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

1,133 篇 会议
24 篇 期刊文献
20 册 图书

馆藏范围

1,177 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

976 篇 工学
- 909 篇 计算机科学与技术...
- 419 篇 软件工程
- 228 篇 信息与通信工程
- 147 篇 电气工程
- 67 篇 电子科学与技术（可...
- 42 篇 控制科学与工程
- 39 篇 生物工程
- 29 篇 机械工程
- 29 篇 动力工程及工程热...
- 24 篇 测绘科学与技术
- 19 篇 生物医学工程（可授...
- 15 篇 安全科学与工程
- 11 篇 光学工程
- 9 篇 建筑学
- 9 篇 网络空间安全
- 8 篇 仪器科学与技术
- 8 篇 材料科学与工程（可...
- 8 篇 化学工程与技术
210 篇 理学
- 138 篇 数学
- 42 篇 生物学
- 35 篇 系统科学
- 34 篇 统计学（可授理学、...
- 19 篇 物理学
133 篇 管理学
- 98 篇 管理科学与工程(可...
- 62 篇 工商管理
- 43 篇 图书情报与档案管...
17 篇 经济学
- 17 篇 应用经济学
17 篇 农学
- 15 篇 作物学
14 篇 法学
- 10 篇 社会学
13 篇 医学
- 9 篇 临床医学
2 篇 教育学
2 篇 军事学
1 篇 艺术学

主题

36 篇 cloud computing
33 篇 parallel process...
27 篇 parallel computi...
24 篇 network topology
22 篇 neural networks
19 篇 machine learning
17 篇 deep learning
17 篇 network security
16 篇 grid computing
15 篇 scalability
15 篇 computer archite...
15 篇 cluster computin...
14 篇 parallel program...
14 篇 computational mo...
14 篇 network architec...
14 篇 edge computing
13 篇 mapreduce
13 篇 high performance...
12 篇 monitoring
12 篇 topology

机构

17 篇 university of ch...
14 篇 institute of com...
9 篇 national univers...
6 篇 college of compu...
6 篇 shandong univ in...
5 篇 university of sc...
5 篇 school of comput...
5 篇 services computi...
5 篇 college of intel...
4 篇 college of physi...
4 篇 qingdao univ col...
4 篇 school of comput...
4 篇 univ bologna dei...
4 篇 jilin univ coll ...
4 篇 imperial vision ...
4 篇 institute of com...
3 篇 college of compu...
3 篇 national enginee...
3 篇 tsinghua univers...
3 篇 department of co...

作者

14 篇 jin hai
7 篇 li daxing
6 篇 liu yang
6 篇 dvorsky jiri
6 篇 shi xuanhua
6 篇 hsu ching-hsien
6 篇 vojacek lukas
5 篇 qian depei
5 篇 xiao limin
4 篇 man ka lok
4 篇 liu jie
4 篇 drazdilova pavla
4 篇 rossi davide
4 篇 benini luca
4 篇 tan guangming
4 篇 zhang jie
4 篇 daxing li
4 篇 beferull-lozano ...
4 篇 li wei
4 篇 tong tong

语言

1,170 篇 英文
5 篇 中文
3 篇 其他

检索条件"任意字段=8th IFIP International Conference on Network and Parallel Computing"

共 1177 条记录，以下是251-260 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：

Synchronized Preprocessing of Sensor Data 8

Synchronized Preprocessing of Sensor Data

引用

8th IEEE international conference on Big Data (Big Data)

作者： Tawakuli, Amal Kaiser, Daniel Engel, thomas Univ Luxembourg Dept Comp Sci Esch Sur Alzette Luxembourg

ISBN: (纸本)9781728162515

Sensor data whether collected for machine learning, deep learning or other applications must be preprocessed to fit input requirements or improve performance and accuracy. Data preparation is an expensive, resource consuming and complex phase often performed centrally on raw data for a specific application. the dataflow between the edge and the cloud can be enhanced in terms of efficiency, reliability and lineage by preprocessing the datasets closer to their data sources. We propose a dedicated data preprocessing framework that distributes preprocessing tasks between a cloud stage and two edge stages to create a dataflow with progressively improving quality. the framework handles heterogenous data and dynamic preprocessing plans simultaneously targeting diverse applications and use cases from different domains. Each stage autonomously executes sensor specific preprocessing plans in parallel while synchronizing the progressive execution and dynamic updates of the preprocessing plans with the other stages. Our approach minimizes the workload on central infrastructures and reduces the resources used for transferring raw data from the edge. We also demonstrate that preprocessing data can be sensor specific rather than application specific and thus can be performed prior to knowing a specific application.

关键词： Data Quality Data Preprocessing Sensor Data Edge computing Data Management

来源：评论

学校读者我要写书评

暂无评论

Dynamic Load Balance for Hot-spot and Unbalance Region Problems in HBase 8

Dynamic Load Balance for Hot-spot and Unbalance Region Probl...

引用

8th IEEE international conference on Big Data (Big Data)

作者： Chen, Yibo Xiang, Xing Ling, Xiao Zhang, Xiangchi Wu, Fan Gao, Jianliang State Grid Hunan Elect Power Co Ltd Informat & Commun Branch Changsha Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China

ISBN: (纸本)9781728162515

In distributed storage, dynamic loads usually cause a huge burden such as hot-spot and unblance region problems. In this paper, we focus on the problems of dynamic unbalance loads in distributed database HBase. Firstly, we propose a new intelligent load balance framework to support the collection and analysis of dynamic load information. then, two efficient load balance strategies are proposed to solve hot-spot problem and unbalance region problem. Finally, we evaluate the strategies on HBase using real-world big data on distributed clusters. Compared to the existing balance strategies, the proposed load balance strategies can make more effective use of the resources and reduce the response time for large dynamic loads.

关键词： distributed storage parallel and distributed computing dynamic loads intelligent load balance

来源：评论

学校读者我要写书评

暂无评论

Blink: Towards Efficient RDMA-based Communication Coroutines for parallel Python Applications 27

Blink: Towards Efficient RDMA-based Communication Coroutines...

引用

27th IEEE international conference on High Performance computing, Data, and Analytics (HiPC)

作者： Shafi, Aamir Hashmi, Jahanzeb Maqbool Subramoni, Hari Panda, Dhabaleswar K. Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9781665422925

Python is emerging as a popular language in the data science community due to its ease-of-use, vibrant community, and rich set of libraries. Dask is a popular Python-based distributed computing framework that allows users to process large amounts of data on parallel hardware. the Dask distributed package is a non-blocking, asynchronous, and concurrent library that offers support for distributed execution of tasks on datacenter and HPC environments. A few key requirements of designing high-performance communication backends for Dask distributed is to provide scalable support for coroutines that are unlike regular Python functions and can only be invoked from asynchronous applications. In this paper, we present Blink-a high-performance communication library for Dask on high-performance RDMA networks like InfiniBand. Blink offers a multi-layered architecture that matches the communication requirements of Dask and exploits high-performance interconnects using a Cython wrapper layer to the C backend. We evaluate the performance of Blink against other counterparts using various micro-benchmarks and application kernels on three different cluster testbeds with varying interconnect speeds. Our micro-benchmark evaluation reveals that Blink outperforms other communication backends by more than 3x for message sizes ranging from 1 Byte to 64 KByte, and by a factor of 2x for message sizes ranging from 128 KByte to 8 MByte. Using various application-level evaluations, we demonstrate that Dask achieves up to 7% improvement in application throughput (e.g., total worker throughput).

关键词： Asynchronous I/O in Python parallel and Distributed computing Framework Communication on RDMA networks Efficient Data Science in Python

来源：评论

学校读者我要写书评

暂无评论

A Partitioning GPU-based Algorithm for Processing the k Nearest-Neighbor Query 20

A Partitioning GPU-based Algorithm for Processing the k Near...

引用

12th international conference on Management of Digital EcoSystems

作者： Velentzas, Polychronis Vassilakopoulos, Michael Corral, Antonio Univ Thessaly Dept Elect & Comp Engn Data Structuring & Engn Lab Volos Greece Univ Almeria Dept Informat Almeria Spain

ISBN: (纸本)9781450381154

the kappa Nearest-Neighbor (kappa-NN) query is a common spatial query that appears in several big data applications. Typically, GPU devices have much larger numbers of processing cores than CPUs and faster device memory than main memory accessed by CPUs, thus, providing higher computing power. We propose and implement a new GPU-based partitioning algorithm for the kappa-NN query, using the CUDA runtime API. Due to partitioning, this algorithm avoids calculating distances for the whole dataset. Using synthetic and real datasets, we present an extensive experimental performance comparison against six existing algorithms. these algorithms are based on calculating distances for the whole in-memory dataset. this comparison shows that the new algorithm excels in all the conducted experiments and outperforms these six algorithms.

关键词： k Nearest-Neighbor Query GPU algorithms Spatial query Partitioning algorithms parallel computing

来源：评论

学校读者我要写书评

暂无评论

Kollaps/thunderstorm: Reproducible Evaluation of Distributed Systems Tutorial Paper 20th

Kollaps/Thunderstorm: Reproducible Evaluation of Distributed...

引用

20th ifip WG 6.1 international conference on Distributed Applications and Interoperable Systems (DAIS) Held as Part of the 15th international Federated conference on Distributed computing Techniques (DisCoTec)

作者： Matos, Miguel Univ Lisbon Lisbon Portugal INESC ID Lisbon Portugal

ISBN: (纸本)9783030503222;9783030503239

Reproducing experimental results is nowadays seen as one of the greatest impairments for the progress of science in general and distributed systems in particular. this stems from the increasing complexity of the systems under study and the inherent complexity of capturing and controlling all variables that can potentially affect experimental results. We argue that this can only be addressed with a systematic approach to all the stages and aspects of the evaluation process, such as the environment in which the experiment is run, the configuration and software versions used, and the network characteristics among others. In this tutorial paper, we focus on the networking aspect, and discuss our ongoing research efforts and tools to contribute to a more systematic and reproducible evaluation of large scale distributed systems.

关键词： Distributed Systems network characteristic Complex systems science in general large-scale systems

来源：评论

学校读者我要写书评

暂无评论

Efficient Implementation of Kyber on Mobile Devices 27

Efficient Implementation of Kyber on Mobile Devices

引用

27th IEEE international conference on parallel and Distributed Systems, ICPADS 2021

作者： Zhao, Lirui Zhang, Jipeng Huang, Junhao Liu, Zhe Hancke, Gerhard Nanjing University of Aeronautics and Astronautics Jiangsu China Beijing Normal University-Hong Kong Baptist University United International College Guangdong China State Key Laboratory of Cryptology Beijing100878 China City University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781665408783

Kyber, an IND-CCA-secure key encapsulation mechanism (KEM) based on the MLWE problem, has been shortlisted for the third round evaluation of the NIST Post-Quantum Cryptography Standardization. In this paper, we explored the optimizations of Kyber in high-performance processors from the ARM Cortex-A series, which are widely used in mainstream mobile phones. To improve the performance of Kyber, we utilized the powerful SIMD instruction set NEON in an ARMv8-A to parallelize the core modules of Kyber, i.e., modular reduction and NTT. Specifically, we specially designed the optimized implementation based on the characteristic of the NEON instruction set for the Barrett and Montgomery reduction algorithms. To make full use of the computing power of NEON instructions, we proposed a novel strategy for computing the 16-bit Barrett reduction without handling the 32-bit intermediate result. Our Barrett and Montgomery reduction showed 8.52 and 8.89 times faster than the reference implementation. As for NTT/INTT, we adopted the 2+5 layer merging strategy on an ARMv8-A to implement NTT/INTT after carefully analyzing the register occupancy of various layer merging techniques. thanks to the selected layer merging strategy, our NTT and INTT achieved 11.89 and 13.45 times speedups compared with the reference implementation. Our optimized software achieved 1.77×, 1.85×, and 2.16× speedups for key generation, encapsulation, and decapsulation compared with Kyber's reference implementation. © 2021 IEEE.

关键词： Neon

来源：评论

学校读者我要写书评

暂无评论

Communication-Efficient Local Stochastic Gradient Descent for Scalable Deep Learning 8

Communication-Efficient Local Stochastic Gradient Descent fo...

引用

8th IEEE international conference on Big Data (Big Data)

作者： Lee, Sunwoo Kang, Qiao Agrawal, Ankit Choudhary, Alok Liao, Wei-keng Northwestern Univ Dept Elect & Comp Engn Evanston IL 60208 USA

ISBN: (纸本)9781728162515

Synchronous Stochastic Gradient Descent (SGD) with data parallelism, the most popular parallel training strategy for deep learning, suffers from expensive gradient communications. Local SGD with periodic model averaging is a promising alternative to synchronous SGD. the algorithm allows each worker to locally update its own model, and periodically averages the model parameters across all the workers. While this algorithm enjoys less frequent communications, the convergence rate is strongly affected by the number of workers. In order to scale up the local SGD training without losing accuracy, the number of workers should be sufficiently small so that the model converges reasonably fast. In this paper, we discuss how to exploit the degree of parallelism in local SGD while maintaining model accuracy. Our training strategy employs multiple groups of processes and each group trains a local model based on data parallelism. the local models are periodically averaged across all the groups. Based on this hierarchical parallelism, we design a model averaging algorithm that has a cheaper communication cost than allreduce-based approach. We also propose a practical metric for finding the maximum number of workers that does not cause a significant accuracy loss. Our experimental results demonstrate that our proposed training strategy provides a significantly improved scalability while achieving a comparable model accuracy to synchronous SGD.

关键词： Deep Learning Local SGD parallel Training

来源：评论

学校读者我要写书评

暂无评论

IMpart: A Partitioning-based parallel Approach to Accelerate Influence Maximization

IMpart: A Partitioning-based Parallel Approach to Accelerate...

引用

international conference on High Performance computing

作者： Reet Barik Marco Minutoli Mahantesh Halappanavar Ananth Kalyanaraman Washington State University Pullman WA Pacific Northwest National Laboratory Richland WA

Influence maximization (IM) is a fundamental operation among graph problems that involve simulating a stochastic diffusion process on real-world networks. Given a graph G(V, E), the objective is to identify a small set of key influential "seeds"— i.e., a fixed-size set of k nodes, which when influenced is likely to lead to the maximum number of nodes in the network getting influenced. the problem has numerous applications including (but not limited to) viral marketing in social networks, epidemic control in contact networks, and in finding influential proteins in molecular networks. Despite its importance, application of influence maximization at scale continues to pose significant challenges. While the problem is NP-hard, efficient approximation algorithms that use greedy hill climbing are used in practice. However those algorithms consume hours of multithreaded execution time even on modest-sized inputs with hundreds of thousands of nodes. In this paper, we present IMpart, a partitioning-based approach to accelerate greedy hill climbing based IM approaches on both shared and distributed memory computers. In particular, we present two parallel algorithms— one that uses graph partitioning (IMpart-metis) and another that uses community-aware partitioning (IMpart-gratis)— with provable guarantees on the quality of approximation. Experimental results show that our approaches are able to deliver two to three orders of magnitude speedup over a state-of-the-art multithreaded hill climbing implementation with negligible loss in quality. For instance, on one of the modest-sized inputs (Slashdot: 73K nodes; 905K edges), our partitioning-based shared memory implementation yields 4610× speedup, reducing the runtime from 9h 36m to 7 seconds on 128 threads. Furthermore, our distributed memory implementation enhances problem size reach to graph inputs with ×10 6 nodes and ×10 8 edges and enables sub-minute computation of IM solutions.

关键词： Proteins Privacy Runtime Social networking (online) Instruction sets Pipelines Memory management

来源：评论

学校读者我要写书评

暂无评论

parallel Scheduling of Data-Intensive Tasks 26th

Parallel Scheduling of Data-Intensive Tasks

引用

26th international conference on parallel and Distributed computing (Euro-Par)

作者： Meng, Xiao Golab, Lukasz Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9783030576752;9783030576745

Workloads with precedence constraints due to data dependencies are common in various applications. these workloads can be represented as directed acyclic graphs (DAG), and are often data-intensive, meaning that data loading cost is the dominant factor and thus cache misses should be minimized We address the problem of parallel scheduling of a DAG of data-intensive tasks to minimize makespan. To do so, we propose greedy online scheduling algorithms that take load balancing, data dependencies, and data locality into account. Simulations and an experimental evaluation using an Apache Spark cluster demonstrate the advantages of our solutions.

关键词： parallel scheduling Data-intensive tasks Caching

来源：评论

学校读者我要写书评

暂无评论

Medical Image Data Upscaling with Generative Adversarial networks 8th

Medical Image Data Upscaling with Generative Adversarial Net...

引用

8th international Work-conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Dobrovolny, Michal Mls, Karel Krejcar, Ondrej Mambou, Sebastien Selamat, Ali Univ Hradec Kralove Fac Informat & Management Ctr Basic & Appl Res Hradec Kralove Czech Republic Univ Teknol Malaysia Malaysia Japan Int Inst Technol Jalan Sultan Yahya Petra Kuala Lumpur 54100 Malaysia Univ Teknol Malaysia Sch Comp Fac Engn Skudai 81310 Malaysia

ISBN: (纸本)9783030453848;9783030453855

Super-resolution is one of the frequently investigated methods of image processing. the quality of the results is a constant problem in the methods used to obtain high resolution images. Interpolation-based methods have blurry output problems, while non-interpolation methods require a lot of training data and high computing power. In this paper, we present a supervised generative adversarial network system that accurately generates high resolution images from a low resolution input while maintaining pathological invariance. the proposed solution is optimized for small sets of input data. Compared to existing models, our network also provides faster learning. Another advantage of our approach is its versatility for various types of medical imaging methods. We used peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as the output image quality evaluation method. the results of our test show an improvement of 5.76% compared to optimizer Adam used in the original paper [10]. For faster training of the neural network model, calculations on the graphic card with the CUDA architecture were used.

关键词： Super-resolution Deep neural network CUDA parallel processing Adam RMSprop

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118页 << < 22 23 24 25 26 27 28 29 30 31 > >>

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：