CUDA-Based Parallelization of Power Iteration Clustering for Large Datasets

By Gustavo Rodrigues Lacerda Silva, R. R. de Medeiros, Brayan Rene Acevedo Jaimes, Carla Caldeira Takahashi, Douglas A. G. Vieira, A. de Pádua Braga. 2017

6 citations recorded by the source corpus.

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 ( $> 10^{7}$ 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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

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Citations in the corpus

Continual Learning on Noisy Data Streams via Self-Purified Replay (2021, background): “1s for buffers of 300, 500, 1K, respectively, which can speed up to 188 by GPU [61].”