Open AccessCUDA-Based Parallelization of Power Iteration Clustering for Large Datasets
Author(s) -
Gustavo Rodrigues Lacerda Silva,
Rafael Ribeiro De Medeiros,
Brayan Rene Acevedo Jaimes,
Carla Caldeira Takahashi,
Douglas Alexandre Gomes Vieira,
Antonio De Padua Braga
Publication year - 2017
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2017.2765380
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
This paper presents a new clustering algorithm, the GPIC, a graphics processing unit (GPU) accelerated algorithm for power iteration clustering (PIC). Our algorithm is based on the original PIC proposal, adapted to take advantage of the GPU architecture, maintaining the algorithm's original properties. The proposed method was compared against the serial implementation, achieving a considerable speedup in tests with synthetic and real data sets. A significant volume of real data application (>107 records) was used, and we identified that GPIC implementation has good scalability to handle data sets with millions of data points. Our implementation efforts are directed towards two aspects: to process large data sets in less time and to maintain the same quality of the clusters results generated by the original PIC version.
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