M3: Scaling Up Machine Learning via Memory Mapping

Summary: Demonstrates memory-mapped, out-of-core ML with M3 for single-machine scaling of logistic regression and k-means on datasets up to ~190GB. M3 delivers speeds faster than a 4-node Spark cluster and comparable to an 8-node cluster, enabling data-bound ML on one machine. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 5282
Venue: SIGMOD
Year: 2016
Pagerank: -
Overall Rank: 13,544 | 7.08%
DOI: 10.1145/2882903.2914830

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Authors

1. Dezhi Fang (Georgia Institute of Technology)
2. Duen Horng Chau (Georgia Institute of Technology)

BibTeX Citation

@inproceedings{fang_sigmod16,
        title = {{M3: Scaling Up Machine Learning via Memory Mapping}},
        author = {Fang, Dezhi and Chau, Duen Horng},
        series = {{SIGMOD} '16},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2882903.2914830},
        url = {https://dl.acm.org/doi/10.1145/2882903.2914830},
        year = {2016}
}

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