research-article

Pardicle: parallel approximate density-based clustering

Authors:

Md. Mostofa Ali Patwary,

Nadathur Satish,

Narayanan Sundaram,

Pradeep DubeyAuthors Info & Claims

SC '14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Pages 560 - 571

https://doi.org/10.1109/SC.2014.51

Published: 16 November 2014 Publication History

Abstract

Dbscan is a widely used isodensity-based clustering algorithm for particle data well-known for its ability to isolate arbitrarily-shaped clusters and to filter noise data. The algorithm is super-linear (O(nlogn)) and computationally expensive for large datasets. Given the need for speed, we propose a fast heuristic algorithm for Dbscan using density based sampling, which performs equally well in quality compared to exact algorithms, but is more than an order of magnitude faster. Our experiments on astrophysics and synthetic massive datasets (8.5 billion numbers) shows that our approximate algorithm is up to 56x faster than exact algorithms with almost identical quality (Omega-Index ≥ 0.99). We develop a new parallel Dbscan algorithm, which uses dynamic partitioning to improve load balancing and locality. We demonstrate near-linear speedup on shared memory (15x using 16 cores, single node Intel^® Xeon^® processor) and distributed memory (3917x using 4096 cores, multinode) computers, with 2x additional performance improvement using Intel^® Xeon Phi^™ coprocessors. Additionally, existing exact algorithms can achieve up to 3.4 times speedup using dynamic partitioning.

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Cited By

Challa JGoyal NSharma ASreekumar NBalasubramaniam SGoyal P(2024)A Survey and Experimental Review on Data Distribution Strategies for Parallel Spatial Clustering AlgorithmsJournal of Computer Science and Technology10.1007/s11390-024-2700-039:3(610-636)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s11390-024-2700-0
Prokopenko ALebrun-Grandie DArndt D(2023)Fast tree-based algorithms for DBSCAN for low-dimensional data on GPUsProceedings of the 52nd International Conference on Parallel Processing10.1145/3605573.3605594(503-512)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3605573.3605594
Gowanlock MEigenmann RDing CMcKee S(2019)Hybrid CPU/GPU clustering in shared memory on the billion point scaleProceedings of the ACM International Conference on Supercomputing10.1145/3330345.3330349(35-45)Online publication date: 26-Jun-2019
https://dl.acm.org/doi/10.1145/3330345.3330349
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Pardicle: parallel approximate density-based clustering

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cover image ACM Conferences

SC '14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

November 2014

1054 pages

ISBN:9781479955008

General Chair:
Trish Damkroger
Lawrence Livermore National Laboratory, Livermore, California
,
Program Chair:
Jack Dongarra
University of Tennessee, Knoxville, Tennessee

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IEEE Press

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Published: 16 November 2014

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SC '14: International Conference for High Performance Computing, Networking, Storage and Analysis

November 16 - 21, 2014

Louisana, New Orleans

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SC '14 Paper Acceptance Rate 83 of 394 submissions, 21%;

Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

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Cited By

Challa JGoyal NSharma ASreekumar NBalasubramaniam SGoyal P(2024)A Survey and Experimental Review on Data Distribution Strategies for Parallel Spatial Clustering AlgorithmsJournal of Computer Science and Technology10.1007/s11390-024-2700-039:3(610-636)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s11390-024-2700-0
Prokopenko ALebrun-Grandie DArndt D(2023)Fast tree-based algorithms for DBSCAN for low-dimensional data on GPUsProceedings of the 52nd International Conference on Parallel Processing10.1145/3605573.3605594(503-512)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3605573.3605594
Gowanlock MEigenmann RDing CMcKee S(2019)Hybrid CPU/GPU clustering in shared memory on the billion point scaleProceedings of the ACM International Conference on Supercomputing10.1145/3330345.3330349(35-45)Online publication date: 26-Jun-2019
https://dl.acm.org/doi/10.1145/3330345.3330349
Zhang XMa ZDeng GSun QZhang JYan J(2018)A Label Propagation Algorithm Based on Local Density of Data PointsProceedings of the 2nd International Conference on Algorithms, Computing and Systems10.1145/3242840.3242872(61-64)Online publication date: 27-Jul-2018
https://dl.acm.org/doi/10.1145/3242840.3242872
Song HLee JDas GJermaine CBernstein P(2018)RP-DBSCANProceedings of the 2018 International Conference on Management of Data10.1145/3183713.3196887(1173-1187)Online publication date: 27-May-2018
https://dl.acm.org/doi/10.1145/3183713.3196887
Kumari SGoyal PSood AKumar DBalasubramaniam SGoyal N(2017)Exact, Fast and Scalable Parallel DBSCAN for Commodity PlatformsProceedings of the 18th International Conference on Distributed Computing and Networking10.1145/3007748.3007773(1-10)Online publication date: 5-Jan-2017
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Lulli ADell'Amico MMichiardi PRicci L(2016)NG-DBSCANProceedings of the VLDB Endowment10.14778/3021924.302193210:3(157-168)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.14778/3021924.3021932
Patwary MByna SSatish NSundaram NLukić ZRoytershteyn VAnderson MYao YPrabhat Dubey PKern JVetter J(2015)BD-CATSProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/2807591.2807616(1-12)Online publication date: 15-Nov-2015
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