CUDA-Based Parallelization of Power Iteration Clustering for Large Datasets

• Published in: IEEE access Vol. 5; pp. 27263 - 27271
• Main Authors: Rodrigues Lacerda Silva, Gustavo, Ribeiro De Medeiros, Rafael, Acevedo Jaimes, Brayan Rene, Caldeira Takahashi, Carla, Gomes Vieira, Douglas Alexandre, De PáDua Braga, AntôNio
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Clustering; Clustering algorithms; Clustering methods; Data points; Datasets; Eigenvalues and eigenfunctions; GPU; Graphics processing units; Instruction sets; Kernel; Parallel processing; power iteration clustering; Scalable machine learning algorithms; Symmetric matrices
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2017.2765380

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.