research-article

Mr. Scan: extreme scale density-based clustering using a tree-based network of GPGPU nodes

Authors:

Benjamin Welton,

Barton P. MillerAuthors Info & Claims

SC '13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

Article No.: 84, Pages 1 - 11

https://doi.org/10.1145/2503210.2503262

Published: 17 November 2013 Publication History

Abstract

Density-based clustering algorithms are a widely-used class of data mining techniques that can find irregularly shaped clusters and cluster data without prior knowledge of the number of clusters it contains. DBSCAN is the most well-known density-based clustering algorithm. We introduce our version of DBSCAN, called Mr. Scan, which uses a hybrid parallel implementation that combines the MRNet tree-based distribution network with GPGPU-equipped nodes. Mr. Scan avoids the problems of existing implementations by effectively partitioning the point space and by optimizing DBSCAN's computation over dense data regions. We tested Mr. Scan on both a geolocated Twitter dataset and image data obtained from the Sloan Digital Sky Survey. At its largest scale, Mr. Scan clustered 6.5 billion points from the Twitter dataset on 8,192 GPU nodes on Cray Titan in 17.3 minutes. All other parallel DBSCAN implementations have only demonstrated the ability to cluster up to 100 million points.

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Cited By

Xing ZZhao W(2024)Block-Diagonal Guided DBSCAN ClusteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.340107536:11(5709-5722)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3401075
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
Wang JPal AYang QKant KZhu KGuo S(2023)Collaborative Machine Learning: Schemes, Robustness, and PrivacyIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.316934734:12(9625-9642)Online publication date: Dec-2023
https://doi.org/10.1109/TNNLS.2022.3169347
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cover image ACM Conferences

SC '13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

November 2013

1123 pages

ISBN:9781450323789

DOI:10.1145/2503210

General Chair:
William Gropp
University of Illinois at Urbana-Champaign, Urbana, Illinois
,
Program Chair:
Satoshi Matsuoka
Tokyo Institute of Technology, Tokyo, Japan

Copyright © 2013 ACM.

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Published: 17 November 2013

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SC13: International Conference for High Performance Computing, Networking, Storage and Analysis

November 17 - 21, 2013

Colorado, Denver

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SC '13 Paper Acceptance Rate 91 of 449 submissions, 20%;

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

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Cited By

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https://doi.org/10.1109/TKDE.2024.3401075
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
Wang JPal AYang QKant KZhu KGuo S(2023)Collaborative Machine Learning: Schemes, Robustness, and PrivacyIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.316934734:12(9625-9642)Online publication date: Dec-2023
https://doi.org/10.1109/TNNLS.2022.3169347
Pal AWang JWu YKant KLiu ZSato K(2023)Social Media Driven Big Data Analysis for Disaster Situation Awareness: A TutorialIEEE Transactions on Big Data10.1109/TBDATA.2022.31584319:1(1-21)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TBDATA.2022.3158431
Nagarajan VKulkarni M(2023)RT-DBSCAN: Accelerating DBSCAN using Ray Tracing Hardware2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS54959.2023.00100(963-973)Online publication date: May-2023
https://doi.org/10.1109/IPDPS54959.2023.00100
Mai SJacobsen JAmer-Yahia SSpence ITran NAssent INguyen Q(2022)Incremental Density-Based Clustering on Multicore ProcessorsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2020.302312544:3(1338-1356)Online publication date: 1-Mar-2022
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Wu GCao LTian HWang W(2022)HY-DBSCAN: A hybrid parallel DBSCAN clustering algorithm scalable on distributed-memory computersJournal of Parallel and Distributed Computing10.1016/j.jpdc.2022.06.005Online publication date: Jun-2022
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Poudel MGowanlock M(2021)CUDA-DClust+: Revisiting Early GPU-Accelerated DBSCAN Clustering Designs2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC)10.1109/HiPC53243.2021.00049(354-363)Online publication date: Dec-2021
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Barua HMondal K(2019)A Comprehensive Survey on Cloud Data Mining (CDM) Frameworks and AlgorithmsACM Computing Surveys10.1145/334926552:5(1-62)Online publication date: 13-Sep-2019
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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
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