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

10,925 篇 会议
282 篇 期刊文献
26 册 图书
1 篇 科技报告

馆藏范围

11,234 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

5,466 篇 工学
- 5,207 篇 计算机科学与技术...
- 2,309 篇 软件工程
- 885 篇 电气工程
- 573 篇 信息与通信工程
- 252 篇 控制科学与工程
- 221 篇 电子科学与技术（可...
- 137 篇 网络空间安全
- 91 篇 机械工程
- 60 篇 生物工程
- 58 篇 动力工程及工程热...
- 52 篇 生物医学工程（可授...
- 46 篇 环境科学与工程（可...
- 45 篇 仪器科学与技术
- 41 篇 安全科学与工程
- 40 篇 建筑学
- 40 篇 测绘科学与技术
- 34 篇 化学工程与技术
- 33 篇 土木工程
- 32 篇 交通运输工程
1,033 篇 理学
- 813 篇 数学
- 121 篇 系统科学
- 116 篇 统计学（可授理学、...
- 83 篇 物理学
- 70 篇 生物学
- 48 篇 化学
- 41 篇 地球物理学
988 篇 管理学
- 843 篇 管理科学与工程(可...
- 285 篇 工商管理
- 176 篇 图书情报与档案管...
57 篇 经济学
- 54 篇 应用经济学
36 篇 法学
26 篇 医学
21 篇 农学
9 篇 教育学
6 篇 文学
3 篇 艺术学
2 篇 军事学

主题

1,705 篇 concurrent compu...
1,583 篇 distributed comp...
1,152 篇 parallel process...
986 篇 computer science
963 篇 computational mo...
873 篇 computer archite...
860 篇 application soft...
725 篇 grid computing
598 篇 high performance...
591 篇 hardware
530 篇 processor schedu...
488 篇 computer network...
456 篇 resource managem...
428 篇 scalability
399 篇 costs
395 篇 bandwidth
394 篇 runtime
383 篇 parallel program...
367 篇 peer to peer com...
356 篇 delay

机构

47 篇 college of compu...
40 篇 institute of com...
30 篇 oak ridge natl l...
29 篇 ibm thomas j. wa...
25 篇 institute of com...
23 篇 inria
23 篇 oak ridge nation...
22 篇 school of comput...
21 篇 mathematics and ...
18 篇 argonne natl lab...
18 篇 department of co...
18 篇 department of co...
18 篇 tokyo institute ...
17 篇 department of co...
17 篇 georgia inst tec...
17 篇 iit dept comp sc...
16 篇 univ chinese aca...
16 篇 lawrence berkele...
16 篇 department of co...
15 篇 department of co...

作者

32 篇 dongarra jack
30 篇 jack dongarra
23 篇 p. sadayappan
21 篇 a. choudhary
21 篇 david a. bader
20 篇 maciejewski anth...
19 篇 feng wu-chun
18 篇 dhabaleswar k. p...
18 篇 hoefler torsten
17 篇 p. banerjee
17 篇 h. casanova
17 篇 bader david a.
17 篇 howard jay siege...
17 篇 schulz martin
17 篇 panda dhabaleswa...
15 篇 s.k. das
15 篇 h.j. siegel
15 篇 hai jin
14 篇 gopalakrishnan g...
14 篇 alexey lastovets...

语言

11,156 篇 英文
68 篇 其他
10 篇 中文

检索条件"任意字段=International Symposium on Parallel and Distributed Computing"

共 11234 条记录，以下是981-990 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：

Low-Latency State Management for Real-Time Tasks in Edge Serverless

Low-Latency State Management for Real-Time Tasks in Edge Ser...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Yanyan Wen Guangping Xu Jianshe Wang Wei Hao School of Computer Science and Engineering Tianjin University of Technology China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Stateful serverless systems commonly adopt an architectural paradigm characterized by compute and storage separation within cloud data centers. Nevertheless, guaranteeing prompt response for real-time tasks at the edge becomes challenging due to network overheads. This paper introduces a Low-Latency state management framework for real-time tasks in edge serverless systems called LoLa, which adaptively places states proximate to functions, thereby mitigating delays in accessing states within edge serverless systems. Our approach aims at mitigating network latency and optimizing resource utilization by co-locating functions and states, thereby enhancing the system’s overall efficiency. We introduce an adaptive strategy to coordinate the migration of states. It dynamically adjusts the positions of states based on historical data and real-time feedback. Additionally, we designed an in-memory state storage mechanism to facilitate low-latency access and implement a lightweight and fine-grained state management to ensure stored state consistency. Evaluation results showcase the efficacy of LoLa in reducing state read and write latency within edge serverless systems. Specifically, the average response latency is observed to decrease by 65.2% and 38.1% in the best and worst-case scenarios, respectively.

关键词： Technological innovation distributed processing Data centers Heuristic algorithms Serverless computing Adaptive algorithms Real-time systems Delays Low latency communication Optimization

来源：评论

学校读者我要写书评

暂无评论

Toward Multi-Agent Coordination in IoT via Prompt Pool-based Continual Reinforcement Learning

Toward Multi-Agent Coordination in IoT via Prompt Pool-based...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Chenhang Xu Jia Wang Xiaohui Zhu Yong Yue Jun Qi Jieming Ma Department of Computing Xi’an Jiaotong-Liverpool University Suzhou China Department of Intelligent Science Xi’an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The Internet of Things (IoT) represents a complex, dynamic environment where edge devices continuously optimize their policies to address a continual stream of tasks. Previous studies have typically relied on a rehearsal buffer containing data from past tasks or a known task identity to mitigate catastrophic forgetting. Our research, Prompt Pool-based Continual Reinforcement Learning (PPCRL), aims to create a more efficient memory system by expanding a single prompt into a prompt pool, allowing agents to automatically select a set of relevant prompts without needing task identity knowledge. Similar to prompt-based learning techniques, our approach utilizes a small trainable prompt pool to guide pre-trained models through sequential task learning systematically. This allows us to optimize prompts for guiding model predictions and effectively manage both shared and task-specific knowledge while maintaining model generalization. We conducted experiments on two multi-agent benchmarks where traditional methods suffer from significant performance degradation. In contrast, PPCRL demonstrates the capability to outperform baselines and exhibits high generalization ability.

关键词： Training Degradation distributed processing Sensitivity Memory management Reinforcement learning Predictive models Benchmark testing Transformers Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Using Pilot Jobs and CernVM File System for Simplified Use of Containers and Software Distribution 21

Using Pilot Jobs and CernVM File System for Simplified Use o...

引用

30th international symposium on High-Performance parallel and distributed computing (HPDC)

作者： Urs, Namratha Mambelli, Marco Dykstra, Dave Univ North Texas Dept Comp Sci & Engn Denton TX 76203 USA Fermilab Natl Accelerator Lab Sci Comp Div POB 500 Batavia IL 60510 USA

ISBN: (纸本)9781450382175

High Energy Physics (HEP) experiments entail an abundance of computing resources, i.e. sites, to run simulations and analyses by processing data. This requirement is fulfilled by local batch farms, grid sites, private/commercial clouds, and supercomputing centers via High Throughput computing (HTC). The growing needs of such experiments and resources being prone to trends of heterogeneity make it difficult for physicists to handle these resources directly. Additionally, HEP collaborations heavily rely on data and software releases, typically in the order of tens of gigabytes, while conducting simulations and analyses. Hence, aspects of scalability, reliability, and maintenance become crucial with regards to the distribution of the necessary data and software stack. The GlideinWMS [4] framework helps with the resource management problem by using pilot jobs, aka Glideins, to provision reliable elastic virtual clusters. Glideins are submitted to unreliable heterogeneous resources which are validated and customized by the Glideins to make the worker nodes available for end-user job execution. On the other hand, the CernVM File System (CernVM-FS or CVMFS) [1] helps with data distribution. It is a write-once, read-everywhere filesystem used to deploy scientific software to thousands of nodes on a worldwide distributed computing infrastructure. CVMFS is based on the Hyper Text Transfer Protocol and has been widely used within the particle physics community for (1) distributing experiment software and data such as calibrations, and (2) facilitating containerization by efficiently hosting container images along with providing containerization software, especially Singularity [3]. GlideinWMS relies on CVMFS installed locally on the computing resources to satisfy the experiments' software needs. This requires system administrators' effort to install and maintain CVMFS at the sites and limits the use of sites, especially HPC resources, that do not have CVMFS installed. This pos

关键词： high throughput computing distributed computing

来源：评论

学校读者我要写书评

暂无评论

Mining Relational Similarity in Social Networks for Enhanced Recommendations

Mining Relational Similarity in Social Networks for Enhanced...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Pengcheng Guo Qingyang Wang Jielin Jiang Siyu Wu Haolong Xiang Xinyue Ji Shengjun Xue Nanjing University of Information Science and Technology Nanjing China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing China Silicon Lake College Suzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Social perception recommendation systems can effectively alleviate the user cold start problem by leveraging the side information of social networks. However, most social perception recommendation systems treat user relations as independently existing entities for learning, thereby overlooking potential connections between relations. Additionally, as the number of relations increases, it inevitably imposes a significant computational burden on the servers. To address these issues, we propose the Social perception recommendation based on relational clustering(SPRRC). SPRRC conducts relational clustering of social networks and projects knowledge graphs, effectively mining information about the similarity between relations. First, we cluster relationships in item knowledge graphs and social networks through unsupervised learning. After that, we use local weighted smoothing to aggregate the map information of the clustered items and social network virtual subgraphs respectively, and use the attention networks to learn the representation of users and items in the interactive bipartite graph. Finally, a large number of experiments have verified the high accuracy of our method, compared with the latest methods.

关键词： distributed processing Smoothing methods Social networking (online) Aggregates Knowledge graphs Bipartite graph Servers Artificial intelligence Recommender systems Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

DAUSK: A Transactional Graph Structure for Skewed Dynamic Graph Storage

DAUSK: A Transactional Graph Structure for Skewed Dynamic Gr...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Anna Ding Yujun Li Yuening Feng Sicong Hu Mengshu Hou Yueyang Zhan University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Dynamic graphs are widely used in real-world applications and exhibit structural skew, which leads to skewed updates. Existing systems that support transactional updates often fail to fully consider this phenomenon, struggling to balance update and query performance. Inspired by the concepts of hierarchical thinking and uneven rebalancing on PMA, we propose DAUSK, a high-performance in-memory structure for dynamic graph storage that supports efficient graph analytics and rapid transactional updates. DAUSK addresses the skewed characteristics of graphs by employing two types of data structures: an unrolled skip list for frequently updated high-degree vertices, allowing asymptotically faster searches and updates, and a compact array-like structure called UPMA for medium and low-degree vertices, enabling efficient sequential scans. At the same time, DAUSK uses a vertex-centric strategy to partition UPMA and applies uneven gap allocation based on vertex degrees. Futhermore, considering the characteristics of transactions on graphs, DAUSK uses the well-established 2PL concurrency protocol to support millions of transactional updates per second. Experimental results demonstrate that DAUSK is up to 10.17×, 3.29× and 1.52× faster in ingesting graph updates compared to three state-of-the-art transactional graph systems, i.e., LiveGraph, Teseo and Sortledton. As for graph analytics, DAUSK achieves comparable, if not superior, performance compared to the other three graph systems.

关键词： Concurrent computing distributed processing Analytical models Protocols Memory management distributed databases Data models Safety Resource management Arrays

来源：评论

学校读者我要写书评

暂无评论

Maximizing Influence of Nodes with Rapid Global Spread in Social Media Data Strea

Maximizing Influence of Nodes with Rapid Global Spread in So...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Ze-Peng Tian Lei-Lei Shi Lu Liu Zi-Xuan Han Zhou Daniel Hao Nick Antonopoulos School of Computer Science and Communication Engineering Jiangsu University China Department of Computer Science University of Exeter UK School of Computing and Mathematical Science University of Leicester UK Principal’s Office Edinburgh Napier University UK

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Influence maximization plays a pivotal role in areas such as cybersecurity, public opinion management, and viral marketing. However, conventional methods for influence maximization grapple with challenges such as seed node clustering, information loss during influence propagation, and biases in overlapping influence consideration. In response to these issues, we introduce Fast Unfold Diffusion (FUD)- an influence maximization algorithm capitalizing on the uniform distribution characteristics in node space and the non-overlapping local features of nodes. This approach aims to facilitate swift and extensive global diffusion of node influence. The performance of our algorithm was validated through the execution of two hundred diffusion simulations, employing the Independent Cascade (IC) diffusion model across six real-world datasets. Compared to the latest algorithms, our algorithm offers substantial benefits in expanding the range of influence diffusion, the speed of influence diffusion and the stability of the algorithm. This not only effectively mitigates the problems found in traditional methodologies but also ensures a significant enhancement in the efficiency of influence maximization.

关键词： distributed processing Social networking (online) distributed databases Clustering algorithms Propagation losses Diffusion models Stability analysis Integrated circuit modeling Computer security Thermal stability

来源：评论

学校读者我要写书评

暂无评论

distributed Data Processing for Large-Scale Simulations on Cloud

Distributed Data Processing for Large-Scale Simulations on C...

引用

IEEE international Joint symposium on Electromagnetic Compatibility (EMC), Signal and Power Integrity (SIPI) and EMC Europe

作者： Lu, Tianjian Hoyer, Stephan Wang, Qing Hu, Lily Chen, Yi-Fan Google Res 1600 Amphitheatre Pkwy Mountain View CA 94043 USA

ISBN: (纸本)9781665448888

The computational challenges encountered in the large-scale simulations are accompanied by those from data-intensive computing. In this work, we proposed a distributed data pipeline for large-scale simulations by using libraries and frameworks available on Cloud services. The building blocks of the proposed data pipeline such as Apache Beam and Zarr are commonly used in the data science and machine learning community. Our contribution is to apply the data-science approaches to handle large-scale simulation data for the hardware design community. The data pipeline is designed with careful considerations for the characteristics of the simulation data in order to achieve high parallel efficiency. The performance of the data pipeline is analyzed with two examples. In the first example, the proposed data pipeline is used to process electric potential obtained with a Poisson solver. In the second example, the data pipeline is used to process thermal and fluid data obtained with a computational fluid dynamic solver. Both solvers are in-house developed and finite-difference based, running in parallel on Tensor Processing Unit (TPU) clusters and serving the purpose of data generation. It is worth mentioning that in this work, the focus is on data processing instead of data generation. The proposed data pipeline is designed in a general manner and is suitable for other types of data generators such as full-wave electromagnetic and multiphysics solvers. The performance analysis demonstrates good storage and computational efficiency of the proposed data pipeline. As a reference, it takes 5 hours and 14 mins to convert simulation data of size 7.8 TB into Zarr format and the maximum total parallelism is chosen as 10,000.

关键词： Apache Beam Data Pipeline distributed computing Simulation Tensor Processing Unit Zarr

来源：评论

学校读者我要写书评

暂无评论

Community Hiding Algorithm Based on Likelihood Analysis Method in Link Prediction

Community Hiding Algorithm Based on Likelihood Analysis Meth...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Ya-Si Wang Lei-Lei Shi Zi-Xuan Han Lu Liu Siyang Song Liming Chen School of Computer Science and Communication Engineering Jiangsu University China School of Computing and Mathematical Sciences University of Leicester UK School of Computer Science and Technology Dalian University of Technology China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In recent years, the rapid development and widespread applications of non-overlapping community discovery have led to a growing issue of privacy leakage. The core of this problem lies in the community discovery algorithms themselves. Researchers have shifted their focus toward developing community hiding algorithms. However, existing non-overlapping community hiding algorithms have mainly been studied in static networks, overlooking the prevalence of dynamic networks in real life. In this paper, we introduce a community hiding algorithm called LALH, which employs the link prediction likelihood analysis method. The algorithm consists of two parts: first, the OLPL algorithm calculates a list of link probabilities in the subsequent time scale of the network, allowing observation of important link distribution. Second, the LALH algorithm selects these crucial links for the community hiding operation. We validate the effectiveness of the LALH algorithm through experiments on three public datasets and a real Twitter dataset.

关键词： Privacy distributed processing Social networking (online) Heuristic algorithms Blogs Probability Prediction algorithms

来源：评论

学校读者我要写书评

暂无评论

Exploiting Inter-Layer Expert Affinity for Accelerating Mixture-of-Experts Model Inference

Exploiting Inter-Layer Expert Affinity for Accelerating Mixt...

引用

international symposium on parallel and distributed Processing (IPDPS)

作者： Jinghan Yao Quentin Anthony Aamir Shafi Hari Subramoni Dhabaleswar K. DK Panda Department of Computer Science and Engineering The Ohio State University Columbus OH U.S.

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

In the realm of large language models (LLMs) like the Generative Pre-trained Transformer (GPT), the Mixture of Experts (MoE) paradigm has emerged as a powerful technique for enhancing model expressiveness and accuracy. However, the deployment of GPT MoE models for parallel inference on distributed systems presents significant challenges, primarily due to the extensive Alltoall communication required for expert routing and aggregation. This communication bottleneck exacerbates the already complex computational landscape, hindering the efficient utilization of high-performance computing resources. In this paper, we propose a lightweight optimization technique called ExFlow, to largely accelerate the inference of these MoE models. We take a new perspective on alleviating the communication overhead by exploiting the inter-layer expert affinity. Unlike previous methods, our solution can be directly applied to pre-trained MoE models without any fine-tuning or accuracy degradation. By proposing a context-coherent expert parallelism on distributed systems, our ExFlow design only uses one Alltoall communication to deliver the same functionality while previous methods all require two Alltoalls. By carefully examining the conditional probability in tokens’ routing across multiple layers, we proved that pre-trained GPT MoE models implicitly exhibit a strong inter-layer expert affinity. We then design an efficient integer programming model to precisely capture such features and show that by properly placing the experts on corresponding GPUs, we can reduce up to 67% of tokens’ cross-GPU routing latency on various hardware configurations and topologies. Our solution beats the cutting-edge Deepspeed-MoE in GPT MoE models with experts from 8 to 64, with up to 2.2x improvement in inference throughput. To the best of our knowledge, this is the first work in leveraging inter-layer expert affinity to accelerate the inference of GPT MoE models. We further provide a detailed study of how

关键词： Training Accuracy Computational modeling Large language models parallel processing Routing Transformers

来源：评论

学校读者我要写书评

暂无评论

Paldia: Enabling SLO-Compliant and Cost-Effective Serverless computing on Heterogeneous Hardware

Paldia: Enabling SLO-Compliant and Cost-Effective Serverless...

引用

international symposium on parallel and distributed Processing (IPDPS)

作者： Vivek M. Bhasi Aakash Sharma Shruti Mohanty Mahmut Taylan Kandemir Chita R. Das The Pennsylvania State University

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Among the variety of applications (apps) being deployed on serverless platforms, apps such as Machine Learning (ML) inference serving can achieve better performance from leveraging accelerators like GPUs. Yet, major serverless providers, despite having GPU-equipped servers, do not offer GPU support for their serverless functions. Given that serverless functions are deployed on various generations of CPUs already, extending this to various (typically more expensive) GPU generations can offer providers a greater range of hardware to serve incoming requests according to the functions and request traffic. Here, providers are faced with the challenge of selecting hardware to reach a well-proportioned trade-off point between cost and performance. While recent works have attempted to address this, they often fail to do so as they overlook optimization opportunities arising from intelligently leveraging existing GPU sharing mechanisms. To address this point, we devise a heterogeneous serverless framework, PALDIA, which uses a prudent Hardware selection policy to acquire capable, cost-effective hardware and perform intelligent request scheduling on it to yield high performance and cost savings. Specifically, our scheduling algorithm employs hybrid spatio-temporal GPU sharing that intelligently trades off job queueing delays and interference to allow the chosen cost-effective hardware to also be highly performant. We extensively evaluate PALDIA using 16 ML inference workloads with real-world traces on a 6 node heterogeneous cluster. Our results show that PALDIA significantly outperforms state-of-the-art works in terms of Service Level Objective (SLO) compliance (up to 13.3% more) and tail latency (up to ∼50% less), with cost savings up to 86%.

关键词： distributed processing Costs Scheduling algorithms Graphics processing units Serverless computing Tail Machine learning

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 << < 95 96 97 98 99 100 101 102 103 104 > >>

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：