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

34 篇 会议
33 篇 期刊文献

馆藏范围

67 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

67 篇 工学
- 65 篇 计算机科学与技术...
- 20 篇 电气工程
- 16 篇 软件工程
- 2 篇 信息与通信工程
- 1 篇 控制科学与工程
- 1 篇 化学工程与技术
8 篇 管理学
- 8 篇 管理科学与工程(可...
4 篇 理学
- 1 篇 数学
- 1 篇 化学
- 1 篇 地球物理学
- 1 篇 生物学

主题

67 篇 data intensive c...
12 篇 big data
11 篇 cloud computing
9 篇 high performance...
7 篇 mapreduce
6 篇 hadoop
6 篇 scheduling
5 篇 grid computing
4 篇 distributed syst...
3 篇 collective i/o
3 篇 data management
3 篇 data science
3 篇 scientific compu...
3 篇 gpu
2 篇 cluster middlewa...
2 篇 system architect...
2 篇 his
2 篇 workflows
2 篇 statistical tech...
2 篇 storage systems

机构

9 篇 texas tech univ ...
3 篇 univ maryland de...
2 篇 cracow univ tech...
2 篇 univ wisconsin d...
2 篇 chinese acad sci...
2 篇 fermilab natl ac...
2 篇 lawrence berkele...
2 篇 china univ geosc...
2 篇 chinese acad sci...
2 篇 ohio state univ ...
2 篇 china univ geosc...
2 篇 univ chicago il ...
1 篇 beihang univ sin...
1 篇 univ n carolina ...
1 篇 univ minnesota t...
1 篇 univ edinburgh s...
1 篇 university of ch...
1 篇 pacific nw natl ...
1 篇 oak ridge natl l...
1 篇 univ minnesota m...

作者

10 篇 chen yong
6 篇 liu jialin
5 篇 wang lizhe
4 篇 kolodziej joanna
3 篇 ma yan
3 篇 liu dingsheng
2 篇 ni jun
2 篇 roth philip c.
2 篇 zhang wanfeng
2 篇 wu min
2 篇 matthew greenway
2 篇 li wenjun
2 篇 chen ying
2 篇 yang hailong
2 篇 zhang minghuan
2 篇 khan samee u.
2 篇 liang wei
2 篇 greenway matthew
2 篇 streit achim
2 篇 weissman jon

语言

66 篇 英文
1 篇 中文

检索条件"主题词=Data Intensive Computing"

共 67 条记录，以下是51-60 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：

Design and Implementation of Metadata System in PetaShare

Design and Implementation of Metadata System in PetaShare

引用

21st International Conference on Scientific and Statistical database Management

作者： Wang, Xinqi Kosar, Tevfik Louisiana State Univ Dept Comp Sci Baton Rouge LA 70803 USA Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA

ISBN: (纸本)9783642022784

As the size of scientific and commercial datasets grows, it becomes imperative that an expressive metadata framework to be developed to facilitate access to the semantics of the data. However, the drive to enhance expressiveness and maximize performance often turns out to be contradictory. Hence, the design of metadata framework needs to take into consideration many factors in order to achieve balance between expressive power and performance. In this paper, we discuss the metadata framework developed for PetaShare and explain the design and implementation decisions we made in the process.

关键词： Metadata data intensive computing Ontology Protege iRODS PetaShare

来源：评论

学校读者我要写书评

暂无评论

DOSAS: Mitigating the Resource Contention in Active Storage Systems

DOSAS: Mitigating the Resource Contention in Active Storage ...

引用

IEEE International Conference on Cluster computing

作者： Chen, Chao Chen, Yong Roth, Philip C. Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN USA

ISBN: (纸本)9781467324229

Active storage provides an effective method to mitigate the I/O bottleneck problem of data intensive high performance computing applications. It can reduce the amount of data transferred as the application runs by moving appropriate computations close to the data. Prior research has achieved considerable progress in developing several active storage prototypes. However, existing studies have neglected the impact of resource contention when concurrent processes request IO operations from the same storage node simultaneously, which happens frequently in practice. In this paper, we analyze the impact of resource contention on active storage systems. Motivated by our analysis, we propose a novel Dynamic Operation Scheduling Active Storage architecture to address the resource contention issue. It offloads the active processing operations dynamically between storage nodes and compute nodes according to the system environment. By evaluating our architecture, we observed that: (1) resource contention is a critical problem for active storage systems, (2) the proposed dynamic operation scheduling method mitigates the problem, and (3) the new active storage architecture outperforms existing active storage systems.

关键词： dynamic active storage active storage resource contention high performance computing data intensive computing parallel I/O parallel file systems

来源：评论

学校读者我要写书评

暂无评论

MapReduce Across Distributed Clusters for data-intensive Applications

MapReduce Across Distributed Clusters for Data-intensive App...

引用

26th IEEE International Parallel and Distributed Processing Symposium (IPDPS) / Workshop on High Performance data intensive computing

作者： Wang, Lizhe Tao, Jie Marten, Holger Streit, Achim Khan, Samee U. Kolodziej, Joanna Chen, Dan Chinese Acad Sci Ctr Earth Observat & Digital Earth Beijing 100864 Peoples R China Steinbuch Ctr Comp Karlsruhe Inst Technol Karlsruhe Germany North Dakota State Univ Dept Elect & Comp Engn Fargo ND 58105 USA Univ Bielsko Biala Dept Math & Comp Sci Bielsko Biala Poland China Univ Geosci Sch Comp Beijing Peoples R China

ISBN: (纸本)9780769546766

Recently, the computational requirements for largescale data-intensive analysis of scientific data have grown significantly. In High Energy Physics (HEP) for example, the Large Hadron Collider (LHC) produced 13 petabytes of data in 2010. This huge amount of data are processed on more than 140 computing centers distributed across 34 countries. The MapReduce paradigm has emerged as a highly successful programming model for large-scale data-intensive computing applications. However, current MapReduce implementations are developed to operate on single cluster environments and cannot be leveraged for large-scale distributed data processing across multiple clusters. On the other hand, workflow systems are used for distributed data processing across data centers. It has been reported that the workflow paradigm has some limitations for distributed data processing, such as reliability and efficiency. In this paper, we present the design and implementation of G-Hadoop, a MapReduce framework that aims to enable large-scale distributed computing across multiple clusters. G-Hadoop uses the Gfarm file system as an underlying file system and executes MapReduce tasks across distributed clusters. Experiments of the G-Hadoop framework on distributed clusters show encouraging results.

关键词： MapReduce Hadoop data intensive computing

来源：评论

学校读者我要写书评

暂无评论

Hierarchical I/O Scheduling for Collective I/O 13

Hierarchical I/O Scheduling for Collective I/O

引用

13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid computing (CCGrid)

作者： Liu, Jialin Chen, Yong Zhuang, Yu Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA

ISBN: (纸本)9780769549965;9781467364652

The non-contiguous access pattern of many scientific applications results in a large number of I/O requests, which can seriously limit the data-access performance. Collective I/O has been widely used to address this issue. However, the performance of collective I/O could be dramatically degraded in today's high-performance computing system due to the increasing shuffle cost caused by highly concurrent data accesses. This situation tends to be even worse as many applications become more and more data intensive. Previous research has primarily focused on optimizing I/O access cost in collective I/O but largely ignored the shuffle cost involved. In this study, we propose a new hierarchical I/O scheduling (HIO) algorithm to address the increasing shuffle cost in collective I/O. The fundamental idea is to schedule applications' I/O requests based on a shuffle cost analysis to achieve the optimal overall performance, instead of achieving optimal I/O accesses only. The algorithm is currently evaluated with the MPICH2 and PVFS2. Both theoretical analysis and experimental tests show that the proposed hierarchical I/O scheduling has a potential in addressing the degraded performance issue of collective I/O with highly concurrent accesses.

关键词： collective I/O scheduling high-performance computing big data data intensive computing

来源：评论

学校读者我要写书评

暂无评论

Building reliable and efficient data transfer and processing pipelines

Building reliable and efficient data transfer and processing...

引用

1st International Workshop on Middleware for Grid computing held in Conjunction with the ACM/IFIP/USINIX Middleware Conference

作者： Kosar, T Kola, G Livny, M Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

Scientific distributed applications have an increasing need to process and move large amounts of data across wide area networks. Existing systems either closely couple computation and data movement, or they require substantial human involvement during the end-to-end process. We propose a framework that enables scientists to build reliable and efficient data transfer and processing pipelines. Our framework provides a universal interface to different data transfer protocols and storage systems. It has sophisticated How control and recovers automatically from network, storage system, software and hardware failures. We successfully used data pipelines to replicate and process three terabytes of DPOSS astronomy image dataset and several terabytes of WCER educational video dataset. In both cases, the entire process was performed without any human intervention and the data pipeline recovered automatically from various failures. Copyright (c) 2005 John Wiley & Sons, Ltd.

关键词： workflows data pipelines data transfer data intensive computing distributed systems Grid computing fault tolerance

来源：评论

学校读者我要写书评

暂无评论

A general accelerated R package using GPU

A general accelerated R package using GPU

引用

2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

作者： Huang, Jie Zhuang, Bojin Su, Fei Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China

ISBN: (纸本)9781509018932

In this paper, we present cuLib, a R package that provides an easy-to-access interface for utilizing the computing power of NVIDIA GPU. The cuLib package aims to make GPU-based parallel programming easier, flexible and high-performance. It allows the use of GPU computing in R without further knowledge because the syntax for definition and manipulation of GPU data is similar to formal R language. cuLib is compatible with device and operation system. The data interface is very flexible, enabling the users manipulate data freely. The cuLib package provides an R wrapper for libraries of NVIDIA's CUDA toolkit and numerous operations. More importantly, it is not only a mathematical tool but also practicable for the algorithm in dealing with data intensive computation.

关键词： R GPU CUDA mathematical tool data intensive computing

来源：评论

学校读者我要写书评

暂无评论

Hybrid Resource Management for HPC and data intensive Workloads 19

Hybrid Resource Management for HPC and Data Intensive Worklo...

引用

19th Annual IEEE/ACM International Symposium on Cluster, Cloud, and Grid computing (CCGRID)

作者： Souza, Abel Rezaei, Mohamad Laure, Erwin Tordsson, Johan Umea Univ Dept Comp Sci Umea Sweden KTH Royal Inst Technol PDC Ctr High Performance Comp Stockholm Sweden

ISBN: (纸本)9781728109121

High Performance computing (HPC) and data intensive (DI) workloads have been executed on separate clusters using different tools for resource and application management. With increasing convergence, where modern applications are composed of both types of jobs in complex workflows, this separation becomes a growing overhead and the need for a common platform increases. Executing both workload classes on the same clusters not only enables hybrid workflows, but can also increase system efficiency, as available hardware often is not fully utilized by applications. While HPC systems are typically managed in a coarse grained fashion, with exclusive resource allocations, DI systems employ a finer grained regime, enabling dynamic allocation and control based on application needs. On the path to full convergence, a useful and less intrusive step is a hybrid resource management system allowing the execution of DI applications on top of standard HPC scheduling systems. In this paper we present the architecture of a hybrid system enabling dual-level scheduling for DI jobs in HPC infrastructures. Our system takes advantage of real-time resource profiling to efficiently co-schedule HPC and DI applications. The architecture is easily extensible to current and new types of distributed applications, allowing efficient combination of hybrid workloads on HPC resources with increased job throughput and higher overall resource utilization. The implementation is based on the Sturm and Mesos resource managers for HPC and DI jobs. Experimental evaluations in a real cluster based on a set of representative HPC and DI applications demonstrate that our hybrid architecture improves resource utilization by 20%, with 12% decrease on queue makespan while still meeting all deadlines for HPC jobs.

关键词： Resource Management High Performance computing data intensive computing Mesos Sturm Bootstrapping

来源：评论

学校读者我要写书评

暂无评论

Multicore-Based High Performance Image Analysis for Batch Processing in Drug Discovery 18

Multicore-Based High Performance Image Analysis for Batch Pr...

引用

IMACS World Congress/Modelling and Simulation Society-of-Australia-and-New-Zealand (MSSANZ)/18th MODSIM09 Biennial Conference on Modelling and Simulation

作者： Wang, D. D. Bourke, D. Domanski, L. Vallotton, P. CSIRO Math & Informat Sci Biotech Imaging N Ryde NSW 1670 Australia

ISBN: (纸本)9780975840078

Automated image analysis allows drug companies to use High Content Screening (HCS) to measure subtle but important changes in fluorescently labeled cells, both rapidly and accurately. Researchers using HCS imaging systems generate thousands of images from which they measure millions of parameters. Some images can be very "dense", with hundreds of cells and complicated cell morphology. It may take several hours or even days to process the images generated from a single experiment which may be unacceptable for the workflows in laboratories. The large image datasets and fast turnaround requirement has made the efficient batch processing of the massive amount of images a challenging task. With the enormous progress in computing power, computers with multi-core CPUs are now becoming standard. The multi-core shift presents unprecedented opportunities for researchers to deal with large datasets with acceptable computing performance. Unfortunately, having a dual core CPU does not speedup one's application automatically. In fact, most applications use just a single core and see no speed improvements when run on a multi-core machine. It is still a challenge to develop parallel algorithms that address the issues of task concurrency and data parallelism, and that actually take advantage of those multiple processors. In this paper, some efforts have been made to develop a multi-core based high performance solution to speedup the batch processing for micro-well-plate-based HCS. The proposed solution employs an automatic parallelization engine which automatically dispatches the batch processing tasks. The engine automatically detects the number of processors available on the computer on which our High Content Analysis (HCA) software runs, and then creates equal number of work threads to process images in parallel. Therefore, the solution can automatically scale to additional cores and future multi-core processors. Loop parallelization mechanism has been implemented to assign diffe

关键词： High Performance computing data intensive computing Parallel Processing Image Analysis Batch processing High Content Screening

来源：评论

学校读者我要写书评

暂无评论

The Design of a Community Science Cloud: The Open Science data Cloud Perspective

The Design of a Community Science Cloud: The Open Science Da...

引用

25th ACM/IEEE International Conference for High Performance computing, Networking, Storage and Analysis (SC)

作者： Grossman, Robert L. Greenway, Matthew Heath, Allison P. Powell, Ray Suarez, Rafael D. Wells, Walt White, Kevin Atkinson, Malcolm Klampanos, Iraklis Alvarez, Heidi L. Harvey, Christine Mambretti, Joe J. Univ Chicago Chicago IL 60637 USA Univ Edinburgh Edinburgh EH8 9YL Midlothian Scotland Florida Int Univ Miami FL 33199 USA Richard Stockton Coll New Jersey Galloway NJ USA Northwestern Univ Evanston IL 60208 USA

ISBN: (纸本)9780769549569;9781467362184

In this paper we describe the design, and implementation of the Open Science data Cloud, or OSDC. The goal of the OSDC is to provide petabyte-scale data cloud infrastructure and related services for scientists working with large quantities of data. Currently, the OSDC consists of more than 2000 cores and 2 PB of storage distributed across four data centers connected by 10G networks. We discuss some of the lessons learned during the past three years of operation and describe the software stacks used in the OSDC. We also describe some of the research projects in biology, the earth sciences, and social sciences enabled by the OSDC.

关键词： data intensive computing cloud computing science clouds open data

来源：评论

学校读者我要写书评

暂无评论

Anatomy of Functional Components of Healthcare Information System 8

Anatomy of Functional Components of Healthcare Information S...

引用

2015 Eighth International Conference on Internet computing for Science and Engineering (ICICSE)

作者： Ni, Jun Chen, Ying Sha, Jie Zhang, Minghuan Shanghai Sanda Univ Sch Informat Sci & Technol Big Data & Informat Res Lab Shanghai 201209 Peoples R China

ISBN: (纸本)9781509004546

The healthcare information system (HIS) becomes very important in today medical and healthcare services. Huge healthcare data with various types and structures are generated in a daily base. The processing and mining of the data is in urgent demand. How to design a HIS system that cooperates with cloud computing and big data technology to meet such emergence become critical, technical challenging to today's healthcare services. In this paper we present our preliminary study of computing architecture for such HIS. The architecture design is based on HIS's sub-systems (functional modules) each of which matches with corresponding healthcare service. The architecture is designed based on the features of desired data intensive computing systems with considerations of data volume and growth rate, heterogeneous data structures, and methods of data storages.

关键词： healthcare information systems HIS big data cloud computing healthcare services system architecture system of systems SoSE data intensive computing high performance computing HPC parallel computing

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7页 << < 1 2 3 4 5 6 7 > >>

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：