CUDA-based parallelization of power iteration clustering for large datasets
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Universidade Federal de Minas Gerais
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Artigo de periódico
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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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Otimização matemática, Banco de dados
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Graphics processing units , Clustering algorithms , Kernel , Eigenvalues and eigenfunctions , Clustering methods , Instruction sets , Symmetric matrices, Scalable machine learning algorithms , GPU , power iteration clustering, Large Datasets , Parallelization , Clustering Algorithm , Real Applications , Graphics Processing Unit , Clustering Quality , Good Scalability , Clustering Method , Image Segmentation , Massive Data , Row Vector , Graphical User Interface , Intel Xeon , Aerial Images , GB Memory , Spectral Method , Order Of Complexity , Spectral Clustering , Affinity Matrix , Code Version , Graphics Processing Unit Memory , Shared Memory , Spectral Clustering Method , Parallel Implementation , Dominant Eigenvalue , Hardware Configuration , Set Of Kernels , Projection Matrix
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https://ieeexplore.ieee.org/document/8078163