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

10,911 篇 会议
230 篇 期刊文献
173 册 图书
3 篇 学位论文

馆藏范围

11,317 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

6,677 篇 工学
- 6,107 篇 计算机科学与技术...
- 2,914 篇 软件工程
- 1,181 篇 电气工程
- 1,067 篇 信息与通信工程
- 482 篇 电子科学与技术（可...
- 358 篇 控制科学与工程
- 176 篇 仪器科学与技术
- 152 篇 机械工程
- 117 篇 动力工程及工程热...
- 113 篇 生物医学工程（可授...
- 101 篇 生物工程
- 87 篇 光学工程
- 86 篇 建筑学
- 68 篇 土木工程
- 68 篇 网络空间安全
- 65 篇 化学工程与技术
- 61 篇 材料科学与工程（可...
- 58 篇 安全科学与工程
1,468 篇 理学
- 976 篇 数学
- 311 篇 物理学
- 156 篇 系统科学
- 140 篇 统计学（可授理学、...
- 136 篇 生物学
- 83 篇 化学
897 篇 管理学
- 647 篇 管理科学与工程(可...
- 306 篇 图书情报与档案管...
- 289 篇 工商管理
145 篇 医学
- 120 篇 临床医学
- 62 篇 基础医学(可授医学...
62 篇 经济学
- 62 篇 应用经济学
56 篇 法学
34 篇 农学
26 篇 教育学
18 篇 文学
11 篇 军事学

主题

1,463 篇 parallel process...
584 篇 computer archite...
553 篇 distributed comp...
534 篇 application soft...
530 篇 computational mo...
528 篇 concurrent compu...
342 篇 hardware
319 篇 graphics process...
309 篇 scalability
307 篇 parallel program...
305 篇 graphics process...
304 篇 computer science
285 篇 runtime
263 篇 big data
261 篇 optimization
249 篇 parallel process...
226 篇 throughput
225 篇 processor schedu...
214 篇 resource managem...
209 篇 bandwidth

机构

31 篇 national laborat...
23 篇 science and tech...
21 篇 department of co...
19 篇 national laborat...
18 篇 university of ch...
17 篇 school of comput...
17 篇 institute of com...
16 篇 school of comput...
15 篇 college of compu...
15 篇 georgia inst tec...
15 篇 barcelona superc...
15 篇 intel corporatio...
14 篇 school of comput...
14 篇 chinese acad sci...
14 篇 department of co...
13 篇 tsinghua univ de...
13 篇 ibm thomas j. wa...
13 篇 cent s univ sch ...
13 篇 college of compu...
13 篇 ohio state univ ...

作者

21 篇 jack dongarra
16 篇 badia rosa m.
16 篇 liu jie
15 篇 wang guojun
15 篇 yijie wang
15 篇 a. choudhary
14 篇 anon
14 篇 kurt rothermel
13 篇 jun wang
13 篇 koldehofe boris
13 篇 mencagli gabriel...
13 篇 prodan radu
13 篇 wang yijie
12 篇 fahringer thomas
12 篇 dongsheng li
12 篇 zomaya albert y.
12 篇 navaux philippe ...
12 篇 yong dou
11 篇 fernandes luiz g...
11 篇 guangwen yang

语言

11,188 篇 英文
95 篇 其他
35 篇 中文
2 篇 俄文
2 篇 土耳其文
1 篇 德文

检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

共 11317 条记录，以下是941-950 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：

Precision Batching: Bitserial Decomposition for Efficient Neural Network Inference on GPUs 30

Precision Batching: Bitserial Decomposition for Efficient Ne...

引用

30th international conference on parallel Architectures and Compilation techniques (PACT)

作者： Lam, Maximilian Yedidia, Zachary Banbury, Colby R. Reddi, Vijay Janapa Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9781665442787

We present PrecisionBatching, a quantized inference algorithm for speeding up neural network inference on traditional hardware platforms at low bitwidths. PrecisionBatching is based on the following insights: 1) neural network inference with low batch sizes on traditional hardware architectures (e.g: GPUs) is memory bound, 2) activation precision is critical to improving quantized model quality and 3) matrix-vector multiplication can be decomposed into binary matrix-matrix multiplications, enabling quantized inference with higher precision activations at the cost of more arithmetic operations. Combining these three insights, PrecisionBatching enables inference at extreme quantization levels (< 8 bits) by shifting a memory bound problem to a compute bound problem and achieves higher compute efficiency and runtime speedup at fixed accuracy thresholds against standard quantized inference methods. Across a variety of applications (MNIST, language modeling, natural language inference, reinforcement learning) and neural network architectures (fully connected, RNN, LSTM), PrecisionBatching yields end-to-end speedups of over 8x on a GPU within a < 1 - 5% error margin of the full precision baseline, outperforming traditional 8-bit quantized inference by over 1.5 x -2x at the same error tolerance.

关键词： Recurrent neural networks Runtime Computational modeling Natural languages Graphics processing units Reinforcement learning Hardware

来源：评论

学校读者我要写书评

暂无评论

Enhancing 6G Network Performance by Reducing Signaling Load via distributed Open RAN and SDN

Enhancing 6G Network Performance by Reducing Signaling Load ...

引用

international conference on Robotics,Electrical and Signal processing techniques (ICREST)

作者： Sally. R. Aldaeabool Wesam H. Al-Zubaedi M. F. Abbod H. S. Al-Raweshidy Dep. of Electronic and Computer Engineering Brunel University London Dep. of Biomedical Engineering University of Kerbala Collage of Engineering

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

The exponential expansion of mobile communication traffic, fuelled by a rising number of smart devices and sophisticated applications, has put significant pressure on existing cellular network architectures. The 6G networks need creative ways to meet these difficulties effectively. This study offers a distributed Open Radio Access Network (DO-RAN) architecture that uses Open-RAN and Software-Defined Networking (SDN) to reduce signalling burden and boost network performance. DO-RAN improves responsiveness, reduces signalling load on the 5GC, and reduces backhaul congestion by decentralising key functionalities such as the Access and Mobility Management Function (AMF) and the Session Management Function (SMF) from the 5G Core Network (5GC) to the Open-RAN framework's Central Units (CUs). Our proposal compares DO-RAN to Central Open RAN (CO-RAN) and other architectures, such as 4G LTE networks. The findings show that DO-RAN considerably reduces signalling load in a variety of situations, such as network- and user-triggered service events. This architecture provides enhanced efficiency and scalability for 6G cellular networks in addition to meeting the growing demands for signalling.

关键词： 6G mobile communication Cellular networks 5G mobile communication Scalability Open RAN Signal processing Copper Software defined networking Smart devices Long Term Evolution

来源：评论

学校读者我要写书评

暂无评论

BB-ML: Basic Block Performance Prediction using Machine Learning techniques

BB-ML: Basic Block Performance Prediction using Machine Lear...

引用

international conference on parallel and distributed Systems (ICPADS)

作者： Hamdy Abdelkhalik Shamminuj Aktar Yehia Arafa Atanu Barai Gopinath Chennupati Nandakishore Santhi Nishant Panda Nirmal Prajapati Nazmul Haque Turja Stephan Eidenbenz Abdel-Hameed A. Badawy Klipsch School of ECE New Mexico State University Las Cruces NM USA Qualcomm Inc USA Intel Corporation USA Amazon Inc USA Los Alamos National Laboratory Los Alamos NM USA

Recent years have seen the adoption of Machine Learning (ML) techniques to predict the performance of large-scale applications, mostly at a coarse level. In contrast, we propose to use ML techniques for performance prediction at a much finer granularity, namely at the Basic Block (BB) level, which are single entry, single exit code blocks that are used for analysis by the compilers to break down a large code into manageable pieces. Utilizing ML and BB analysis together can enable scalable hardware-software co-design beyond the current state of the art. In this work, we extrapolate the basic block execution counts of GPU applications and use it for predicting the performance for large input sizes from the counts of smaller input *** trained a Poisson Neural Network (PNN) model using random input values as well as the lowest input values of the application to learn the relationship between inputs and basic block counts. Experimental results show that the model can accurately predict the basic block execution counts of 16 GPU benchmarks. We achieved an accuracy of 93.5% for extrapolating the basic block counts for large input sets when the model is trained using smaller input sets. Additionally, the model shows an accuracy of 97.7% for predicting basic block counts on random instances. In a significant case study, we applied the ML model to CUDA GPU benchmarks for performance prediction across a spectrum of applications, spanning linear algebra to machine learning benchmarks. We employed a diverse set of metrics for evaluation, including global memory requests, tensor cores’ active cycles, and the active cycles of ALU and FMA units. The results from the case study demonstrate that the model is capable of predicting the performance of large datasets with high accuracy. For example, The average error rates for global and shared memory requests are 0.85% and 0.17%, respectively. Furthermore, to address the utilization of the main functional units in Ampere architectu

关键词：

来源：评论

学校读者我要写书评

暂无评论

Exploring HW/SW Co-Optimizations for Accelerating Large-scale Texture Identification on distributed GPUs 21

Exploring HW/SW Co-Optimizations for Accelerating Large-scal...

引用

50th international conference on parallel processing (ICPP)

作者： Wang, Junsong Zhang, Xiaofan Li, Yubo Lin, Yonghua V Origin Technol Beijing Peoples R China Univ Illinois Urbana IL USA

ISBN: (纸本)9781450390682

Texture identification has been developed recently to support oneto-one verification and one-to-many search, which provides much broader support than texture classification in real-life applications. It has demonstrated great potentials to enable product traceability by identifying the unique texture information on the surface of the targeted objects. However, existing hardware acceleration schemes are not enough to support a large-scale texture identification, especially for the search task, where the number of texture images being searched can reach millions, creating enormous compute and memory demands and making real-time texture identification infeasible. To address these problems, we propose a comprehensive toolset with jointly optimization strategies from both hardware and software to deliver optimized GPU acceleration and leverage large-scale texture identification with real-time responses. Novel technologies include: 1) a highly-optimized cuBLAS implementation for efficiently running 2-nearest neighbors algorithm;2) a hybrid cache design to incorporate host memory for streaming data toward GPUs, which delivers a 5x larger memory capacity while running the targeted workloads;3) a batch process to fully exploit the data reuse opportunities by considering available compute resources and memory bandwidth constraints. 4) an asymmetric local feature extraction to reduce the memory footprint for keeping feature matrices of reference texture images. To the best of our knowledge, this work is the first implementation to provide realtime large-scale texture identification on GPUs. By exploring the co-optimizations from both hardware and software, we can deliver 31x faster search and 20x larger feature cache capacity compared to a conventional CUDA implementation. We also demonstrate our proposed designs by proposing a distributed texture identification system with 14 Nvidia Tesla P100 GPUs which can complete 872,984 texture similarity comparisons in just one second.

关键词： texture identification GPU acceleration cuBLAS batching hybrid cache nearest neighbor feature extraction SIFT

来源：评论

学校读者我要写书评

暂无评论

A Cloud-based IoT-enabled framework for BCI applications

A Cloud-based IoT-enabled framework for BCI applications

引用

international conference on Internet of Things and applications (IoT)

作者： Ali Baghban Baghestan Zeinab Zali Farzaneh Shayegh Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

The interplay of brain signals and the Internet of Things (IoT) is an emerging area. The processing of electroencephalographic (EEG) signals is mainly divided into two categories: (1) signal analysis for diagnosis and (2) brain-computer interfaces that can control devices through brain signals. Some applications of EEG are early detection of seizures, wheelchair control, and smart homes. processing EEG is a complex task that requires expertise in fields such as neuroscience and computer science. There are frameworks to facilitate EEG processing, such as MNE-Python. Utilizing distributed and parallel processing is crucial in dealing with large-scale EEG data or when requiring scalable models. We have looked into platforms like WeBrain and a Spark-based framework to understand their capabilities for the issue. Based on our findings, we suggest a cloud framework. It supports the main features of previous works. It includes EEG processing capability using common machine learning (ML) and deep learning (DL) approaches based on the user’s required action paradigm. Since it is possible to run some trained models on limited resources devices in fog computing, an IoT device with caching capability is proposed to send and receive data. Collaboration and data sharing among users are made possible in this system.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Multi-Objective Hybrid parallel-Batch Job Scheduling in Edge Computing

A Multi-Objective Hybrid Parallel-Batch Job Scheduling in Ed...

引用

Emerging Smart Technologies and applications (eSmarTA), international conference on

作者： Khalid Al–Hussaini Tahani Hamoud Al–Najhi Information Technology Dept. Thamar University Dhamar Yemen Computer Science Dept. Thamar University Dhamar Yemen

Edge computing is an emerging technique for enhancing the computing experience on mobile devices by offloading computationally demanding jobs to the edge cloud. One of the critical scientific issues in edge computing is scheduling jobs on edge servers with restricted computational resources. In order to address this issue, the paper first suggests a parallel batch processing solution to multi-objective task scheduling using a hybrid P-BPM and PSO algorithm (P-MPSO) method that optimizes objectives while minimizing running costs and the amount of failed jobs running behind schedule in edge clouds. In order to maximize the effectiveness of our approach in light of the variable server performance and the various lags in the processing of jobs, this study then defines two parameters: the processing effectiveness of the server's performance by the P-BPM algorithm and the order of importance of the job by PSO algorithm. The simulation examined in this work were designed to compare the hybrid scheduling algorithms with several already-in-use scheduling techniques. Experiments demonstrate that the hybrid algorithms presented in this research are effective and dependable in lowering running costs and ensuring the scheduling jobs in edge clouds in a timely manner.

关键词：

来源：评论

学校读者我要写书评

暂无评论

21st IFIP WG 6.1 international conference on distributed applications and Interoperable Systems, DAIS 2021 held as part of 16th international Federated conference on distributed Computing techniques, DisCoTec 2021

21st IFIP WG 6.1 International Conference on Distributed App...

引用

21st IFIP WG 6.1 international conference on distributed applications and Interoperable Systems, DAIS 2021 held as part of 16th international Federated conference on distributed Computing techniques, DisCoTec 2021

ISBN: (纸本)9783030781972

The proceedings contain 10 papers. The special focus in this conference is on distributed applications and Interoperable Systems. The topics include: Simulation of Large Scale Computational Ecosystems with Alchemist: A Tutorial;network Federation for Inter-cloud Operations;specK: Composition of Stream processing applications over Fog Environments;ASPAS: As Secure as Possible Available Systems;portable Intermediate Representation for Efficient Big Data Analytics;shared-Dining: Broadcasting Secret Shares Using Dining-Cryptographers Groups;UCBFed: Using Reinforcement Learning Method to Tackle the Federated Optimization Problem;keVlar-Tz: A Secure Cache for Arm TrustZone: (Practical Experience Report);analysis and Improvement of Heterogeneous Hardware Support in Docker Images.

关键词：

来源：评论

学校读者我要写书评

暂无评论

HPC-in-Containers: A Containerized parallel Environment for parallel Programming Learning Using Docker

HPC-in-Containers: A Containerized Parallel Environment for ...

引用

international conference on Information Technology Systems and Innovation (ICITSI)

作者： Andria Arisal Heru Suhartanto Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia Faculty of Computer Science University of Indonesia Depok Indonesia

ISBN: (数字)9798331511470

ISBN: (纸本)9798331511487

This paper presents HPC-in-Containers, a novel containerized parallel computing environment using Docker. It is designed to facilitate learning parallel programming concepts, where users do not have to deploy a multicomputer infrastructure. Instead, they can use their desktop and laptop to easily have a multicomputer environment by utilizing containerization on the multi-core CPU. The environment offers a comprehensive platform for exploring various parallel programming paradigms, including distributed memory parallelism, shared memory parallelism, and hybrid approaches. We include source code samples for each paradigm to demonstrate its capabilities: parallel multicomputer implementations using MPI, parallel thread implementations for single computers using OpenMP, and hybrid parallelization techniques combining both MPI and OpenMP. Furthermore, to enhance user interaction and ease of use, the Docker image incorporates a Jupyter Notebook interface with a C interpreter, allowing users to write, execute, and visualize parallel code through a web browser. This feature makes HPC-in-Containers particularly suitable for educational settings, enabling students to experiment with parallel programming concepts in a controlled, reproducible environment. Our work contributes to the field of parallel computing education by providing a flexible, scalable, and easily deployable platform that bridges the gap between theoretical concepts and practical implementation. It offers a valuable resource for educators, students, and researchers seeking to explore and master the intricacies of parallel programming in a containerized setting.

关键词： Visualization Technological innovation Portable computers parallel programming Source coding Instruction sets Education parallel processing User experience Monitoring

来源：评论

学校读者我要写书评

暂无评论

Layer-wise Top-$k$ Gradient Sparsification for distributed Deep Learning

Layer-wise Top-$k$ Gradient Sparsification for Distributed D...

引用

Neural Networks, Information and Communication Engineering (NNICE), international conference on

作者： Guangyao Li Institute of Automation Chinese Academy of Sciences Beijing China

distributed training is widely used in training large-scale deep learning models, and data parallelism is one of the dominant approaches. Data-parallel training has additional communication overhead, which might be the bottleneck of training system. Top- $\boldsymbol{k}$ sparsification is a successful technique to reduce the communication volume to break the bottleneck. However, top- $\boldsymbol{k}$ sparsification cannot be executed until backpropagation is completed, which disables the overlap of backpropagation computations and gradient communications, leading to limiting the system scaling efficiency. In this paper, we propose a new distributed optimization approach named LKGS-SGD, which combines synchronous SGD (S-SGD) with a novel layer-wise top- $\boldsymbol{k}$ sparsification algorithm (LKGS). The LKGS-SGD enables the overlap of computations and communications, and adapts to gradient exchange at layer-wise. Evaluations are conducted by real-world applications. Experimental results show that LKGS-SGD achieves similar convergence to dense S-SGD, while outperforming the original S-SGD and S-SGD with top- $\boldsymbol{k}$ sparsification.

关键词： Training Backpropagation Deep learning Limiting Scalability distributed databases parallel processing

来源：评论

学校读者我要写书评

暂无评论

parallel Implementation of SHA256 on Multizone Heterogeneous Systems

Parallel Implementation of SHA256 on Multizone Heterogeneous...

引用

IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Yongtao Luo Jie Liu Tiaojie Xiao Chunye Gong Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Digitizing program for Frontier Equipment National University of Defense Technology Changsha China National Supercomputer Center in Tianjin Tianjin China

SHA-256 plays an important role in widely used applications, such as data security, data integrity, digital signatures, and cryptocurrencies. However, most of the current optimized implementations of SHA-256 are based on CPUs or dedicated hardware, such as ASICs and FPGAs. Consequently, there is a need to explore whether new heterogeneous parallel framework can improve the computational performance of the hash function. To address this issue, we conducted a study on the MT-3000 platform, which is a special architecture processor for the next-generation exascale prototype supercomputer. We proposed MT-SHA256, a heterogeneous multistage parallel implementation for hashing multiple messages on the MT-3000. Combining the architectural features of this processor, we developed an effective solution that significantly improved the computational performance of SHA-256. As a result, MT-SHA256 achieved a maximum throughput of 1045.68 MB/s on a single acceleration core of MT-3000. This is 9.84x higher than the C code implementation on one CPU core of MT-3000. We also performed a scalability test and found that MT-SHA256 achieved a throughput of 98.04 GB/s on a computing node, and extended to 512 nodes (2048 acceleration clusters) on this system with good scalability.

关键词：

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500页 << < 91 92 93 94 95 96 97 98 99 100 > >>

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：