Parallel implementation of a density-based stream clustering algorithm over a GPU scheduling system

M. Hassani, A. Tarakji, L. Georgiev, T. Seidl

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

Abstract

Graphics Processing Units (GPUs) are used together with the CPU to accelerate a wide range of general purpose applications or scientific computations. The highly parallel architecture of the GPU consists of hundreds of cores optimized for parallel performance. Applications taking benefit of the GPU architecture have to be implemented according to the GPU parallel concept. An algorithm which follows a sequential work flow, has to be redesigned to achieve good performance on the GPU device. DenStream is a recent stream clustering algorithm that consists of two main parts. The online part summarizes data from the data stream, and builds micro clusters, while the offline part generates the final clustering using density-based clustering. In this work, we present a GPU-based efficient implementation of DenStream called (G-DenStream). G-DenStream is faster than DenStream, especially when the dimensionality of the streaming dataset increases, while keeping the quality of the reflected clustering as it is. The implementations in this work achieve palatalization of both online and offline parts and test the performance and the utilization on the GPU.
Original languageEnglish
Title of host publicationTrends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA,Tainan, Taiwan, May 13-16, 2014. Revised Selected Papers
EditorsW.-C. Peng, H. Wang, J. Bailey, V.S. Tseng, T.B. Ho, Z.-H. Zhou, A.L.P. Chen
Place of PublicationCham
PublisherSpringer
Pages441-453
Number of pages13
ISBN (Electronic)978-3-319-13186-3
ISBN (Print)978-3-319-13185-6
DOIs
Publication statusPublished - 2014
Externally publishedYes
EventWorkshop on Scalable Data Analytics : Theory and Algorithms (SDA) - Tainan, Taiwan
Duration: 13 May 201416 May 2014

Publication series

NameLecture Notes in Artificial Intelligence (LNAI)
PublisherSpringer
Volume8643
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceWorkshop on Scalable Data Analytics : Theory and Algorithms (SDA)
CountryTaiwan
CityTainan
Period13/05/1416/05/14

Fingerprint Dive into the research topics of 'Parallel implementation of a density-based stream clustering algorithm over a GPU scheduling system'. Together they form a unique fingerprint.

Cite this