P-Means, a parallel clustering algorithm for a heterogeneous multi-processor environment
Visualitza/Obre
P-Means, a parallel clustering algorithm for a heterogeneous multi-processor environment (2,800Mb) (Accés restringit)
Sol·licita una còpia a l'autor
Què és aquest botó?
Aquest botó permet demanar una còpia d'un document restringit a l'autor. Es mostra quan:
- Disposem del correu electrònic de l'autor
- El document té una mida inferior a 20 Mb
- Es tracta d'un document d'accés restringit per decisió de l'autor o d'un document d'accés restringit per política de l'editorial
10.1109/HPCSim.2011.5999830
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/24822
Tipus de documentText en actes de congrés
Data publicació2011
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés restringit per política de l'editorial
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
:
Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
G-means is a data mining clustering algorithm based on k-means, used to find the number of Gaussian distributions and their centers inside a multi-dimensional dataset. This paper presents the performance gain obtained from the development of a parallel G-means algorithm for a heterogeneous multi-processor environment using the StarSs framework, called here P means. The P-means execution was divided into 6 well defined steps, where each step was analyzed to create a hierarchical task structure in order to parallelize the execution enabling it to explore the hierarchy and heterogeneity of the Cell BE blades and others heterogeneous architectures. The algorithm implementation was also adapted to perform sequential timing measures to evaluate the Amdahl's law, to compare the theoretical calculation and the execution times' measurements and to introduce parallel computation by using the StarSs framework. The algorithm was executed using a 30 clusters dataset containing 600 thousand points of 60 dimensions in different hardware configurations in order to compare its execution time and speedup, and it showed a overall speedup of more than 18 times. A successful experimentation with real data demonstrated the usefulness of the algorithm.
CitacióFoina, A. [et al.]. P-Means, a parallel clustering algorithm for a heterogeneous multi-processor environment. A: International Conference on High Performance Computing & Simulation. "Proceedings of the 2011 International Conference on High Performance Computing & Simulation (HCPS 2011): July 4-July 8, 2011: Istanbul, Turkey". Istanbul: Institute of Electrical and Electronics Engineers (IEEE), 2011, p. 239-248.
ISBN978-1-61284-380-3
Versió de l'editorhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5999830&tag=1
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
P-Means, a para ... -processor environment.pdf | P-Means, a parallel clustering algorithm for a heterogeneous multi-processor environment | 2,800Mb | Accés restringit |