Characterizing Distributed Machine Learning Workloads on Apache Spark

Author:

Djebrouni Yasmine1ORCID,Rocha Isabelly2ORCID,Bouchenak Sara3ORCID,Chen Lydia4ORCID,Felber Pascal2ORCID,Marangozova Vania1ORCID,Schiavoni Valerio2ORCID

Affiliation:

1. University of Grenoble Alps, France

2. University of Neuchâtel, Switzerland

3. INSA Lyon, France

4. University of Neuchâtel, Switzerland, TU Delft, Netherlands

Publisher

ACM

Reference80 articles.

1. MLPerf. https://mlperf.org/. Last accessed : Oct 24, 2023 . MLPerf. https://mlperf.org/. Last accessed: Oct 24, 2023.

2. News 20 dataset. http://qwone.com/~jason/20Newsgroups. Last accessed : Oct 24, 2023 . News 20 dataset. http://qwone.com/~jason/20Newsgroups. Last accessed: Oct 24, 2023.

3. Sparkmeasure a tool for performance troubleshooting of apache spark workloads. https://db-blog.web.cern.ch/blog/luca-canali/2018-08-sparkmeasure-tool-performance-troubleshooting-apache-spark-workloads. Last accessed: Oct 24 2023. Sparkmeasure a tool for performance troubleshooting of apache spark workloads. https://db-blog.web.cern.ch/blog/luca-canali/2018-08-sparkmeasure-tool-performance-troubleshooting-apache-spark-workloads. Last accessed: Oct 24 2023.

4. UCI Machine Learning Repository . http://archive.ics.uci.edu/ml. Last accessed : Oct 24, 2023 . UCI Machine Learning Repository. http://archive.ics.uci.edu/ml. Last accessed: Oct 24, 2023.

5. Kaggle. https://www.kaggle.com/datasets , 2021 . Kaggle. https://www.kaggle.com/datasets, 2021.

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