@inproceedings{72cfa5ab76034f1da984dc8f1df2749e,
title = "Parallelization and Auto-scheduling of Data Access Queries in ML Workloads",
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.",
keywords = "Auto-scheduling, Data access queries, Machine learning, SABNAtk",
author = "Pawel Bratek and Lukasz Szustak and Jaroslaw Zola",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 27th International Conference on Parallel and Distributed Computing, Euro-Par 2021 ; Conference date: 30-08-2021 Through 31-08-2021",
year = "2022",
doi = "10.1007/978-3-031-06156-1\_43",
language = "English",
isbn = "9783031061554",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "525--529",
editor = "Ricardo Chaves and \{B. Heras\}, Dora and Aleksandar Ilic and Didem Unat and Badia, \{Rosa M.\} and Andrea Bracciali and Patrick Diehl and Anshu Dubey and Oh Sangyoon and \{L. Scott\}, Stephen and Laura Ricci",
booktitle = "Euro-Par 2021",
address = "Germany",
}