Abstract
We propose an auto-scheduling mechanism to execute counting queries in machine learning applications. Our approach improves the runtime efficiency of query streams by selecting, in the on-line manner, the optimal execution strategy for each query. We also discuss how to scale up counting queries in multi-threaded applications.
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Acknowledgments
This research was supported by the National Science Centre (Poland) under grant no. UMO-2017/26/D/ST6/00687.
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Bratek, P., Szustak, L., Zola, J. (2022). Parallelization and Auto-scheduling of Data Access Queries in ML Workloads. In: Chaves, R., et al. Euro-Par 2021: Parallel Processing Workshops. Euro-Par 2021. Lecture Notes in Computer Science, vol 13098. Springer, Cham. https://doi.org/10.1007/978-3-031-06156-1_43
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DOI: https://doi.org/10.1007/978-3-031-06156-1_43
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