HPAC-ML: A Programming Model for Embedding ML Surrogates in Scientific Applications
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...
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Zusammenfassung: | 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). |
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DOI: | 10.48550/arxiv.2407.18352 |