检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

5,679 篇 期刊文献
3,143 篇 会议
183 篇 学位论文
12 册 图书
12 篇 科技报告
9 篇 资讯

馆藏范围

9,038 篇 电子文献
2 种 纸本馆藏

日期分布

学科分类号

7,607 篇 工学
- 5,080 篇 计算机科学与技术...
- 1,984 篇 电气工程
- 976 篇 软件工程
- 507 篇 信息与通信工程
- 440 篇 力学（可授工学、理...
- 364 篇 控制科学与工程
- 350 篇 机械工程
- 234 篇 材料科学与工程（可...
- 175 篇 仪器科学与技术
- 172 篇 电子科学与技术（可...
- 170 篇 动力工程及工程热...
- 158 篇 测绘科学与技术
- 148 篇 土木工程
- 142 篇 环境科学与工程（可...
- 139 篇 化学工程与技术
- 139 篇 石油与天然气工程
- 131 篇 生物医学工程（可授...
- 124 篇 水利工程
- 108 篇 航空宇航科学与技...
- 77 篇 光学工程
3,026 篇 理学
- 1,380 篇 数学
- 970 篇 物理学
- 263 篇 地球物理学
- 239 篇 生物学
- 189 篇 化学
- 147 篇 统计学（可授理学、...
- 78 篇 地理学
610 篇 管理学
- 541 篇 管理科学与工程(可...
360 篇 医学
- 216 篇 临床医学
- 98 篇 基础医学(可授医学...
92 篇 经济学
89 篇 农学
43 篇 教育学
24 篇 法学
9 篇 艺术学
6 篇 文学
3 篇 哲学
1 篇 历史学
1 篇 军事学

主题

9,038 篇 parallel computi...
367 篇 gpu
361 篇 cuda
196 篇 mpi
163 篇 big data
151 篇 optimization
136 篇 gpgpu
133 篇 high performance...
128 篇 distributed comp...
117 篇 domain decomposi...
107 篇 load balancing
93 篇 parallel process...
92 篇 graphics process...
91 篇 openmp
88 篇 high-performance...
87 篇 scalability
82 篇 machine learning
81 篇 computational fl...
80 篇 mapreduce
79 篇 simulation

机构

45 篇 univ chinese aca...
31 篇 univ colorado de...
27 篇 chinese acad sci...
22 篇 texas a&m univer...
21 篇 tsinghua univ de...
19 篇 hunan univ sch m...
18 篇 brno university ...
17 篇 chinese acad sci...
17 篇 argonne natl lab...
16 篇 zhejiang univ co...
15 篇 virginia tech | ...
14 篇 los alamos natl ...
14 篇 lawrence livermo...
13 篇 shenzhen key lab...
12 篇 peking univ sch ...
12 篇 hunan univ coll ...
11 篇 chongqing univ c...
11 篇 inst appl phys &...
11 篇 natl univ def te...
11 篇 huazhong univ sc...

作者

38 篇 cai xiao-chuan
34 篇 magoules frederi...
23 篇 huang bormin
22 篇 yang chao
18 篇 cole colin
17 篇 wu qing
16 篇 spiryagin maksym
15 篇 nakano aiichiro
15 篇 liu ying
15 篇 liu jie
14 篇 cannataro mario
14 篇 wang hao
14 篇 chen rongliang
13 篇 yang bo
13 篇 forsell martti
13 篇 mielikainen jarn...
12 篇 wang wei
12 篇 janna carlo
12 篇 nakashima hirosh...
12 篇 vega-rodriguez m...

语言

8,429 篇 英文
508 篇 其他
67 篇 中文
6 篇 俄文
4 篇 日文
3 篇 西班牙文
3 篇 法文
2 篇 土耳其文
1 篇 德文
1 篇 朝鲜文

检索条件"主题词=parallel Computing"

共 9038 条记录，以下是4991-5000 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Using high performance algorithms for the hybrid simulation of disease dynamics on CPU and GPU

Using high performance algorithms for the hybrid simulation ...

引用

15th Annual International Conference on Computational Science (ICCS)

作者： Leonenko, Vasiliy N. Pertsev, Nikolai V. Artzrouni, Marc ITMO Univ St Petersburg Russia RAS Sobolev Inst Math Siberian Branch Novosibirsk Russia Univ Pau & Pays Adour Pau France

In the current work the authors present several approaches to the high performance simulation of human diseases propagation using hybrid two-component imitational models. The models under study were created by coupling compartmental and discrete-event submodels. The former is responsible for the simulation of the demographic processes in a population while the latter deals with a disease progression for a certain individual. The number and type of components used in a model may vary depending on the research aims and data availability. The introduced high performance approaches are based on batch random number generation, distribution of simulation runs and the calculations on graphical processor units. The emphasis was made on the possibility to use the approaches for various model types without considerable code refactoring for every particular model. The speedup gained was measured on simulation programs written in C++ and MATLAB for the models of HIV and tuberculosis spread and the models of tumor screening for the prevention of colorectal cancer. The benefits and drawbacks of the described approaches along with the future directions of their development are discussed.

关键词： mathematical epidemiology individual-based modeling computer simulation Monte Carlo methods parallel computing C plus MATLAB OpenMP MPI GPGPU CUDA

来源：评论

学校读者我要写书评

暂无评论

Is Apache Spark Scalable to Seismic Data Analytics and Computations? 3

Is Apache Spark Scalable to Seismic Data Analytics and Compu...

引用

IEEE International Conference on Big Data

作者： Yan, Yuzhong Huang, Lei Yi, Liqi Prairie View A&M Univ Dept Comp Sci Prairie View TX USA Intel Corp Hillsboro OR USA

ISBN: (纸本)9781479999255

High Performance computing (HPC) has been a dominated technology used in seismic data processing at the petroleum industry. However, with the increasing data size and varieties, traditional HPC focusing on computation meets new challenges. Researchers are looking for new computing platforms with a balance of both performance and productivity, as well as featured with big data analytics capability. Apache Spark is a new big data analytics platform that supports more than map/reduce parallel execution mode with good scalability and fault tolerance. In this paper, we try to answer the question that if Apache Spark is scalable to process seismic data with its in-memory computation and data locality features. We use a few typical seismic data processing algorithms to study the performance and productivity. Our contributions include customized seismic data distributions in Spark, extraction of commonly used templates for seismic data processing algorithms, and performance analysis of several typical seismic processing algorithms.

关键词： parallel computing Big Data Analytics Seismic Data Stencil computing

来源：评论

学校读者我要写书评

暂无评论

Distributed Graph Algorithmics: Theory and Practice 15

Distributed Graph Algorithmics: Theory and Practice

引用

8th ACM International Conference on Web Search and Data Mining (WSDM)

作者： Lattanzi, Silvio Mirrokni, Vahab Google Res New York NY 10001 USA

ISBN: (纸本)9781450333177

As a fundamental tool in modeling and analyzing social, and information networks, large-scale graph mining is an important component of any tool set for big data analysis. Processing graphs with hundreds of billions of edges is only possible via developing distributed algorithms under distributed graph mining frameworks such as MapReduce, Pregel, Gigraph, and alike. For these distributed algorithms to work well in practice, we need to take into account several metrics such as the number of rounds of computation and the communication complexity of each round. For example, given the popularity and ease-of-use of MapReduce framework, developing practical algorithms with good theoretical guarantees for basic graph algorithms is a problem of great importance. In this tutorial, we first discuss how to design and implement algorithms based on traditional MapReduce architecture. In this regard, we discuss various basic graph theoretic problems such as computing connected components, maximum matching, MST, counting triangle and overlapping or balanced clustering. We discuss a computation model for MapReduce and describe the sampling, filtering, local random walk, and core-set techniques to develop efficient algorithms in this framework. At the end, we explore the possibility of employing other distributed graph processing frameworks. In particular, we study the effect of augmenting MapReduce with a distributed hash table (DHT) service and also discuss the use of a new graph processing framework called ASYMP based on asynchronous message-passing method. In particular, we will show that using ASyMP, one can improve the CPU usage, and achieve significantly improved running time.

关键词： parallel computing MapReduce algorithms Large scale data-mining

来源：评论

学校读者我要写书评

暂无评论

An efficient interior-point decomposition algorithm for parallel solution of large-scale nonlinear problems with significant variable coupling

An efficient interior-point decomposition algorithm for para...

引用

作者： Kang, Jia Texas A&M University

学位级别：Ph.D.

In this dissertation we develop multiple algorithms for efficient parallel solution of structured nonlinear programming problems by decomposition of the linear augmented system solved at each iteration of a nonlinear interior-point approach. In particular, we address large-scale, block-structured problems with a significant number of complicating, or coupling variables. This structure arises in many important problem classes including multi-scenario optimization, parameter estimation, two-stage stochastic programming, optimal control and power network problems. The structure of these problems induces a block-angular structure in the augmented system, and parallel solution is possible using a Schur-complement decomposition. Three major variants are implemented: a serial, full-space interior-point method, serial and parallel versions of an explicit Schur-complement decomposition, and serial and parallel versions of an implicit PCG-based Schur-complement decomposition. All of these algorithms have been implemented in C++ in an extensible software framework for nonlinear optimization. The explicit Schur-complement decomposition is typically effective for problems with a few hundred coupling variables. We demonstrate the performance of our implementation on an important problem in optimal power grid operation, the contingency-constrained AC optimal power flow problem. In this dissertation, we present a rectangular IV formulation for the contingency-constrained ACOPF problem and demonstrate that the explicit Schur-complement decomposition can dramatically reduce solution times for a problem with a large number of contingency scenarios. Moreover, a comparison of the explicit Schur-complement decomposition implementation and the Progressive Hedging approach provided by Pyomo is provided, showing that the internal decomposition approach is computationally favorable to the external approach. However, the explicit Schur-complement decomposition approach is not appropriate for

关键词： parallel computing Interior-point Nonlinear programming Schur-complement decomposition Contingency-constrained ACOPF Thesis

来源：评论

学校读者我要写书评

暂无评论

A HADOOP-BASED DISTRIBUTED FRAMEWORK FOR EFFICIENT MANAGING AND PROCESSING BIG REMOTE SENSING IMAGES 1

A HADOOP-BASED DISTRIBUTED FRAMEWORK FOR EFFICIENT MANAGING ...

引用

ISPRS International Workshop on Spatiotemporal computing

作者： Wang, C. Hu, F. Hu, X. Zhao, S. Wen, W. Yang, C. Natl Adm Surveying Mapping & Geoinformat China Hainan Geomat Ctr Haikou 570203 Hainan Peoples R China George Mason Univ Dept Geog & GeoInformat Sci Fairfax VA 22030 USA George Mason Univ Ctr Intelligent Spatial Comp Fairfax VA 22030 USA Natl Adm Surveying Mapping & Geoinformat China Inst Photogrammetry 4 Haikou 570203 Hainan Peoples R China

Various sensors from airborne and satellite platforms are producing large volumes of remote sensing images for mapping, environmental monitoring, disaster management, military intelligence, and others. However, it is challenging to efficiently storage, query and process such big data due to the data- and computing-intensive issues. In this paper, a Hadoop-based framework is proposed to manage and process the big remote sensing data in a distributed and parallel manner. Especially, remote sensing data can be directly fetched from other data platforms into the Hadoop Distributed File System ( HDFS). The Orfeo toolbox, a ready-to-use tool for large image processing, is integrated into MapReduce to provide affluent image processing operations. With the integration of HDFS, Orfeo toolbox and MapReduce, these remote sensing images can be directly processed in parallel in a scalable computing environment. The experiment results show that the proposed framework can efficiently manage and process such big remote sensing data.

关键词： Remote Sensing Image Processing HDFS MapReduce GIS parallel computing

来源：评论

学校读者我要写书评

暂无评论

parallel Execution of SVM Training using Graphics Processing Units (SVMTrGPUs) 5

Parallel Execution of SVM Training using Graphics Processing...

引用

5th IEEE International Conference on Control System, computing and Engineering (ICCSCE)

作者： Salleh, Nur Shakirah Md Baharim, Muhammad Fahim Univ Tenaga Nas Dept Syst & Networking Shah Alam Selangor Malaysia

ISBN: (纸本)9781479982523

parallel computing is a simultaneous use of multiple compute resources, for example, processors to solve complex computational problems. It has been used in high-end computing areas such as pattern recognition, medical diagnosis, national defense, and web search engine. This paper focuses on the implementation of pattern classification technique, Support Vector Machine (SVM) using vector processor approach. We have carried out a performance analysis to benchmark the sequential SVM program against the Graphics Processing Units (GPUs) optimization. The result shows that the parallelization of SVM training duration achieves a better performance than the sequential code speedups by 6.40.

关键词： parallel computing Support Vector Machine CUDA Vector Processor Graphics Processing Units UCW dataset

来源：评论

学校读者我要写书评

暂无评论

A GPU-based fast solution for Riesz space fractional reaction-diffusion equation 18

A GPU-based fast solution for Riesz space fractional reactio...

引用

18th International Conference on Network-Based Information Systems (NBiS)

作者： Wang, Qinglin Liu, Jie Gong, Chunye Zhang, Yang Xing, Zuocheng Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China Sci & Technol Space Phys Lab Beijing 10076 Peoples R China Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781479999422

The fast numerical solutions of Riesz fractional equation have computational cost of O(NMlogM), where M, N are the number of grid points and time steps. In this paper, we present a GPU-based fast solution for Riesz space fractional equation. The GPU-based fast solution, which is based on the fast method using FFT and implemented with CUDA programming model, consists of parallel FFT, vector vector addition and vector-vector multiplication on GPU. The experimental results show that the GPU-based fast solution compares well with the exact solution. Compared to the known parallel fast solution on 8-core Intel E5-2670 CPU, the overall performance speedup on NVIDIA GTX650 GPU reaches 2.12 times and that on NVIDIA K20C GPU achieves 10.93 times.

关键词： parallel computing GPU fractional differential equation FFT fast solution

来源：评论

学校读者我要写书评

暂无评论

Reuse distance models for accelerating scientific computing workloads on multicore processors

Reuse distance models for accelerating scientific computing ...

引用

作者： Park, Jeonghyung PennState University Libraries

学位级别：Doctor of Philosophy

As the number of cores increase in chip multiprocessor microarchitecture (CMP)or multicores, we often observe performance degradation due to complex memorybehavior on such systems. To mitigate such inefficiencies, we develop schemes thatcan be used to characterize and improve the memory behavior of a multicore nodefor scientific computing applications that require high *** leverage the fact that such scientific computing applications often comprisecode blocks that are repeated, leading to certain periodic properties. We conjecturethat their periodic properties and their observable impacts on cache performancecan be characterized in sufficient detail by simple 'alpha + beta*sine'models. Additionally,starting from such a model of the observable reuse distances, we developa predictive cache miss model, followed by appropriate extensions for predictivecapability in the presence of *** consider the utilization of our reuse distance and cache miss models for acceleratingscientific workloads on multicore system. We use our cache miss modelto determine a set of preferred applications to be co-scheduled with a given applicationto minimize performance degradation from interference. Further, we proposea reuse distance reducing ordering that improves the performance of Laplacianmesh smoothing. We reorder mesh vertices based on the initial quality for eachnode and its neighboring nodes so that we can improve both temporal and spatiallocalities. The reordering results show that 38.75% of performance improvementof Laplacian mesh smoothing can be obtained by our reuse distance reducing orderingwhen running on a single core. 75x of speedup is obtained when scaling upto 32 cores.

关键词： high performance computing scientific computing parallel computing performance optimization

来源：评论

学校读者我要写书评

暂无评论

E-Biothon: an Experimental Platform for BioInformatics 10

E-Biothon: an Experimental Platform for BioInformatics

引用

Tenth International Conference on Computer Science and Information Technologies Revised Selected Papers CSIT

作者： Dayde, Michel Depardon, Benjamin Franc, Alain Gibrat, Jean-Francois Guilllier, Romaric Karami, Yasaman Sutter, Frederic Taddese, Bruck Chabbert, Marie Therond, Sylvie Univ Toulouse INPT CNRS ENSEEIHTIRIT Toulouse France SysFera Lyon France INRA UMR BioGeCo Cestas France INRIA Pleiade Team Talence France INRA UR1404 Unite Math & Informat Appl Genome Environm F-78350 Jouy En Josas France CNRS LIP ENS Lyon Lyon France UPMC CNRS UMR 7238 Unite Biol Computat & Quantitat Paris France CNRS LIP ENS Lyon CC IN2P3 Lyon France CNRS UMR 6214 INSERM Lab BNMIU1083Fac Med Angers France CNRS IDRIS Orsay France

ISBN: (纸本)9781467375627

The E-Biothon platform [1] is an experimental Cloud platform to help speed up and advance research in biology, health and environment. It is based on a Blue Gene/P system and a web portal that allow members of the bioinformatics community to easily launch their scientific applications. We describe in this paper the technical capacities of the platform, the different applications supported and finally a set of user experiences on the platform.

关键词： Bioinformatics parallel computing Cloud

来源：评论

学校读者我要写书评

暂无评论

Improving Energy Efficiency of IO-Intensive MapReduce Jobs 15

Improving Energy Efficiency of IO-Intensive MapReduce Jobs

引用

16th International Conference on Distributed computing and Networking

作者： Tiwari, Nidhi Sarkar, Santonu Indrawan-Santiago, Maria Bellur, Umesh Infosys Ltd Bangalore Karnataka India IITB Monash Res Acad Bombay Maharashtra India BITS Pilani Pilani Goa India Monash Univ Clayton Vic 3800 Australia Indian Inst Technol Bombay Maharashtra India

ISBN: (纸本)9781450329286

Map-Reduce is a popular data-parallel programming model for varied analysis of huge volumes of data. While a multicore and many CPU HPC infrastructure can be used to improve parallelism of map-reduce tasks, IO-bandwidth limitations may make them ineffective. IO-intensive activities are essential in any MapReduce cluster. In HPC nodes, IO-intensive jobs get queued at the IO-resources while the CPU remain underutilized, resulting in a poor performance, high power consumption and thus, energy inefficiency. In this paper, we investigate which power management setting can be used to improve the energy efficiency of IO-intensive MapReduce jobs by performing a thorough empirical study. Our analysis indicates that a constant CPU frequency can reduce the energy consumption of an IO-intensive job, while improving its performance. Consequently, we build a set of regression models to predict the energy consumption of IO-intensive jobs at a CPU frequency for a given input data volume. We obtained same set of models, with different coefficients, for two different types of IO-intensive jobs, which substantiates the suitability of identified models. These models predict respective outcomes with 80% accuracy for 80% of the new test cases.

关键词： Energy efficiency DVFS MapReduce Predictive energy models Power aware computing Energy characterization HPC parallel computing Distributed computing MapReduce Energy Efficiency

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 491 492 493 494 495 496 497 498 499 500 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：