检索结果-内蒙古大学图书馆

Memory bandwidth optimization of SpMV on GPGPUs

Frontiers of Computer Science 2015年第3期9卷 431-441页

作者： Chenggang Clarence YAN Hui YU Weizhi XU Yingping ZHANG Bochuan CHEN Zhu TIAN Yuxuan WANG Jian YIN Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Institute of Microelectronics Tsinghua University Beijing 100084 China Automation Department Tsinghua University Beijing 100084 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China State Grid Information & Communication Company of Hunan EPC Changsha 410007 China Department of Computer Shandong University Weihai 250101 China

It is an important task to improve performance for sparse matrix vector multiplication （SpMV）, and it is a difficult task because of its irregular memory access. Gen- eral purpose GPU （GPGPU） provides high computing abil- ity and substantial bandwidth that cannot be fully exploited by SpMV due to its irregularity. In this paper, we propose two novel methods to optimize the memory bandwidth for SpMV on GPGPU. First, a new storage format is proposed to exploit memory bandwidth of GPU architecture more effi- ciently. The new storage format can ensure that there are as many non-zeros as possible in the format which is suitable to exploit the memory bandwidth of the GPU. Second, we pro- pose a cache blocking method to improve the performance of SpMV on GPU architecture. The sparse matrix is partitioned into sub-blocks that are stored in CSR format. With the block- ing method, the corresponding part of vector x can be reused in the GPU cache, so the time to access the global memory for vector x is reduced heavily. Experiments are carried out on three GPU platforms, GeForce 9800 GX2, GeForce GTX 480, and Tesla K40. Experimental results show that both new methods can efficiently improve the utilization of GPU mem- ory bandwidth and the performance of the GPU.

关键词： GPGPU performance tuning SpMV cacheblocking memory bandwidth

来源：评论

学校读者我要写书评

暂无评论

A new logistic map based chaotic biogeography-based optimization approach for cluster analysis

A new logistic map based chaotic biogeography-based optimiza...

引用

IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Changsha Hunan China School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China Department of Electrical Engineering CUNY New York NY USA Department of Mechanical and Control Engineering Kyushu Institute of Technology Fukuoka Prefecture Japan School of Information Engineering Yangzhou University Yangzhou Jiangsu China School of Physics and Electronic Engineering Taizhou University Taizhou Zhejiang China School of Electronic Information Shanghai Dianji University Shanghai China School of Energy and Mechanical Engineering Nanjing Normal University Nanjing Jiangsu China Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University Quanzhou Fujian China

ISBN: (纸本)9781509034857

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization (CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： Genetic algorithms Optimization Clustering algorithms Particle swarm optimization Algorithm design and analysis Logistics Mathematical model

来源：评论

学校读者我要写书评

暂无评论

Whole-proteome based phylogenetic tree construction with inter-amino-acid distances and the conditional geometric distribution profiles

引用

Molecular phylogenetics and evolution 2015年 89卷 37-45页

作者： Xian-Hua Xie Zu-Guo Yu Guo-Sheng Han Wei-Feng Yang Vo Anh Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 PR China School of Mathematics and Computer Science Gannan Normal University Jiangxi 341000 PR China. Electronic address: xxianhua@***. School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane QLD 4001 Australia. Electronic address: yuzuguo@***. Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 PR China. Electronic address: korea10282003@***. Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 PR China. Electronic address: yangweifeng09@***. School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane QLD 4001 Australia. Electronic address: v.anh@qut.edu.au.

There has been a growing interest in alignment-free methods for whole genome comparison and phylogenomic studies. In this study, we propose an alignment-free method for phylogenetic tree construction using whole-proteome sequences. Based on the inter-amino-acid distances, we first convert the whole-proteome sequences into inter-amino-acid distance vectors, which are called observed inter-amino-acid distance profiles. Then, we propose to use conditional geometric distribution profiles (the distributions of sequences where the amino acids are placed randomly and independently) as the reference distribution profiles. Last the relative deviation between the observed and reference distribution profiles is used to define a simple metric that reflects the phylogenetic relationships between whole-proteome sequences of different organisms. We name our method inter-amino-acid distances and conditional geometric distribution profiles (IAGDP). We evaluate our method on two data sets: the benchmark dataset including 29 genomes used in previous published papers, and another one including 67 mammal genomes. Our results demonstrate that the new method is useful and efficient.

关键词： Alignment-free whole-proteome comparison Conditional geometric distribution profile Inter-amino-acid distances Phylogenetic tree

来源：评论

学校读者我要写书评

暂无评论

The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

引用

BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

来源：评论

学校读者我要写书评

暂无评论

Determination of multifractal dimensions of complex networks by means of the sandbox algorithm

引用

Chaos 2015年第2期25卷 023103页

作者： Liu, Jin-Long Yu, Zu-Guo Anh, Vo Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia

Complex networks have attracted much attention in diverse areas of science and technology. Multifractal analysis (MFA) is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. In this paper, we employ the sandbox (SB) algorithm proposed by Tél et al. (Physica A 159, 155-166 (1989)), for MFA of complex networks. First, we compare the SB algorithm with two existing algorithms of MFA for complex networks: the compact-box-burning algorithm proposed by Furuya and Yakubo (Phys. Rev. E 84, 036118 (2011)), and the improved box-counting algorithm proposed by Li et al. (J. Stat. Mech.: Theor. Exp. 2014, P02020 (2014)) by calculating the mass exponents ?(q) of some deterministic model networks. We make a detailed comparison between the numerical and theoretical results of these model networks. The comparison results show that the SB algorithm is the most effective and feasible algorithm to calculate the mass exponents ?(q) and to explore the multifractal behavior of complex networks. Then, we apply the SB algorithm to study the multifractal property of some classic model networks, such as scale-free networks, small-world networks, and random networks. Our results show that multifractality exists in scale-free networks, that of small-world networks is not obvious, and it almost does not exist in random networks. © 2015 AIP Publishing LLC.

关键词：

来源：评论

学校读者我要写书评

暂无评论

引用

Current Bioinformatics 2014年第3期9卷 218-227页

作者： Chen, Xiao-Su Yu, Zu-Guo Zheng, Juan Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan411105 China School of Mathematical Sciences Queensland University of Technology GPO Box 2434 BrisbaneQ 4001 Australia School of Information and Engineering Xiangtan University Hunan411105 China

Whole genome sequences are generally accepted as excellent tools for studying evolutionary relationships. Due to the problems caused by the uncertainty in alignment, existing tools for phylogenetic analysis based on multiple alignments could not be directly applied to the whole-genome comparison and phylogenomic studies. There has been a growing interest in alignment-free methods for phylogenetic analysis using complete genome data. The "distances" used in these alignment-free methods are not proper distance metrics in the strict mathematical sense. In this study, we first review them in a more general frame — dissimilarity. Then we propose some new dissimilarities for phylogenetic analysis. Last three genome datasets are employed to evaluate these dissimilarities from a biological point of view. © 2014 Bentham Science Publishers.

关键词： Alignment

来源：评论

学校读者我要写书评

暂无评论

Secondary structure element alignment kernel method for prediction of protein structural classes

引用

Current Bioinformatics 2014年第3期9卷 253-257页

作者： Han, Guo-Sheng Yu, Zu-Guo Anh, Vo Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 China School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q 4001 Australia

In this paper, we aim at predicting protein structural classes for low-homology data sets based on predicted secondary structures. We propose a new and simple kernel method, named as SSEAKSVM, to predict protein structural classes. The secondary structures of all protein sequences are obtained by using the tool PSIPRED and then a linear kernel on the basis of secondary structure element alignment scores is constructed for training a support vector machine classifier without parameter adjusting. Our method SSEAKSVM was evaluated on two low-homology datasets 25PDB and 1189 with sequence homology being 25% and 40%, respectively. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies on these two data sets are 86.3% and 84.5%, respectively, which are higher than those obtained by other existing methods. Especially, our method achieves higher accuracies (88.1% and 88.5%) for differentiating the α+ β class and the α/β class compared to other methods. This suggests that our method is valuable to predict protein structural classes particularly for low-homology protein sequences. The source code of the method in this paper can be downloaded at http://***/myphp/math/research/source/SSEAK_source_***. © 2014 Bentham Science Publishers.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

A cooperative immune coevolutionary algorithm for multi-objective optimization

引用

Tien Tzu Hsueh Pao/Acta Electronica Sinica 2014年第5期42卷 858-867页

作者： Qi, Yu-Tao Liu, Fang Ren, Yuan Liu, Jing-Le Jiao, Li-Cheng School of Computer Science and Technology Xidian University Xi'an Shaanxi 710071 China Institute of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an Shaanxi 710071 China

According to the distribution characteristics of the Pareto set (PS) of multi-objective optimization problems (MOPs), a cooperative coevolutionary model with new problem decomposition method was designed. By introducing the proposed coevolutionary model into artificial immune system, a cooperative immune coevolutionary algorithm for multi-objective optimization (CICAMO) was proposed. In CICAMO, the Tchebycheff decomposition method is employed to divide sub-populations at first, and then linear probabilistic models are built for each sub-population to piecewise approximate the distribution of the whole PS. In antibody reproducing step, two types of approaches based on clonal selection and model sampling are employed. Experimental results indicate that CICAMO can achieve a good performance in terms of both solution quality and convergence rate, especially when solving MOPs with non-linear relationship between decision variables.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Multifractal analysis of weighted networks by a modified sandbox algorithm

引用

Scientific reports 2015年第1期5卷 17628页

作者： Yu-Qin Song Jin-Long Liu Zu-Guo Yu Bao-Gen Li Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China. College of Science Hunan University of technology Zhuzhou Hunan 412007 China. School of Mathematical Sciences Queensland University of Technology Brisbane Q4001 Australia.

Complex networks have attracted growing attention in many fields. As a generalization of fractal analysis, multifractal analysis (MFA) is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. Some algorithms for MFA of unweighted complex networks have been proposed in the past a few years, including the sandbox (SB) algorithm recently employed by our group. In this paper, a modified SB algorithm (we call it SBw algorithm) is proposed for MFA of weighted networks. First, we use the SBw algorithm to study the multifractal property of two families of weighted fractal networks (WFNs): "Sierpinski" WFNs and "Cantor dust" WFNs. We also discuss how the fractal dimension and generalized fractal dimensions change with the edge-weights of the WFN. From the comparison between the theoretical and numerical fractal dimensions of these networks, we can find that the proposed SBw algorithm is efficient and feasible for MFA of weighted networks. Then, we apply the SBw algorithm to study multifractal properties of some real weighted networks - collaboration networks. It is found that the multifractality exists in these weighted networks, and is affected by their edge-weights.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Laplacian normalization and random walk on heterogeneous networks for disease-gene prioritization

引用

Computational biology and chemistry 2015年 57卷 21-8页

作者： Zhi-Qin Zhao Guo-Sheng Han Zu-Guo Yu Jinyan Li Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China. Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia. Electronic address: yuzg1970@***. Advanced Analytics Institute & Centre for Health Technologies University of Technology Sydney Broadway NSW 2007 Australia. Electronic address: jinyan.li@uts.edu.au.

Random walk on heterogeneous networks is a recently emerging approach to effective disease gene prioritization. Laplacian normalization is a technique capable of normalizing the weight of edges in a network. We use this technique to normalize the gene matrix and the phenotype matrix before the construction of the heterogeneous network, and also use this idea to define the transition matrices of the heterogeneous network. Our method has remarkably better performance than the existing methods for recovering known gene-phenotype relationships. The Shannon information entropy of the distribution of the transition probabilities in our networks is found to be smaller than the networks constructed by the existing methods, implying that a higher number of top-ranked genes can be verified as disease genes. In fact, the most probable gene-phenotype relationships ranked within top 3 or top 5 in our gene lists can be confirmed by the OMIM database for many cases. Our algorithms have shown remarkably superior performance over the state-of-the-art algorithms for recovering gene-phenotype relationships. All Matlab codes can be available upon email request.

关键词： Disease genes and phenotypes Heterogeneous network Laplacian normalization Leave-one-out cross-validation Random walk with restart Shannon information entropy

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：