Proceedings Article10.1109/APCSAC.2008.4625449
Parallelization of spectral clustering algorithm on multi-core processors and GPGPU
Jing Zheng,Wenguang Chen,Yurong Chen,Yimin Zhang,Ying Zhao,Weimin Zheng +5 more
- 16 Sep 2008
- pp 1-8
12
TL;DR: Two versions of implementation ofSpectral clustering are provided: one is parallelized in OpenMP; the other is programmed in the NVIDIA CUDA (compute unified device architecture), which is the environment provided by NVIDIA to program on its CUDA-Enabled GPGPUs (general-purpose graphic processing unit).
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Abstract: Spectral clustering is a widely-used algorithm in the field of information retrieval, data mining, machine learning and many others It can help to cluster a large number of data into several categories without requiring any additional information about the dataset or the categories, so that people can find information by categories easily In this paper, we parallelize the algorithm proposed by Andrew Y Ng, Michael I Jordan and Yair Weiss We provide two versions of implementation: one is parallelized in OpenMP; the other is programmed in the NVIDIA CUDA (compute unified device architecture), which is the environment provided by NVIDIA to program on its CUDA-Enabled GPGPUs (general-purpose graphic processing unit) We can achieve about three times speedup in OpenMP and around ten times speedup using CUDA in our experiments
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References
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TL;DR: In this article, the authors present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches, and discuss the advantages and disadvantages of these algorithms.
•Proceedings Article
On Spectral Clustering: Analysis and an algorithm
Andrew Y. Ng,Michael I. Jordan,Yair Weiss +2 more
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TL;DR: A simple spectral clustering algorithm that can be implemented using a few lines of Matlab is presented, and tools from matrix perturbation theory are used to analyze the algorithm, and give conditions under which it can be expected to do well.
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Document clustering based on non-negative matrix factorization
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TL;DR: This paper proposes a novel document clustering method based on the non-negative factorization of the term-document matrix of the given document corpus that surpasses the latent semantic indexing and the spectral clustering methods not only in the easy and reliable derivation of document clustered results, but also in document clusters accuracies.
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Optimization principles and application performance evaluation of a multithreaded GPU using CUDA
Shane Ryoo,Christopher I. Rodrigues,Sara S. Baghsorkhi,Sam S. Stone,David B. Kirk,Wen-mei W. Hwu +5 more
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TL;DR: This work discusses the GeForce 8800 GTX processor's organization, features, and generalized optimization strategies, and achieves increased performance by reordering accesses to off-chip memory to combine requests to the same or contiguous memory locations and apply classical optimizations to reduce the number of executed operations.