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

16,237 篇 会议
368 篇 期刊文献
22 册 图书

馆藏范围

16,627 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

9,336 篇 工学
- 8,536 篇 计算机科学与技术...
- 4,019 篇 软件工程
- 1,982 篇 电气工程
- 1,383 篇 信息与通信工程
- 676 篇 电子科学与技术（可...
- 535 篇 控制科学与工程
- 228 篇 网络空间安全
- 188 篇 仪器科学与技术
- 141 篇 机械工程
- 115 篇 生物医学工程（可授...
- 106 篇 动力工程及工程热...
- 105 篇 测绘科学与技术
- 97 篇 光学工程
- 91 篇 生物工程
- 82 篇 建筑学
- 70 篇 土木工程
- 63 篇 环境科学与工程（可...
- 61 篇 安全科学与工程
1,973 篇 理学
- 1,505 篇 数学
- 245 篇 物理学
- 203 篇 统计学（可授理学、...
- 177 篇 系统科学
- 115 篇 生物学
- 100 篇 地球物理学
- 69 篇 化学
1,463 篇 管理学
- 1,205 篇 管理科学与工程(可...
- 468 篇 工商管理
- 321 篇 图书情报与档案管...
106 篇 医学
- 86 篇 临床医学
96 篇 经济学
- 93 篇 应用经济学
56 篇 法学
53 篇 农学
18 篇 教育学
12 篇 文学
9 篇 军事学
1 篇 艺术学

主题

2,212 篇 parallel process...
1,199 篇 computer archite...
1,130 篇 concurrent compu...
1,116 篇 distributed comp...
1,063 篇 computational mo...
1,037 篇 application soft...
1,017 篇 distributed proc...
990 篇 hardware
905 篇 computer science
708 篇 graphics process...
595 篇 runtime
527 篇 scalability
518 篇 parallel process...
507 篇 algorithm design...
494 篇 parallel program...
490 篇 parallel algorit...
470 篇 graphics process...
460 篇 kernel
446 篇 processor schedu...
440 篇 conferences

机构

38 篇 ibm thomas j. wa...
33 篇 college of compu...
31 篇 school of comput...
27 篇 oak ridge nation...
26 篇 university of ch...
26 篇 oak ridge natl l...
25 篇 georgia inst tec...
25 篇 ohio state univ ...
24 篇 department of co...
23 篇 tsinghua univers...
23 篇 pacific northwes...
21 篇 argonne national...
21 篇 oak ridge nation...
20 篇 georgia inst tec...
19 篇 college of compu...
19 篇 school of comput...
19 篇 department of co...
19 篇 argonne natl lab...
19 篇 pacific northwes...
19 篇 national laborat...

作者

39 篇 jack dongarra
31 篇 dongarra jack
29 篇 zomaya albert y.
26 篇 bader david a.
23 篇 feng wu-chun
22 篇 boukerche azzedi...
19 篇 hoefler torsten
18 篇 gagan agrawal
18 篇 schulz martin
16 篇 dhabaleswar k. p...
16 篇 p. sadayappan
16 篇 wang yijie
15 篇 ito yasuaki
15 篇 yves robert
14 篇 h. casanova
14 篇 alexey lastovets...
14 篇 azad ariful
13 篇 dongsheng li
13 篇 wang guojun
13 篇 kishore kothapal...

语言

16,553 篇 英文
44 篇 其他
27 篇 中文
2 篇 土耳其文
1 篇 葡萄牙文

检索条件"任意字段=IEEE International Symposium on Parallel and Distributed Processing with Applications"

共 16627 条记录，以下是421-430 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：

Everything Everyway All at Once -Time Traveling Debugging for Stream processing applications 40

Everything Everyway All at Once -Time Traveling Debugging fo...

引用

40th ieee international Conference on Data Engineering, ICDE 2024

作者： Räth, Timo Schlegel, Marius Sattler, Kai-Uwe TU Ilmenau Ilmenau Germany

ISBN: (纸本)9798350317152

Debugging, evaluating, and optimizing stream processing applications is challenging due to continuous streams of input data and typically distributed and parallel execution environments. To address these issues, we present an approach for explorative debugging of stream processing pipelines that allows in-depth investigation of a pipeline's execution behavior and evolution. The time traveling debugger enables traveling back in time within the pipeline's execution history and thoroughly analyzing and retracing each fine-grained step. Any changes made to the pipeline's structure or parameters are captured based on provenance information and can be reviewed, compared, and analyzed with the provenance inspector to understand the impact of each alteration on the quality of the pipeline. © 2024 ieee.

关键词： Program debugging

来源：评论

学校读者我要写书评

暂无评论

Accelerating Deep Neural Networks on Mobile Multicore NPUs 2023

Accelerating Deep Neural Networks on Mobile Multicore NPUs

引用

21st ACM/ieee international symposium on Code Generation and Optimization (CGO)

作者： Jung, Hanwoong Ji, Hexiang Pushchin, Alexey Ostapenko, Maxim Niu, Wenlong Palachev, Ilya Qu, Yutian Fedin, Pavel Gribov, Yuri Nam, Heewoo Lim, Dongguen Kim, Hyunjun Song, Joonho Lee, Seungwon Han, Hwansoo Samsung Adv Inst Technol Suwon South Korea Samsung R&D Inst Xian Peoples R China

ISBN: (纸本)9798400701016

Neural processing units (NPUs) have become indispensable parts of mobile SoCs. Furthermore, integrating multiple NPU cores into a single chip becomes a promising solution for ever-increasing computing power demands in mobile devices. This paper addresses techniques to maximize the utilization of NPU cores and reduce the latency of on-device inference. Mobile NPUs typically have a small amount of local memory (or scratch pad memory, SPM) that provides space only enough for input/output tensors and weights of one layer operation in deep neural networks (DNNs). Even in multicore NPUs, such local memories are distributed across the cores. In such systems, executing network layer operations in parallel is the primary vehicle to achieve performance. By partitioning a layer of DNNs into multiple sub-layers, we can execute them in parallel on multicore NPUs. Within a core, we can also employ pipelined execution to reduce the execution time of a sub-layer. In this execution model, synchronizing parallel execution and loading/storing intermediate tensors in global memory are the main bottlenecks. To alleviate these problems, we propose novel optimization techniques which carefully consider partitioning direction, execution order, synchronization, and global memory access. Using six popular convolutional neural networks (CNNs), we evaluate our optimization techniques in a flagship mobile SoC with three cores. Compared to the highest-performing partitioning approach, our techniques improve performance by 23%, achieving a speedup of 2.1x over single-core systems.

关键词： Neural networks Multicore Neural processing unit Layer partitioning Scheduling optimization

来源：评论

学校读者我要写书评

暂无评论

GPU-Accelerated Maximal Bicliques Mining Framework for Large E-commerce Networks 21

GPU-Accelerated Maximal Bicliques Mining Framework for Large...

引用

21st ieee international symposium on parallel and distributed processing with applications, 13th ieee international Conference on Big Data and Cloud Computing, 16th ieee international Conference on Social Computing and Networking and 13th international Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Li, Jingdong Li, Zhao Wang, Xiaoling Lu, Xingjian Zhang, Ji Chen, Hongyang East China Normal University Shanghai China Zhejiang Lab China University of Southern Queensland Australia

ISBN: (纸本)9798350329223

Many of Taobaos important daily data mining tasks, such as anomaly attack detection and interest group detection, require efficient algorithmic solutions for mining specific graph patterns. The most common graph pattern is biclique which has a very dense structure and often contains rich implicit information. An important question to address is whether and how we can efficiently find all the interesting bicliques in large e-commerce networks, which is coined as the Maximal Biclique Enumeration problem (MBE). MBE involves enumerating all the maximal bicliques in the given graph, which is rather computationally expensive for large networks. However, recent research works on MBE haven't made good use of GPU, a very widely used high-speed computing resource. In this paper, we propose GMBE, a novel framework that achieves an efficient utilization of the power of GPUs to parallelize the MBE algorithm to find all maximal bicliques. We design a programmable API for data analysts to meet different business needs, enabling GMBE to become the middleware to effectively support various graph mining applications in e-commerce domain. Extensive experiments show that GMBE achieves significant (12X) speedup on average over the state-of-the-art MBE algorithms. © 2023 ieee.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

A distributed AND parallel METHOD OF HYPERSPECTRAL COMPUTATIONAL IMAGING VIA COLLABORATIVE TUCKER3 TENSOR DECOMPOSITION

A DISTRIBUTED AND PARALLEL METHOD OF HYPERSPECTRAL COMPUTATI...

引用

ieee international Geoscience and Remote Sensing symposium (IGARSS)

作者： Zhang, Ling Wu, Zebin Sun, Jin Xu, Yang Wei, Zhihui Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Hyperspectral computational imaging (HCI) is to reconstruct hyperspectral images (HSIs) based on the compressed signals collected by remote sensing and imaging systems. Collaborative Tucker3 tensor decomposition is beneficial for HCI models in reconstructing high-fidelity HSIs. However, the ever-increasing amount of compressed data leads to heavy computation burden for tensor decomposition-based HCI models, which may exceed the computing capacity of a single machine. For this reason, this paper proposes a Spark-based distributed and parallel HCI implementation via collaborative Tucker3 tensor decomposition. The proposed implementation decomposes the processing flow of the HCI algorithm into several stages, each of which can be processed in parallel on Spark. In addition, we develop parallel strategies for improving the performance of the redundant computational procedure and data storage procedure, respectively. Experimental results demonstrate that the parallel algorithm not only achieves high accuracy but also improves the computational efficiency when processing large-scale HSI datasets.

关键词： Hyperspectral computational imaging Spark distributed and parallel processing Tucker3 tensor decomposition

来源：评论

学校读者我要写书评

暂无评论

parallel Simulation of Power Systems with High Penetration of distributed Generation Using GPUs and OpenCL 13

Parallel Simulation of Power Systems with High Penetration o...

引用

13th international symposium on Power Electronics for distributed Generation Systems (PEDG)

作者： Zhang, Junjie Mittenbuehler, Marcel Razik, Lukas Benigni, Andrea Forschungszentrum Julich IEK 10 Energy Syst Engn D-52428 Julich Germany Rhein Westfal TH Aachen D-52056 Aachen Germany Raz InformING D-52070 Aachen Germany JARA Energy D-52425 Julich Germany

ISBN: (数字)9781665466189

ISBN: (纸本)9781665466189

In this paper, we present the use of parallel-in-space simulation approach to accelerate the dynamic simulation of power systems with high penetration of distributed generation, i. e. high number of power electronic devices. The approach is implemented using the OpenCL framework and executed on a graphic processing unit (GPU). We benchmark our prototype implementation using a distribution network with increasing number of distributed generator which is modeled by voltage source inverter. Results show that the computation time for the distributed generators solution stays nearly constant with the increasing number of the distributed generators in the network.

关键词： parallel programming power system simulation power electronics

来源：评论

学校读者我要写书评

暂无评论

Strengthening Healthcare Cybersecurity by Optimizing Adversarial Attack Defences in Deep Learning Models using GPU and parallel processing Technologies 7

Strengthening Healthcare Cybersecurity by Optimizing Adversa...

引用

7th international Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Venugopal, Hridya Christo, Mary Subaja Kattankulathur Campus SRM Institute of Science and Technology Department of Networking and Communications Tamilnadu Chengalpattu603203 India

ISBN: (纸本)9798350372816

In healthcare, safeguarding patient data integrity is paramount, necessitating robust deep learning models. This research focuses on bolstering the cyber security defenses of these models, particularly against adversarial attacks, through the application of the Localized Adversarial Feature Attack (LAFEAT) technique. Leveraging Graphics processing Units (GPU) and the TensorFlow framework, the study explores algorithmic enhancements like the Fast Gradient Sign Method (FGSM) and Model-Agnostic Meta-Learning (MAML). These techniques perturb input data gradients or adapt model parameters, enhancing resilience against adversarial perturbations. Additionally, Particle Swarm Optimization (PSO), a metaheuristic optimization technique, is examined to fortify defense mechanisms further. parallel processing techniques, utilizing parallel GPU or distributed clusters, are implemented to expedite the optimization process, reducing computational burdens and enhancing scalability. Despite advancements, gaps remain in understanding how these models can be optimized for real-world healthcare applications, particularly in terms of balancing computational efficiency and robustness. The research demonstrates tangible benefits, with a significant 10% increase in success rates against adversarial attacks compared to baseline methods. Moreover, parallel processing implementation results in a 30% reduction in optimization time, improving the efficiency of cyber security defenses for deep learning models in healthcare. These numerical enhancements underscore the research's value in fortifying healthcare systems against adversarial threats, ensuring the security and integrity of patient data while addressing existing gaps in the optimization of adversarial defenses in healthcare applications. The strengths of this research lie in its robust adversarial attack optimization techniques, integration of parallel processing for efficiency, and application to critical healthcare tasks. Comprehensive

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

Efficient FPGA-Based Accelerator of the L-BFGS Algorithm for IoT applications 56

Efficient FPGA-Based Accelerator of the L-BFGS Algorithm for...

引用

56th ieee international symposium on Circuits and Systems (ISCAS)

作者： Xiong, Huiyang Xiong, Bohang Wang, Wenhao Tian, Jing Zhu, Hao Wang, Zhongfeng Nanjing Univ Sch Elect Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9781665451093

The Internet of Things (IoT)-centric applications, such as augmented reality and self-driven cars, require realtime task processing, large bandwidth, and low data transmission latency. FPGA-based edge computing is considered an effective solution to tackle these challenges. As an excellent tool in these applications, nonlinear optimization methods involve computation-intensive and data-dependency operations leading to limited real-time applications. The limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm ranks among the most efficient algorithms for large-scale optimization problems. In this paper, we propose, for the first time, a high-parallel FPGA-based architecture for the two key parts of the L-BFGS algorithm: the search direction computation and line searching. Compared with the implementation on CPU, the search direction computation and line searching implementation on FPGA achieve 39.73x and 5.50x speedups, respectively. Compared with the straightforward implementation on GPU, the search direction computation on FPGA obtains a speedup of 31.03 x.

关键词： IoT FPGA nonlinear optimization L-BFGS hardware implementation

来源：评论

学校读者我要写书评

暂无评论

Architectural Modeling and Benchmarking for Digital DRAM PIM 27

Architectural Modeling and Benchmarking for Digital DRAM PIM

引用

27th international symposium on Workload Characterization

作者： Siddique, Farzana Ahmed Guo, Deyuan Fan, Zhenxing Gholamrezaei, Mohammadhosein Baradaran, Morteza Ahmed, Alif Abbot, Hugo Durrer, Kyle Nandagopal, Kumaresh Ermovick, Ethan Kiyawat, Khyati Gul, Beenish Mughrabi, Abdullah Venkat, Ashish Skadron, Kevin Univ Virginia Dept Comp Sci Charlottesville VA 22903 USA

ISBN: (纸本)9798350356045;9798350356038

processing In Memory (PIM) integrates computational logic units directly into the memory architecture, offering significant performance improvements for memory-bound applications such as matrix operations, vector operations, and database applications compared to general-purpose CPUs or GPUs. However, the lack of a standardized benchmark suite and simulation framework poses a challenge in exploring, evaluating, and designing different PIM architectures. This paper addresses this gap by introducing a comprehensive benchmark suite, PIMbench, along with a performance and energy modeling framework, PIMeval, both designed to support a wide range of PIM architectures for DRAM. This paper also proposes a set of PIM APIs for writing PIM programs, enabling benchmarks to be executed across different PIM architectures, and allowing for comparing the performance of bit-serial and bit-parallel subarray-level PIM and bank-level PIM. PIMbench and PIMeval have been open-sourced and can be accessed at: https://***/UVA-LavaLab/PIMeval-PIMbench

关键词： processing in memory benchmarks

来源：评论

学校读者我要写书评

暂无评论

A distributed Automatic Domain-Specific Multi-Word Term Recognition Architecture using Spark Ecosystem 22

A Distributed Automatic Domain-Specific Multi-Word Term Reco...

引用

22nd international symposium on parallel and distributed Computing, ISPDC 2023

作者： Truica, Ciprian-Octavian Istrate, Neculai-Ovidiu Apostol, Elena-Simona University Politehnica of Bucharest Faculty of Automatic Control and Computers Computer Science and Engineering Department Bucharest Romania

ISBN: (纸本)9798350341270

Automatic Term Recognition is used to extract domain-specific terms that belong to a given domain. In order to be accurate, these corpus and language-dependent methods require large volumes of textual data that need to be processed to extract candidate terms that are afterward scored according to a given metric. To improve text preprocessing and candidate terms extraction and scoring, we propose a distributed Spark-based architecture to automatically extract domain-specific terms. The main contributions are as follows: (1) propose a novel distributed automatic domain-specific multi-word term recognition architecture built on top of the Spark ecosystem;(2) perform an in-depth analysis of our architecture in terms of accuracy and scalability;(3) design an easy-to-integrate Python implementation that enables the use of Big Data processing in fields such as Computational Linguistics and Natural Language processing. We prove empirically the feasibility of our architecture by performing experiments on two real-world datasets. © 2023 ieee.

关键词： Ecosystems

来源：评论

学校读者我要写书评

暂无评论

Autoencoder-based Anomaly Detection in Microservices using distributed Tracing 20

Autoencoder-based Anomaly Detection in Microservices using D...

引用

20th CSI international symposium on Artificial Intelligence and Signal processing, AISP 2024

作者： Shahini, Shayan Momeni, Hossien Golestan University Computer Engineering Department Gorgan Iran

ISBN: (纸本)9798350383942

In the fast-evolving realm of software development, microservice architecture has become a pivotal strategy for managing modern business tasks. While offering benefits like scalability and independence, ensuring the reliability of microservice systems is crucial for sustained customer satisfaction and business success. This paper introduces AnoTraceAE, an unsupervised anomaly detection framework tailored for microservice applications. Using distributed tracing, AnoTraceAE employs span and trace embeddings with a Convolutional Autoencoder to identify deviations from normal behavior in microservice systems. The model's efficacy is demonstrated through experiments on the TrainTicket dataset, showcasing superior performance across key metrics compared to existing models. AnoTraceAE proves versatile, robustly addressing the challenges of anomaly detection in microservice architectures, highlighting its effectiveness in accurately identifying anomalies and minimizing false positives. © 2024 ieee.

关键词： Anomaly detection

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 << < 39 40 41 42 43 44 45 46 47 48 > >>

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：