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- W4285114328 abstract "AbstractOpenMP 5.0 introduced the directive to support compile-time selection from a set of directive variants based on OpenMP context. OpenMP 5.1 extended context information to include user-defined conditions that enable user-guided runtime adaptation. However, defining conditions that capture the complex interactions between applications and hardware platforms to select an optimized variant is challenging for programmers. This paper explores a novel approach to automate runtime adaptation through machine learning. We design a new directive to describe semantics for model-driven adaptation and also develop a prototype implementation. Using the Smith-Waterman algorithm as a use-case, our experiments demonstrate that the proposed adaptive OpenMP extension automatically chooses the code variants that deliver the best performance in heterogeneous platforms that consist of CPU and GPU processing capabilities. Using decision tree models for tuning has an accuracy of up to 93.1% in selecting the optimal variant, with negligible runtime overhead.KeywordsOpenMPMachine LearningRuntime Adaptation" @default.
- W4285114328 created "2022-07-14" @default.
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- W4285114328 date "2022-01-01" @default.
- W4285114328 modified "2023-10-16" @default.
- W4285114328 title "Extending OpenMP for Machine Learning-Driven Adaptation" @default.
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- W4285114328 doi "https://doi.org/10.1007/978-3-030-97759-7_3" @default.
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