Cluster and GPU accelerated Mixture Elucidator

碩士 === 國立臺灣大學 === 資訊網路與多媒體研究所 === 101 === Mass spectrometry is the most versatile and widely used analytical method today. In a study called Scaffold Hunter (2011), Yeu-Chern Harn et al. proposed a method to output candidates for each peak of the mass spectrum by exploiting the NPSDB, a database of...

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Main Authors: Alioune Schurz, 舒艾琉
Other Authors: YF Tseng
Format: Others
Language:en_US
Published: 2013
Online Access:http://ndltd.ncl.edu.tw/handle/87365086688656175984
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spelling ndltd-TW-101NTU056410112016-03-16T04:15:06Z http://ndltd.ncl.edu.tw/handle/87365086688656175984 Cluster and GPU accelerated Mixture Elucidator GPU在結構計算上的應用 Alioune Schurz 舒艾琉 碩士 國立臺灣大學 資訊網路與多媒體研究所 101 Mass spectrometry is the most versatile and widely used analytical method today. In a study called Scaffold Hunter (2011), Yeu-Chern Harn et al. proposed a method to output candidates for each peak of the mass spectrum by exploiting the NPSDB, a database of 82,242 scaffolds, with side chain likeliness data. The core combinatorial problem of this study was proved to be NP-complete by Bohan-Su et al. (2012) who introduced a dynamic programming and an iterative dynamic programming algorithm able to solve the problem in average pseudo polynomial time and average polynomial time respectively. Although the methods proposed by both Yeu-Chern Harn and Su-Bohan seem very promising, they lack three major elements: First, a stricter mathematical framework to bridge Yeu-Chern Harn and Su-Bohan’s works. Second, a framework for testing, evaluating and tuning the prediction method and third, efficient and predictable running times. Therefore, in this study we first develop a mathematical framework to represent all important concepts. We then introduce FFT, a Framework for Fast Tuning of the prediction method; and evaluate the total running time for processing all 82,242 scaffolds of Yeu-Chern Harn’s Natural Product Scaffold database, using MapReduce on a home made 5 nodes Hadoop cluster. Finally we study the different improvements that can be made to Su-Bohan’s dynamic programming algorithms using General purpose GPU programming, and evaluate the running time of GAME, a CUDA based Gpu Accelerated Mixture Elucidator. YF Tseng 曾宇鳳 2013 學位論文 ; thesis 125 en_US
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sources NDLTD
description 碩士 === 國立臺灣大學 === 資訊網路與多媒體研究所 === 101 === Mass spectrometry is the most versatile and widely used analytical method today. In a study called Scaffold Hunter (2011), Yeu-Chern Harn et al. proposed a method to output candidates for each peak of the mass spectrum by exploiting the NPSDB, a database of 82,242 scaffolds, with side chain likeliness data. The core combinatorial problem of this study was proved to be NP-complete by Bohan-Su et al. (2012) who introduced a dynamic programming and an iterative dynamic programming algorithm able to solve the problem in average pseudo polynomial time and average polynomial time respectively. Although the methods proposed by both Yeu-Chern Harn and Su-Bohan seem very promising, they lack three major elements: First, a stricter mathematical framework to bridge Yeu-Chern Harn and Su-Bohan’s works. Second, a framework for testing, evaluating and tuning the prediction method and third, efficient and predictable running times. Therefore, in this study we first develop a mathematical framework to represent all important concepts. We then introduce FFT, a Framework for Fast Tuning of the prediction method; and evaluate the total running time for processing all 82,242 scaffolds of Yeu-Chern Harn’s Natural Product Scaffold database, using MapReduce on a home made 5 nodes Hadoop cluster. Finally we study the different improvements that can be made to Su-Bohan’s dynamic programming algorithms using General purpose GPU programming, and evaluate the running time of GAME, a CUDA based Gpu Accelerated Mixture Elucidator.
author2 YF Tseng
author_facet YF Tseng
Alioune Schurz
舒艾琉
author Alioune Schurz
舒艾琉
spellingShingle Alioune Schurz
舒艾琉
Cluster and GPU accelerated Mixture Elucidator
author_sort Alioune Schurz
title Cluster and GPU accelerated Mixture Elucidator
title_short Cluster and GPU accelerated Mixture Elucidator
title_full Cluster and GPU accelerated Mixture Elucidator
title_fullStr Cluster and GPU accelerated Mixture Elucidator
title_full_unstemmed Cluster and GPU accelerated Mixture Elucidator
title_sort cluster and gpu accelerated mixture elucidator
publishDate 2013
url http://ndltd.ncl.edu.tw/handle/87365086688656175984
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AT shūàiliú clusterandgpuacceleratedmixtureelucidator
AT aliouneschurz gpuzàijiégòujìsuànshàngdeyīngyòng
AT shūàiliú gpuzàijiégòujìsuànshàngdeyīngyòng
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