML: A Programming Model for Embedding ML Surrogates in Scientific Applications
Abstract
Recent advancements in Machine Learning (ML) have substantially improved its predictive and computational abilities, offering promising opportunities for surrogate modeling in scientific applications. By accurately approximating complex functions with low computational cost, ML-based surrogates can accelerate scientific applications by replacing computationally intensive components with faster model inference. However, integrating ML models into these applications remains a significant challenge, hindering the widespread adoption of ML surrogates as an approximation technique in modern scientific computing. We propose an easy-to-use directive-based programming model that enables developers to seamlessly describe the use of ML models in scientific applications. The runtime support, as instructed by the programming model, performs data assimilation using the original algorithm and can replace the algorithm with model inference. Our evaluation across five benchmarks, testing over 5000 ML models, shows up to 83.6x speed improvements with minimal accuracy loss (as low as 0.01 RMSE).
Cite
@article{arxiv.2407.18352,
title = {HPAC-ML: A Programming Model for Embedding ML Surrogates in Scientific Applications},
author = {Zane Fink and Konstantinos Parasyris and Praneet Rathi and Giorgis Georgakoudis and Harshitha Menon and Peer-Timo Bremer},
journal= {arXiv preprint arXiv:2407.18352},
year = {2024}
}
Comments
16 pages, 9 figures. Accepted at SC24