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

2,694 篇 会议
58 册 图书
53 篇 期刊文献

馆藏范围

2,805 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,844 篇 工学
- 1,629 篇 计算机科学与技术...
- 847 篇 软件工程
- 340 篇 电气工程
- 221 篇 电子科学与技术（可...
- 209 篇 信息与通信工程
- 84 篇 控制科学与工程
- 63 篇 光学工程
- 57 篇 机械工程
- 41 篇 仪器科学与技术
- 39 篇 生物医学工程（可授...
- 38 篇 生物工程
- 31 篇 材料科学与工程（可...
- 25 篇 动力工程及工程热...
- 21 篇 化学工程与技术
- 20 篇 建筑学
- 15 篇 土木工程
- 13 篇 力学（可授工学、理...
- 12 篇 交通运输工程
499 篇 理学
- 343 篇 数学
- 113 篇 物理学
- 51 篇 系统科学
- 48 篇 生物学
- 30 篇 统计学（可授理学、...
- 26 篇 化学
173 篇 管理学
- 119 篇 管理科学与工程(可...
- 62 篇 图书情报与档案管...
- 49 篇 工商管理
40 篇 医学
- 30 篇 临床医学
- 14 篇 基础医学(可授医学...
15 篇 法学
- 15 篇 社会学
9 篇 经济学
9 篇 农学
8 篇 文学
2 篇 军事学
1 篇 教育学

主题

363 篇 parallel process...
219 篇 computer archite...
205 篇 graphics process...
146 篇 parallel archite...
136 篇 graphics process...
129 篇 hardware
116 篇 parallel algorit...
112 篇 image processing
99 篇 computational mo...
94 篇 concurrent compu...
87 篇 instruction sets
86 篇 field programmab...
83 篇 algorithm design...
79 篇 multicore proces...
77 篇 signal processin...
76 篇 parallel process...
66 篇 parallel program...
60 篇 throughput
60 篇 gpu
59 篇 kernel

机构

11 篇 natl univ def te...
6 篇 college of compu...
6 篇 school of comput...
6 篇 hosei univ dept ...
6 篇 natl univ def te...
5 篇 univ aizu dept c...
5 篇 carleton univ sc...
5 篇 school of comput...
5 篇 computer science...
5 篇 inria rennes
5 篇 city university ...
4 篇 chinese acad sci...
4 篇 univ michigan ad...
4 篇 institute of com...
4 篇 univ chinese aca...
4 篇 school of comput...
4 篇 univ jaume 1 dep...
4 篇 hainan internati...
4 篇 tech univ cluj n...
4 篇 department of co...

作者

11 篇 jack dongarra
10 篇 roman wyrzykowsk...
9 篇 konrad karczewsk...
9 篇 quintana-orti en...
7 篇 dongarra jack
7 篇 kothapalli kisho...
6 篇 hannig frank
6 篇 liu jie
6 篇 su jinshu
6 篇 nakano koji
6 篇 peng shietung
6 篇 li yamin
6 篇 chu wanming
6 篇 wyrzykowski roma...
6 篇 thulasiraman par...
5 篇 ito yasuaki
5 篇 jerzy waśniewski
5 篇 wang guojun
5 篇 geyong min
5 篇 wanlei zhou

语言

2,744 篇 英文
36 篇 其他
18 篇 中文
11 篇 俄文
2 篇 乌克兰文
1 篇 西班牙文

检索条件"任意字段=10th International Conference on Algorithms and Architectures for Parallel Processing"

共 2805 条记录，以下是1361-1370 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：

Data Gathering and processing for Large-Scale Wireless Sensor Networks

Data Gathering and Processing for Large-Scale Wireless Senso...

引用

IEEE 9th international conference on Mobile Ad-hoc and Sensor Networks (MSN)

作者： Xing, Xiaofei Xie, Dongqing Wang, Guojun Guangzhou Univ Sch Comp Sci & Educ Software Guangzhou 510006 Guangdong Peoples R China Cent South Univ Technol Sch Informat Sci & Engn Changsha 410083 Peoples R China

ISBN: (纸本)9780769551593

Mass data are usually collected and processed in large and ultra large-scale wireless sensor networks, and this will greatly affect the life of Intelligent sensors and the performance of network. In this paper, we propose an approach to reduce the collected data from wireless sensor networks by using compressed sensing method. Compressed sensing is a new sampling method that the data sampling and compressing can be done simultaneously. Compressed sensing can significantly reduce the collected data size by lowering the sampling rates of sensors, but it is non-adaptive and its algorithm has high computational complexity as well. We put forward and achieved the parallel processing of compressed sensing algorithm for improving algorithms execution speed. Experiment results shows that the proposed scheme significantly outperforms existing solutions in terms of reconstruction accuracy.

关键词： Wireless sensor networks data reconstruction compressed sensing sparse approximation

来源：评论

学校读者我要写书评

暂无评论

Optimizations with CUDA: A Case Study on 3D Curve-Skeleton Extraction from Voxelized Models 1

引用

7th international Joint conference on Computer Vision, Imaging and Computer Graphics theory and Applications, VISIGRAPP 2012

作者： Jiménez, Jesús Ruiz de Miras, Juan Department of Computer Science University of Jaén Campus Las Lagunillas s/n 23071 Jaén Spain

ISBN: (数字)9783642382413

ISBN: (纸本)9783642382406

In this paper, we show how we have coded and optimized a complex and not trivially parallelizable case study: a 3D curve-skeleton calculation algorithm. For this we use NVIDIA CUDA, which allows the programmer to easily code algorithms for executing in a parallel way on NVIDIA GPU devices. However, when working with algorithms that have high data-sharing or data-dependence requirements, like the curve-skeleton calculation, it is not always a trivial task to achieve acceptable acceleration rates. So we detail step by step a comprehensive collection of optimizations to be considered in this class of algorithms, and in general in any CUDA implementation. Two different GPU architectures have been used to test the implications of each optimization, the NVIDIA GT200 architecture and the Fermi GF100. As a result, although the first direct CUDA implementation of our algorithm ran even slower than its CPU version, overall speedups of 19x (GT200) and 68x (Fermi GF100) were finally achieved. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures

Making a science of model search: Hyperparameter optimizatio...

引用

30th international conference on Machine Learning, ICML 2013

作者： Bergstra, J. Yamins, D. Cox, D.D. Rowland Institute at Harvard 100 Edwin H. Land Boulevard Cambridge MA 02142 United States Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA 02139 United States

ISBN: (纸本)9781629933061

Many computer vision algorithms depend on configuration settings that are typically hand-tuned in the course of evaluating the algorithm for a particular data set. While such parameter tuning is often presented as being incidental to the algorithm, correctly setting these parameter choices is frequently critical to realizing a method's full potential. Compounding matters, these parameters often must be re-tuned when the algorithm is applied to a new problem domain, and the tuning process itself often depends on personal experience and intuition in ways that are hard to quantify or describe. Since the performance of a given technique depends on both the fundamental quality of the algorithm and the details of its tuning, it is sometimes difficult to know whether a given technique is genuinely better, or simply better tuned. In this work, we propose a meta-modeling approach to support automated hyperparameter optimization, with the goal of providing practical tools that replace hand-tuning with a reproducible and unbiased optimization process. Our approach is to expose the underlying expression graph of how a performance metric (e.g. classification accuracy on validation examples) is computed from hyperparameters that govern not only how individual processing steps are applied, but even which processing steps are included. A hyperparameter optimization algorithm transforms this graph into a program for optimizing that performance metric. Our approach yields state of the art results on three disparate computer vision problems: a face-matching verification task (LFW), a face identification task (PubFig83) and an object recognition task (CIFAR-10), using a single broad class of feed-forward vision architectures. Copyright 2013 by the author(s).

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

GPS: A General Framework for parallel Queries over Data Streams in Cloud

GPS: A General Framework for Parallel Queries over Data Stre...

引用

IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Xiaoyong Li Yijie Wang Yu Zhao Yuan Wang Xiaoling Li Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Hunan P. R. China

parallel query processing over data streams in cloud computing environments has attracted considerable attention recently in various fields, due to the huge potential value of analyzing massive data or big data in a large number of streaming applications. Nevertheless, existing studies on queries primarily focus on the algorithms for the specific query types with the lack of the general framework for processing various queries. Moreover, existing parallel frameworks in cloud such as MapReduce and its variations are not suitable for many complex queries over complex data streams. In this paper, we extensively discuss the problem of designing the general framework for parallel queries over data streams in cloud. Particularly, we propose and implement a framework called GPS, which can be well adapted to various queries over complex data streams like the uncertain data streams. Furthermore, we further propose a hierarchical and general parallel model for queries over data streams based on the proposed framework, which is more flexible than the MapReduce model. the skyline queries over uncertain data streams based on our proposed framework with real deployment are conducted as an example to verify the performances of our proposals.

关键词： Peer-to-peer computing parallel processing Data models Query processing Global Positioning System Object oriented modeling Distributed databases

来源：评论

学校读者我要写书评

暂无评论

Optimization of memory accesses for CUDA architecture and image warping algorithms

Optimization of memory accesses for CUDA architecture and im...

引用

4th international conference on Image processing and Communications, IPC 2012

作者： Mazurek, Przemyslaw Department of Signal Processing and Multimedia Engineering West-Pomeranian University of Technology Szczecin 26. Kwietnia 10 Str. Szczecin 71126 Poland

ISBN: (纸本)9783642323836

the CUDA software platform gives abilities of outstanding performance for parallel computing using GPGPUs (General Purpose Graphics processing Unit). the external memory interface is the main bottleneck of GPGPU for memory intense operations. there are a lot of reduction ways of this disadvantage for real-time applications. the profiling of the algorithm and execution parameters fitting are presented as a solution for the minimization of execution time. the fisheye to perspective transform is optimized as the example of the nonlinear image warping algorithm. the code optimization using search of the optimal kernel starting parameters is necessary. Such optimization gives better results for all cases due to limited processing area and the execution time is about 12% smaller. the unconventional method for CUDA of block-to-image assignment is emphasized. © 2013 Springer-Verlag.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Two parallel Finite Element Implementations of the Time-Dependent Advection Diffusion Problem: GPU versus Cluster Considering Time and Energy Consumption 1

引用

10th international conference on High Performance Computing for Computational Science (VECPAR)

作者： De Souza, Alberto F. Veronese, Lucas Lima, Leonardo M. Badue, Claudine Catabriga, Lucia Univ Fed Espirito Santo Dept Informat Vitoria Brazil Ciencias & Tecnol Espirto Santo Inst Fed Educ Vitoria Brazil

ISBN: (数字)9783642387180

ISBN: (纸本)9783642387180;9783642387173

We analyze two parallel finite element implementations of the 2D time-dependent advection diffusion problem, one for multi-core clusters and one for CUDA-enabled GPUs, and compare their performances in terms of time and energy consumption. the parallel CUDA-enabled GPU implementation was derived from the multi-core cluster version. Our experimental results show that a desktop machine with a single CUDA-enabled GPU can achieve performance higher than a 24-machine (96 cores) cluster in this class of finite element problems. Also, the CUDA-enabled GPU implementation consumes less than one twentieth of the energy (Joules) consumed by the multi-core cluster implementation while solving a whole instance of the finite element problem.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

parallelization of discrete stochastic arithmetic on multicore architectures

Parallelization of discrete stochastic arithmetic on multico...

引用

international conference on Information Technology: New Generations (ITNG)

作者： F. Jézéquel J.-L. Lamotte O. Chubach UPMC Univ Paris 06 Laboratoire d'Informatique de Paris 6 France Odessa I.I.Mechnikov National University Odessa Ukraine

ISBN: (纸本)9781467359603

Discrete Stochastic Arithmetic (DSA) estimates round-off error propagation in a program. It is based on a synchronous execution of several instances of the program to control using a random rounding mode. In this paper we show how we can take advantage of multicore processors, which are nowadays widespread, to reduce the cost of DSA in terms of execution time. Several processes execute in parallel different instances of the program and exchange data when necessary. Several strategies are compared for the estimation of the result accuracy and the detection of numerical instabilities. With our parallel implementation, the cost of DSA is reduced by a factor of about 2 compared with the sequential approach. Our parallel implementation of DSA has been used successfully for the numerical validation of a real-life application.

关键词： Accuracy Libraries Jacobian matrices Multicore processing Estimation Program processors Random variables

来源：评论

学校读者我要写书评

暂无评论

DTW-Based Subsequence Similarity Search on AMD Heterogeneous Computing Platform

DTW-Based Subsequence Similarity Search on AMD Heterogeneous...

引用

IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Sitao Huang Guohao Dai Yuliang Sun Zilong Wang Yu Wang Huazhong Yang E.E. Dept. TNLIST Tsinghua University

Subsequence similarity search is one of the most common subroutines in time series data mining algorithms. According to previous studies, Dynamic Time Warping (DTW) distance is the best distance measurement in many domains. However, the high computational complexity of DTW distance makes it a critical bottleneck in many subsequence similarity search applications. In some applications, the performance of software implementation still could not meet the high requirements of applications. Under the circumstance, some hardware implementations of DTW-based algorithms were proposed in the data mining community, using GPUs and FPGAs. In this paper, we propose a full system implementation for subsequence similarity search on AMD heterogeneous computing platform, including complete normalization pre-processing, two kinds of improved lower bound for pruning, and a novel segmented parallel DTW calculation process, which efficiently utilizes the capacity of CPU and GPU on the platform. Our work achieves one to two orders of magnitude speedup compared to software implementation and several times speedup to other GPU or FPGA implementations.

关键词： Time series analysis Acceleration parallel processing Field programmable gate arrays Graphics processing units Hardware Algorithm design and analysis

来源：评论

学校读者我要写书评

暂无评论

An Efficient Graph Isomorphism Algorithm Based on Canonical Labeling and Its parallel Implementation on GPU

An Efficient Graph Isomorphism Algorithm Based on Canonical ...

引用

IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Renda Wang Longjiang Guo Chunyu Ai Jinbao Li Meirui Ren Keqin Li School of Computer Science and Technology Heilongjiang University Harbin Heilongjiang China Key Laboratory of Database and Parallel Computing Harbin Heilongjiang China Department of Computer Science University of South Carolina Upstate Spartanburg SC USA Department of Computer Science State University of New York New Paltz New York USA

the Graph Isomorphism (GI) problem has been extensively studied due to its significant applications. the most effective class of GI algorithms, i.e., canonical labeling algorithms, are suitable for either graphs with high randomness or symmetry, or graphs for which both of them are not strongly held. Also, the core operations of canonical labeling algorithms, i.e., individuation-refinement (IR) and certificate comparison, usually occupy more than 70% of the total running time. How to weaken the limitations of structures and improve the efficiency of these operations are challenges. In this paper, we present an efficient GI algorithm called PEACE, which is particularly suitable for graphs with high randomness or symmetry. We present a parallel implementation of our algorithm on GPUs. We design some new techniques and also use some existing methods to speed up calculations under CUDA. More importantly, these techniques can be applied to all IR-based GI algorithms. We evaluate the proposed algorithm on various graphs to make comprehensive comparison with currently the most efficient canonical labeling algorithms on CPUs. Experimental results show that PEACE is superior to other algorithms on graphs with high symmetry or many automorphisms, and up to 50% performance increase can be achieved in the best case. We also apply our parallel techniques on these algorithms, and compare the performance on CPU and multiple GPU devices. the results show that the techniques make all algorithms gain 15~55 speedup.

关键词： Labeling Graphics processing units Algorithm design and analysis Indexes Arrays Instruction sets Partitioning algorithms

来源：评论

学校读者我要写书评

暂无评论

Performance Bottlenecks in Manycore Systems: A Case Study on Large Scale Feature Matching within Image Collections

Performance Bottlenecks in Manycore Systems: A Case Study on...

引用

IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Xiaoxin Tang Steven Mills David Eyers Kai-Cheung Leung Zhiyi Huang Minyi Guo Department of Computer Science Shanghai Jiao Tong University China Department of Computer Science University of Otago New Zealand

In memory-intensive algorithms, the problem size is often so large that it cannot fit into the cache of a CPU, and this may result in an excessive number of cache misses, a bottleneck that can easily make seemingly embarrassingly-parallel algorithms such as feature-matching unscalable in many core systems. To solve this bottleneck, this paper proposes a general Divide-and-Merge methodology, which divides the feature space into several small sub-spaces, so that the shared resources in each sub-space can be satisfied without causing bottlenecks. Experimental results have shown that the Divide-and-Merge methodology reduces the L3 cache misses and time spent on memory-allocation-related system calls, resulting in a 211% performance improvement on an AMD 64-core CPU machine, and 57% and 16% performance improvements on AMD and Intel 16-core machines respectively. Performance results on a modern GPU also show that a well-tuned algorithm with time complexity of O(F^2) is able to defeat a state-of-the-art O(F^1.5) algorithm by 188% for our real-world dataset, which again highlights the huge performance impact of the memory system.

关键词： Approximation algorithms Graphics processing units Time complexity Random access memory Libraries parallel processing Indexes

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281页 << < 133 134 135 136 137 138 139 140 141 142 > >>

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：