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

24 篇 期刊文献
14 篇 会议

馆藏范围

38 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

34 篇 工学
- 26 篇 计算机科学与技术...
- 13 篇 电气工程
- 7 篇 信息与通信工程
- 4 篇 控制科学与工程
- 2 篇 仪器科学与技术
- 2 篇 电子科学与技术（可...
- 2 篇 软件工程
- 2 篇 网络空间安全
8 篇 理学
- 6 篇 数学
- 1 篇 大气科学
- 1 篇 地球物理学
- 1 篇 统计学（可授理学、...
3 篇 管理学
- 2 篇 管理科学与工程(可...
- 1 篇 图书情报与档案管...
1 篇 经济学
- 1 篇 应用经济学

主题

38 篇 parallel and dis...
5 篇 federated learni...
3 篇 outlier detectio...
3 篇 deep learning
3 篇 scheduling polic...
3 篇 distance-based o...
3 篇 optimization
3 篇 machine learning
3 篇 high performance...
3 篇 randomized algor...
2 篇 matrix factoriza...
2 篇 collaborative fi...
2 篇 quantum computin...
2 篇 load balancing
2 篇 wireless communi...
2 篇 matrix completio...
2 篇 convergence
2 篇 divide-and-conqu...
2 篇 robust matrix fa...
2 篇 large-scale mach...

机构

2 篇 forschungszentru...
2 篇 univ calabria di...
2 篇 univ bologna dep...
2 篇 univ calif berke...
2 篇 italian natl res...
1 篇 univ iceland dep...
1 篇 singapore univ t...
1 篇 princeton univ d...
1 篇 google res mount...
1 篇 college of telec...
1 篇 kyushu univ int ...
1 篇 unidade de ciênc...
1 篇 auburn univ mont...
1 篇 kyoto univ grad ...
1 篇 boise state univ...
1 篇 italian natl res...
1 篇 department of el...
1 篇 college of portl...
1 篇 natl univ singap...
1 篇 univ miami coope...

作者

4 篇 angiulli fabrizi...
3 篇 basta stefano
3 篇 lodi stefano
3 篇 sartori claudio
2 篇 michael i. jorda...
2 篇 pang guohao
2 篇 zhu xiaorong
2 篇 jordan michael i...
2 篇 cavallaro gabrie...
2 篇 folino gianluigi
1 篇 liu zuozhu
1 篇 barakat chadi
1 篇 mehrpouyan hani
1 篇 sauerwald thomas
1 篇 fard arash
1 篇 he h.
1 篇 sun he
1 篇 feng zhengkun
1 篇 miller john a.
1 篇 singh pankaj

语言

37 篇 英文
1 篇 其他

检索条件"主题词=Parallel and distributed algorithms"

共 38 条记录，以下是21-30 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：

Multi-Stream LDPC Decoder on GPU of Mobile Devices 9

Multi-Stream LDPC Decoder on GPU of Mobile Devices

引用

9th IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Amiri, Roohollah Mehrpouyan, Hani Boise State Univ Elect & Comp Engn Boise ID 83725 USA

ISBN: (纸本)9781728105543

Low-density parity check (LDPC) codes have been extensively applied in mobile communication systems due to their excellent error correcting capabilities. However, their broad adoption has been hindered by the high complexity of the LDPC decoder. Although to date, dedicated hardware has been used to implement low latency LDPC decoders, recent advancements in the architecture of mobile processors have made it possible to develop software solutions. In this paper, we propose a multi-stream LDPC decoder designed for a mobile device. The proposed decoder uses graphics processing unit (GPU) of a mobile device to achieve efficient real-time decoding. The proposed solution is implemented on an NVIDIA Tegra board as a system on a chip (SoC), where our results indicate that we can control the load on the central processing units through the multi-stream structure.

关键词： parallel and distributed algorithms Multiprocessor Architectures LDPC Decoder GPU Processing

来源：评论

学校读者我要写书评

暂无评论

parallel Streaming Signature EM-tree: A Clustering Algorithm for Web Scale Applications 15

Parallel Streaming Signature EM-tree: A Clustering Algorithm...

引用

24th International Conference on World Wide Web (WWW)

作者： De Vries, Christopher M. De Vine, Lance Geva, Shlomo Nayak, Richi Game Analyt ApS Berlin Germany Queensland Univ Technol Brisbane Qld Australia

ISBN: (纸本)9781450334693

The proliferation of the web presents an unsolved problem of automatically analyzing billions of pages of natural language. We introduce a scalable algorithm that clusters hundreds of millions of web pages into hundreds of thousands of clusters. It does this on a single mid-range machine using efficient algorithms and compressed document representations. It is applied to two web-scale crawls covering tens of terabytes. ClueWeb09 and ClueWeb12 contain 500 and 733 million web pages and were clustered into 500,000 to 700,000 clusters. To the best of our knowledge, such fine grained clustering has not been previously demonstrated. Previous approaches clustered a sample that limits the maximum number of discoverable clusters. The proposed EM-tree algorithm uses the entire collection in clustering and produces several orders of magnitude more clusters than the existing algorithms. Fine grained clustering is necessary for meaningful clustering in massive collections where the number of distinct topics grows linearly with collection size. These fine-grained clusters show an improved cluster quality when assessed with two novel evaluations using ad hoc search relevance judgments and spam classifications for external validation. These evaluations solve the problem of assessing the quality of clusters where categorical labeling is unavailable and unfeasible.

关键词： Document Clustering Large Scale Learning parallel and distributed algorithms Random Projections Hashing Document Signatures Search Tree Compressed Learning

来源：评论

学校读者我要写书评

暂无评论

Scalable architecture for allocation of idle CPUs in a P2P network

引用

2nd International Conference on High Performance Computing and Communications (HPCC 2006)

作者： Celaya, Javier Arronategui, Unai Univ Zaragoza Dept Comp Sci & Syst Engn Zaragoza 50018 Spain

ISBN: (纸本)3540393684

In this paper we present a scalable, distributed architecture that allocates idle CPUs for task execution, where any node may request the execution of a group of tasks by other ones. A fast, scalable discovery protocol is an essential component. Also, up to date information about free nodes is efficiently managed in each node by an availability protocol. Both protocols exploit a tree-based peer-to-peer network that adds fault-tolerant capabilities. Results from experiments and simulation tests, using a simple allocation method, show discovery and allocation costs scaling logarithmically with the number of nodes, even with low communication overhead and little, bounded state in each node.

关键词： parallel and distributed architectures networking protocols and routing and algorithms reliability and fault-tolerance grid computing peer-to-peer computing parallel and distributed algorithms

来源：评论

学校读者我要写书评

暂无评论

Practice and Experience in using parallel and Scalable Machine Learning with Heterogenous Modular Supercomputing Architectures

Practice and Experience in using Parallel and Scalable Machi...

引用

35th IEEE International parallel and distributed Processing Symposium (IPDPS)

作者： Riedel, Morris Sedona, Rocco Barakat, Chadi Einarsson, Petur Hassanian, Reza Cavallaro, Gabriele Book, Matthias Neukirchen, Helmut Lintermann, Andreas Univ Iceland Dept Comp Sci Reykjavik Iceland Forschungszentrum Julich Julich Supercomp Ctr Julich Germany

ISBN: (纸本)9781665435772

We observe a continuously increased use of Deep Learning (DL) as a specific type of Machine Learning (ML) for data-intensive problems (i.e., 'big data') that requires powerful computing resources with equally increasing performance. Consequently, innovative heterogeneous High-Performance Computing (HPC) systems based on multi-core CPUs and many-core GPUs require an architectural design that addresses end user communities' requirements that take advantage of ML and DL. Still the workloads of end user communities of the simulation sciences (e.g., using numerical methods based on known physical laws) needs to be equally supported in those architectures. This paper offers insights into the Modular Supercomputer Architecture (MSA) developed in the Dynamic Exascale Entry Platform (DEEP) series of projects to address the requirements of both simulation sciences and data-intensive sciences such as High Performance Data Analytics (HPDA). It shares insights into implementing the MSA in the Julich Supercomputing Centre (JSC) hosting Europe No. 1 Supercomputer Julich Wizard for European Leadership Science (JUWELS). We augment the technical findings with experience and lessons learned from two application communities case studies (i.e., remote sensing and health sciences) using the MSA with JUWELS and the DEEP systems in practice. Thus, the paper provides details into specific MSA design elements that enable significant performance improvements of ML and DL algorithms. While this paper focuses on MSA-based HPC systems and application experience, we are not losing sight of advances in Cloud Computing (CC) and Quantum Computing (QC) relevant for ML and DL.

关键词： High performance computing cloud computing quantum computing machine learning deep learning parallel and distributed algorithms remote sensing health sciences modular supercomputer architecture

来源：评论

学校读者我要写书评

暂无评论

Tight Bounds for Randomized Load Balancing on Arbitrary Network Topologies

Tight Bounds for Randomized Load Balancing on Arbitrary Netw...

引用

IEEE 53rd Annual Symposium on Foundations of Computer Science (FOCS)

作者： Sauerwald, Thomas Sun, He Max Planck Inst Informat D-66123 Saarbrucken Germany

ISBN: (纸本)9781467343831

We consider the problem of balancing load items (tokens) on networks. Starting with an arbitrary load distribution, we allow in each round nodes to exchange tokens with their neighbors. The goal is to achieve a distribution where all nodes have nearly the same number of tokens. For the continuous case where tokens are arbitrarily divisible, most load balancing schemes correspond to Markov chains whose convergence is fairly well-understood in terms of their spectral gap. However, in many applications load items cannot be divided arbitrarily and we need to deal with the discrete case where the load is composed of indivisible tokens. This discretization entails a non-linear behavior due to its rounding errors, which makes the analysis much harder than in the continuous case. Therefore, it has been a major open problem to understand the limitations of discrete load balancing and its relation to the continuous case. We investigate several randomized protocols for different communication models in the discrete case. Our results demonstrate that there is almost no difference between the discrete and continuous case. For instance, for any regular network in the matching model, all nodes have the same load up to an additive constant in (asymptotically) the same number of rounds required in the continuous case. This generalizes and tightens the previous best result, which only holds for expander graphs.

关键词： randomized algorithms parallel and distributed algorithms graph expansion Markov chains load balancing

来源：评论

学校读者我要写书评

暂无评论

A Framework for parallelizing Hierarchical Clustering Methods

A Framework for Parallelizing Hierarchical Clustering Method...

引用

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Lattanzi, Silvio Lavastida, Thomas Lu, Kefu Moseley, Benjamin Google Zurich Zurich Switzerland Carnegie Mellon Univ Tepper Sch Business Pittsburgh PA 15213 USA Washington & Lee Univ Comp Sci Dept Lexington VA 24450 USA

ISBN: (纸本)9783030461508;9783030461492

Hierarchical clustering is a fundamental tool in data mining, machine learning and statistics. Popular hierarchical clustering algorithms include top-down divisive approaches such as bisecting k-means, k-median, and k-center and bottom-up agglomerative approaches such as single-linkage, average-linkage, and centroid-linkage. Unfortunately, only a few scalable hierarchical clustering algorithms are known, mostly based on the single-linkage algorithm. So, as datasets increase in size every day, there is a pressing need to scale other popular methods. We introduce efficient distributed algorithms for bisecting k-means, k-median, and k-center as well as centroid-linkage. In particular, we first formalize a notion of closeness for a hierarchical clustering algorithm, and then we use this notion to design new scalable distributed methods with strong worst case bounds on the running time and the quality of the solutions. Finally, we show experimentally that the introduced algorithms are efficient and close to their sequential variants in practice.

关键词： Hierarchical clustering parallel and distributed algorithms Clustering Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Evaluation-Time Bias in Quasi-Generational and Steady-State Asynchronous Evolutionary algorithms 16

Evaluation-Time Bias in Quasi-Generational and Steady-State ...

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Scott, Eric O. De Jong, Kenneth A. George Mason Univ Dept Comp Sci Fairfax VA 22030 USA

ISBN: (纸本)9781450342063

A number of papers have emerged in the last two years that apply and study asynchronous master-slave evolutionary algorithms based on a steady-state model. These efforts are largely motivated by the observation that, unlike traditional (synchronous) EAs, asynchronous EAs are able to make maximal use of many parallel processors, even when some individuals evaluate more slowly than others. Asynchronous EAs do not behave the same as their synchronous counterparts, however, and as of yet there is very little theory that makes it possible to predict how they will perform on new problems. Of some concern is evidence suggesting that the steady-state versions tend to be biased toward regions of the search space where fitness evaluation is cheaper. This has led some authors to suggest a so-called 'quasi-generational' asynchronous EA as an intermediate solution that incurs neither idle time nor significant bias toward fast solutions. We perform experiments with the quasi-generational EA, and show that it does not deliver the promised bene fits: it is, in fact, just as biased toward fast solutions as the steady-state approach is, and it tends to converge even more slowly than the traditional, generational EA.

关键词： Evolutionary algorithms parallel and distributed algorithms Asynchronous algorithms

来源：评论

学校读者我要写书评

暂无评论

Improving the Scalability of a Hurricane Forecast System in Mixed-parallel Environments Advancing the WRF framework toward faster and more accurate forecasts 16

Improving the Scalability of a Hurricane Forecast System in ...

引用

16th IEEE Int Conf on High Performance Computing and Communications/11th IEEE Int Conf on Embedded Software and Systems\6th Int Symposium on Cyberspace Safety and Security

作者： Quirino, Thiago Santos Delgado, Javier Zhang, Xuejin NOAA Hurricane Res Div US DOC OARAOMLHRD Miami FL 33165 USA Univ Miami Cooperat Inst Marine & Atmospher Studies Miami FL USA

ISBN: (纸本)9781479961238

The Hurricane Weather Research and Forecasting (HWRF) model is one of the premier models in NOAA's operational suite of severe weather forecasting systems. An axiom in numerical weather prediction suggests that modeling the environment at high resolution optimizes forecast accuracy. However, due to operational time constraints, only the region immediately surrounding a single hurricane can be modeled in high resolution. Currently, this is achieved by embedding a relatively small high resolution, storm-following pair of grids within a larger and coarser grid. In a previous work, we extended HWRF to support multiple such independent storm-following pair of grids. The result was improved forecast accuracy by virtue of modeling storm-to-storm interactions in high resolution. However, some shortcomings in the underlying WRF framework cause these independent pairs of grids to be simulated sequentially. This limits the model's scalability and makes it impossible to harness this novel capability within the operational time constraints. In this paper, we address this issue by modifying the underlying WRF framework to simulate these independent pairs of storm-following grids in parallel. This is the first approach to be successfully implemented in the history of the WRF framework.

关键词： High-performance scientific and engineering computing HWRF WRF parallel and distributed algorithms

来源：评论

学校读者我要写书评

暂无评论

PRACTICE AND EXPERIENCE IN USING parallel AND SCALABLE MACHINE LEARNING IN REMOTE SENSING FROM HPC OVER CLOUD TO QUANTUM COMPUTING

PRACTICE AND EXPERIENCE IN USING PARALLEL AND SCALABLE MACHI...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Rieder, Morris Cavallaro, Gabriele Benediktsson, Jon Atli Univ Iceland Sch Engn & Nat Sci Reykjavik Iceland Forschungszentrum Julich Julich Supercomp Ctr Julich Germany

ISBN: (纸本)9781665403696

Using computationally efficient techniques for transforming the massive amount of Remote Sensing (RS) data into scientific understanding is critical for Earth science. The utilization of efficient techniques through innovative computing systems in RS applications has become more widespread in recent years. The continuously increased use of Deep Learning (DL) as a specific type of Machine Learning (ML) for data-intensive problems (i.e., 'big data') requires powerful computing resources with equally increasing performance. This paper reviews recent advances in High-Performance Computing (HPC), Cloud Computing (CC), and Quantum Computing (QC) applied to RS problems. It thus represents a snapshot of the state-of-the-art in ML in the context of the most recent developments in those computing areas, including our lessons learned over the last years. Our paper also includes some recent challenges and good experiences by using Europeans fastest supercomputer for hyper-spectral and multi-spectral image analysis with state-of-the-art data analysis tools. It offers a thoughtful perspective of the potential and emerging challenges of applying innovative computing paradigms to RS problems.

关键词： High performance computing cloud computing quantum computing machine learning deep learning parallel and distributed algorithms remote sensing

来源：评论

学校读者我要写书评

暂无评论

Iterated Vector Fields and Conservatism, with Applications to Federated Learning 33

Iterated Vector Fields and Conservatism, with Applications t...

引用

33rd International Conference on Algorithmic Learning Theory (ALT)

作者： Charles, Zachary Rush, Keith Google Res Mountain View CA 94043 USA

We study whether iterated vector fields (vector fields composed with themselves) are conservative. We give explicit examples of vector fields for which this self-composition preserves conservatism. Notably, this includes gradient vector fields of loss functions associated to some generalized linear models. In the context of federated learning, we show that when clients have loss functions whose gradient satisfies this condition, federated averaging is equivalent to gradient descent on a surrogate loss function. We leverage this to derive novel convergence results for federated learning. By contrast, we demonstrate that when the client losses violate this property, federated averaging can yield behavior which is fundamentally distinct from centralized optimization. Finally, we discuss theoretical and practical questions our analytical framework raises for federated learning.

关键词： Optimization federated learning dynamical systems parallel and distributed algorithms

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4页 << < 1 2 3 4 > >>

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：