Improved massively parallel computation algorithms for MIS, matching, and vertex cover
- Submitting institution
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University of Bristol
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 196732394
- Type
- E - Conference contribution
- DOI
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10.1145/3212734.3212743
- Title of conference / published proceedings
- PODC 2018 - Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing
- First page
- 129
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- July
- Year of publication
- 2018
- URL
-
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
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D - Fundamentals of Computing
- Citation count
- 17
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes improved algorithms for symmetry breaking problems in massively parallel computation models. These models abstract well-established frameworks such as MapReduce and Hadoop, with very low runtime algorithms. The key technical contribution is the identification of a new property of a classical Greedy algorithm. This property has since seen applications beyond this field, in particular, streaming algorithms [Konrad, MFCS’18], [Gamlath et al., PODC’19], and [Farhadi et al., SODA’20]. Timeliness is further demonstrated by the fact that our results improve on very recent works published in top conferences [Czumaj et al., STOC’18] and [Assadi et al. SODA’19].
- Author contribution statement
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- Non-English
- No
- English abstract
- -