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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/49305

Title: 利用GPU及m最近群中心加速廣義模糊k均值分群演算法
Speeding up Generalized Fuzzy k-Means Clustering Using m Nearest Cluster Centers Algorithm on GPU
Authors: Dinh-Trung Vu
武亭忠
Contributors: NTOU:Department of Computer Science and Engineering
國立臺灣海洋大學:資訊工程學系
Keywords: 圖型處理器;資料分群;廣義模糊k均值分群法
GFKM;GPU;CUDA;data clustering
Date: 2015
Issue Date: 2018-08-22T06:56:28Z
Abstract: 隨著圖型處理器(GPU)的問世,例如NVIDIA GeForce系列,繪圖硬體逐漸變得更強大且可編程。由於GPU內大量的平行架構,GPU做浮點數運算的能力超越了通用的CPU。而最新的GPU實際上也擁有足夠的編程自由去做除了圖形處理以外之其他計算。此篇論文會利用GPU的優點進而提高資料分群演算法的效能。本篇論文我們將在GPU上實作一個資料分群的演算法-使用m個最近群中心的廣義模糊k均值分群法(GFKM)。我們將會比較GFKM在CPU和GPU運算後的加速程度,同時也會分析使用GPU運算此分群法的利與弊。從我們的實驗結果可以得知我們提出的方法比CPU的最佳版本速度加快約三到十八倍。
The graphics hardware is becoming increasingly more powerful and programmable with the introduction of Graphics Processing Units (GPU) like the NVidia GeForce series. The GPU’s exceed the ordinary general purpose CPU’s ability to do floating point operations due to the massively parallel architecture in the GPU’s. With the newest GPU’s one actually have enough programmable freedom to do other computations than computer graphics processing. This project will take advantage of this in order to get high performance implementations of data clustering algorithms. In this project we will implement a data clustering algorithm, which is Generalized Fuzzy k-Means Clustering Using m nearest Cluster Centers (GFKM) [1], on a GPU. We also make comparisons with CPU based implementations and analysis the pros and cons of using GPU’s. Our experimental results show that our GPU-based GFKM algorithms are about three to eighteen times faster than the optimized CPU code-based GFKM algorithms.
URI: http://ethesys.lib.ntou.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=G0010257053.id
http://ntour.ntou.edu.tw:8080/ir/handle/987654321/49305
Appears in Collections:[資訊工程學系] 博碩士論文

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