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41-50 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：

Pressure-Driven Hardware Managed Thread Concurrency for Irregular Applications 7

Pressure-Driven Hardware Managed Thread Concurrency for Irre...

引用

7th Workshop on Irregular Applications - Architectures and Algorithms (IA3)

作者： Leidel, John D. Wang, Xi Chen, Yong Texas Tech Univ Box 43104 Lubbock TX 79409 USA

ISBN: (纸本)9781450351362

Given the increasing importance of efficient data intensive computing, we find that modern processor designs are not well suited to the irregular memory access patterns found in these algorithms. This research focuses on mapping the compiler's instruction cost scheduling logic to hardware managed concurrency controls in order to minimize pipeline stalls. In this manner, the hardware modules managing the low-latency thread concurrency can be directly understood by modern compilers. We introduce a thread context switching method that is managed directly via a set of hardware-based mechanisms that are coupled to the compiler instruction scheduler. As individual instructions from a thread execute, their respective cost is accumulated into a control register. Once the register reaches a pre-determined saturation point, the thread is forced to context switch. We evaluate the performance benefits of our approach using a series of 24 benchmarks that exhibit performance acceleration of up to 14.6X.

关键词： data intensive computing irregular algorithms thread concurrency context switching

来源：评论

学校读者我要写书评

暂无评论

In-Map/In-Reduce: Concurrent Job Execution in MapReduce 13

In-Map/In-Reduce: Concurrent Job Execution in MapReduce

引用

13th IEEE International Conference on Trust Security and Privacy in computing and Communications (TrustCom)

作者： Idris, Muhammad Hussain, Shujaat Lee, Sungyoung Kyung Hee Univ Ubiquitous Comp Lab Coll Elect & Informat Seoul South Korea

ISBN: (纸本)9781479965137

Hadoop based MapReduce(MR) has emerged as big data processing mechanism in terms of its data intensive applications. In data intensive systems;analysis and visualizations as a result of various algorithms can lead to differentiable and comparable results. Current implementations of MR facilitates to reuse the results of MR jobs in other MR jobs and to distribute the cloud resources among jobs. However, very little work is done in terms of using same data for multiple algorithms at the same time in a single job using either shared resources or dynamic resource allocation based on the data and scheduling of MapReduce jobs. In this paper we propose a method to execute multiple algorithms on same data in HDFS concurrently and to use the same available resources by dynamically managing the task assignment and results aggregation. Our proposed approach reduces the execution time and supports multiple algorithms execution in parallel. In-Map/In-Reduce shows 200% decrease in execution time.

关键词： Big data Hadoop MapReduce data intensive computing HDFS

来源：评论

学校读者我要写书评

暂无评论

Low Power computing and Simultaneous Electro-Optical/Radar data Processing using IBM's NS16e 16-chip Neuromorphic Hardware

Low Power Computing and Simultaneous Electro-Optical/Radar D...

引用

IEEE High Performance Extreme computing Conference (HPEC)

作者： Barnell, Mark Raymond, Courtney Brown, Daniel Wilson, Matthew Cote, Eric Air Force Res Lab Informat Directorate Rome NY 13441 USA SRC Inc Radars & Sensors Div N Syracuse NY USA

ISBN: (纸本)9781728150208

For the first time ever, advanced machine learning (ML) compute architectures, techniques, and methods were demonstrated on United States Geological Survey (USGS) optical imagery and Department of Defense (DoD) Synthetic Aperture Radar (SAR) imagery, simultaneously, using IBM's new NS16e neurosynaptic processor board comprised of 16 TrueNorth chips. The Air Force Research Laboratory (AFRL) Information Directorate Advanced computing and Communications Division continues to develop and demonstrate new bio-inspired computing algorithms and architectures, designed to provide advanced, ultra-low power, ground and airborne High-Performance computing (HPC) solutions to meet operational and tactical, real-time processing needs for Intelligence, Surveillance, and Reconnaissance (ISR) missions on small form factor hardware, and in Size, Weight and Power (SWaP) constrained environments. With an average throughput of 16,000 inferences per second, the system provided a processing efficiency of 1,066 inferences per Watt. The NS16e power utilization never exceeded 15 Watts for this application. The contribution of power consumption from TrueNorth processors was bound to less than 5.5 Watts.

关键词： computing technologies for challenging form factors radar processing and exploitation data intensive computing Machine Learning (ML) High Performance computing (HPC) Artificial Intelligence (AI) region proposal networks object detection Deep Learning (DL) Convolutional Neural Networks (CNN) advanced processor architectures autonomy Electro-Optical (EO) Intelligence Surveillance and Reconnaissance (ISR) neuromorphic computing

来源：评论

学校读者我要写书评

暂无评论

Implementing data cube construction using a cluster middleware: Algorithms, implementation experience, and performance evaluation

Implementing data cube construction using a cluster middlewa...

引用

2nd IEEE/ACM International Symposium on Cluster computing and the Grid

作者： Yang, G Jin, RM Agrawal, G Ohio State Univ Dept Comp & Informat Sci Columbus OH 43210 USA

ISBN: (纸本)0769515827

With increases in the amount of data available for analysis in commercial settings, On Line Analytical Processing (OLAP) and decision support have become important applications for high performance computing. Implementing such applications on clusters requires a lot of expertise and effort, particularly because of the sizes of input and output datasets. In this paper we describe our experiences in developing one such application using a cluster middleware, called ADR. We focus on the problem of data cube construction, which commonly arises in multi-dimensional OLAP. We show how ADR, originally developed for scientific data intensive applications, can be used for carrying out an efficient and scalable data cube construction implementation. A particular issue with the use of ADR is tiling of output datasets. We present new algorithms that combine inter-processor communication and tiling within each processor These algorithms preserve the important properties that are desirable front any parallel data cube construction algorithm. We have carried out a detailed evaluation of our implementation. The main results from our experiments are as follows: 1) High speedups are achieved on both dense and sparse datasets, even though we have used simple algorithms that sequentialize a part of the computation, 2) The execution time depends only upon the amount of computation, and does not increase in a super-linear fashion as the dataset size or the number of tiles increases, and 3) As the datasets become more sparse, sequential performance degrades, but the parallel speedups are still quite good.

关键词： Cluster middleware data cube construction data intensive computing Performance evaluation

来源：评论

学校读者我要写书评

暂无评论

Applying Selectively Parallel I/O Compression to Parallel Storage Systems 1

引用

20th European Conference on Parallel computing (Euro-Par)

作者： Filgueira, Rosa Atkinson, Malcolm Tanimura, Yusuke Kojima, Isao Univ Edinburgh Sch Informat Edinburgh EH8 9AB Midlothian Scotland AIST Informat Technol Res Inst Tsukuba Ibaraki Japan

ISBN: (数字)9783319098739

ISBN: (纸本)9783319098739;9783319098722

This paper presents a new I/O technique called Selectively Parallel I/O Compression (SPIOC) for providing high-speed storage and access to data in QoS enabled parallel storage systems. SPIOC reduces the time of I/O operations by applying transparent compression between the computing and the storage systems. SPIOC can predict whether to compress or not at runtime, allowing parallel or sequential compression techniques, guaranteeing QoS and allowing partial and full reading by decompressing the minimum part of the file. SPIOC maximises the measured efficiency of data movement by applying run-time customising compression before storing data in the Papio storage system.

关键词： Parallel File System data intensive computing Compression algorithms Adaptive systems

来源：评论

学校读者我要写书评

暂无评论

Modeling data Heterogeneity Using Big dataSpace Architecture 9th

Modeling Data Heterogeneity Using Big DataSpace Architecture

引用

9th International Conference on Advanced computing and Communication Technologies (ICACCT)

作者： Sheokand, Vishal Singh, Vikram Natl Inst Technol Dept Comp Engn Kurukshetra 136119 Haryana India

ISBN: (纸本)9789811010231;9789811010217

With the wide use of information expertise in advanced analytics, basically three characteristics of big data have been identified. These are volume, velocity and variety. The first of these two have enjoyed quite a lot of focus, volume of data and velocity of data, less thought has been focused on variety of available data worldwide. data variety refers to the nature of data in store and under processing, which has three orthogonal natures: structured, semi-structured and unstructured. To handle the variety of data, current universally acceptable solutions are either costlier than customized solutions or less efficient to cater data heterogeneity. Thus, a basic idea is to, first design data processing systems that create abstraction that covers a wide range of data types and support fundamental processing on underlying heterogeneous data. In this paper, we conceptualized data management architecture 'Big dataSpace', for big data processing with the capability to combine heterogeneous data from various data sources. Further, we explain how Big dataSpace architecture can help in processing the heterogeneous and distributed data, a fundamental task in data management.

关键词： Big data Mining dataspace System data intensive computing data Variety data Volume data Integration System

来源：评论

学校读者我要写书评

暂无评论

Improving data Analysis Performance for High-Performance computing with Integrating Statistical Metadata in Scientific datasets

Improving Data Analysis Performance for High-Performance Com...

引用

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

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

ISBN: (纸本)9780769549569;9781467362184

Scientific datasets and libraries, such as HDF5, ADIOS, and NetCDF, have been used widely in many data intensive applications. These libraries have their special file formats and I/O functions to provide efficient access to large datasets. When the data size keeps increasing, these high level I/O libraries face new challenges. Recent studies have started to utilize database techniques such as indexing and subsetting, and data reorganization to manage the increasing datasets. In this work, we present a new approach to boost the data analysis performance, namely Fast Analysis with Statistical Metadata (FASM), via data subsetting and integrating a small amount of statistics into the original datasets. The added statistical information illustrates the data shape and provides knowledge of the data distribution;therefore the original I/O libraries can utilize these statistical metadata to perform fast queries and analyses. The proposed FASM approach is currently evaluated with the PnetCDF on Lustre file systems, but can also be implemented with other scientific libraries. The FASM can potentially lead to a new dataset design and can have an impact on big data analysis.

关键词： high performance computing data intensive computing big data storage systems statistical techniques FASM

来源：评论

学校读者我要写书评

暂无评论

Design Issues of Big data Parallelisms 3rd

Design Issues of Big Data Parallelisms

引用

3rd International Conference on Information System Design and Intelligent Applications (INDIA)

作者： Mondal, Koushik Indian Inst Technol Indore Madhya Pradesh India

ISBN: (纸本)9788132227526;9788132227502

data intensive computing for Scientific Research needs effective tools for data capture, curate them for designing appropriate algorithms and multidimensional analysis for effective decision making for the society. Different computational environments used for different data intensive problems such as Sentiment Analysis and Opinion Mining of Social media, Massive Open Online Courses (MOOCs), Large Hadron Collider of CERN, Square Kilometer Array (SKA) of radio telescopes project, are usually capable of generating exabytes (EB) of data per day, but present situations limits them to more manageable data collection rates. Different disciplines and data generation rates of different lab experiments, online as well as offline, make the issue of creating effective tools a formidable problem. In this paper we will discuss about different data intensive computing tools, trends of different emerging technologies, how big data processing heavily relying on those effective tools and how it helps in creating different models and decision making.

关键词： data intensive computing High dimensional data Scalable framework data science Machine learning Semi-stochastic X-informatics Big data

来源：评论

学校读者我要写书评

暂无评论

In-advance data Analytics for Reducing Time to Discovery 2

In-advance Data Analytics for Reducing Time to Discovery

引用

IEEE International Conference on Big data

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

ISBN: (纸本)9781479956661

Scientific 2workflow involves data generation, data analysis, and knowledge discovery. As the data volume exceeds a few terabytes (TB) in a single simulation run, the data movement, which happens among data generation, data analysis, and knowledge discovery, becomes a bottleneck in most scientific big data applications. Our previous work shows that reusing the analysis results can have a significant potential in reducing the overlap between data movement among compute nodes and storage nodes. In this work, we propose a new in-advance data analytics method to augment the result reuse. The fundamental idea of this in-advance data analytics method and its prototyping system is to predict the potential useful analytics operations by studying the users' analysis pattern. The predicted analysis operation is pro-actively performed on existing data and the analysis results are stored in an in-memory database for result reuse. The evaluation shows that the in-advance data analytics method and its prototyping system gains 1.2X-6.1X speedup in I/O performance improvement with 50% data overlapping and 10%-100% operation recommendation hit rate. The proposed in-advance data analytics method brings a new promising data reduction solution for big data applications.

关键词： in-advance data analytics big data data intensive computing scientific computing

来源：评论

学校读者我要写书评

暂无评论

OpenFlow Enabled Hadoop Over Local and Wide Area Clusters

OpenFlow Enabled Hadoop Over Local and Wide Area Clusters

引用

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

作者： Narayan, Sandhya Bailey, Stuart Greenway, Matthew Grossman, Robert Heath, Allison Powell, Ray Daga, Anand InfoBlox Inc Santa Clara CA USA Univ Chicago Inst Comp Chicago IL USA Univ Houston Dept Comp Sci Houston TX USA

ISBN: (纸本)9780769549569;9781467362184

Hadoop has emerged as an important platform for data intensive computing. The shuffle and sort phases of a MapReduce computation often saturate top of the rack switches, as well as switches that aggregate multiple racks. In addition, MapReduce computations often have "hot spots" in which the computation is lengthened due to inadequate bandwidth to some of the nodes. In principle, OpenFlow enables an application to adjust the network topology as required by the computation, providing additional network bandwidth to those resources requiring it. We describe Hadoop-OFE, which is an OpenFlow enabled version of Hadoop that dynamically modifies the network topology in order to improve the performance of Hadoop.

关键词： Hadoop OpenFlow OpenFlow over Ethernet data intensive computing MapReduce

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：