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Effectively deploying services on virtualization infrastructure

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Frontiers of Computer Science 2012年第4期6卷 398-408页

作者： Wei GAO Hai JIN Song WU xuanhua shi Jinyan YUAN School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Virtualization technology provides an opportu- nity to acieve efficient usage of computing resources. How- ever, the management of services on virtualization infras- tructure is still in the preliminary stage. Contstructing user service environments quickly and efficiently remains a chal- lenge. This paper presents a service oriented multiple-VM deployment system （SO-MVDS） for creating and configur- ing virtual appliances running services on-demand. The sys- tem provides a template management model where all the virtual machines are created based on the templates with the software environment pre-prepared. To improve the deploy- ment performance, we explore some strategies for incremen- tal mechanisms and deployment. We also design a service de- ployment mechanism to dynamically and automatically de- ploy multiple services within virtual appliances. We evalu- ate both the deployment time and I/O performance using the proposed incremental mechanism. The experimental results show that the incremental mechanism outperforms the clone one.

关键词： multi-VM deployment template incrementalmechanism batch deployment

A nearly optimal distributed algorithm for computing the weighted girth

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Science China(Information Sciences) 2021年第11期64卷 80-94页

作者： Qiang-Sheng HUA Lixiang QIAN Dongxiao YU xuanhua shi Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology School of Computer Science and Technology Shandong University

Computing the weighted girth, which is the sum of weights of edges in the minimum weight cycle,is an important problem in network analysis. The problem for distributively computing girth in unweighted graphs has garnered lots of attention, but there are few studies in weighted graphs. In this paper, we propose a distributed randomized algorithm for computing the weighted girth in weighted graphs with integral edge weights in the range [1, nc], where n is the number of vertices and c is a constant. The algorithm is devised under the standard synchronous CON GE S T model, which limits each vertex can only transfer O（log n） bits information along each incident edge in a round. The upper bound of the algorithm is O（n log;n） rounds. We also prove the lower bound for computing the weighted girth is ?（D + n/log n） where D is the hop diameter of the weighted graph. This means our distributed algorithm is optimal within a factor of O（log;n）.

关键词： distributed algorithms weighted girth $\mathcal{CONGEST}$ model communication complexity round complexity

Reveal training performance mystery between Tensor Flow and PyTorch in the single GPU environment

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Science China(Information Sciences) 2022年第1期65卷 147-163页

作者： Hulin DAI Xuan PENG xuanhua shi Ligang HE Qian XIONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabSchool of Computer Science and Technology Huazhong University of Science and Technology Department of Computer Science University of Warwick

Deep learning has gained tremendous success in various fields while training deep neural networks(DNNs) is very compute-intensive, which results in numerous deep learning frameworks that aim to offer better usability and higher performance to deep learning practitioners. Tensor Flow and Py Torch are the two most popular frameworks. Tensor Flow is more promising within the industry context, while Py Torch is more appealing in academia. However, these two frameworks differ much owing to the opposite design philosophy:static vs dynamic computation graph. Tensor Flow is regarded as being more performance-friendly as it has more opportunities to perform optimizations with the full view of the computation graph. However, there are also claims that Py Torch is faster than Tensor Flow sometimes, which confuses the end-users on the choice between them. In this paper, we carry out the analytical and experimental analysis to unravel the mystery of comparison in training speed on single-GPU between Tensor Flow and Py Torch. To ensure that our investigation is as comprehensive as possible, we carefully select seven popular neural networks, which cover computer vision, speech recognition, and natural language processing(NLP). The contributions of this work are two-fold. First, we conduct the detailed benchmarking experiments on Tensor Flow and Py Torch and analyze the reasons for their performance difference. This work provides the guidance for the end-users to choose between these two frameworks. Second, we identify some key factors that affect the performance,which can direct the end-users to write their models more efficiently.

关键词： deep learning performance comparison TensorFlow PyTorch

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基于异构硬件的LSTM训练系统

大数据 2024年第4期10卷 172-188页

作者：黄为新胡伟方曹雪娇石宣化华中科技大学计算机科学与技术学院湖北武汉430074 华中科技大学大数据技术与系统国家地方联合工程研究中心服务计算技术与系统教育部重点实验室湖北武汉430074

在大数据时代,以LSTM为代表的深度神经网络模型具有处理海量数据的能力,在语言处理、语音识别、时序数据预测等领域表现优异。随着模型复杂度的提高,训练成本大幅提升。现有的LSTM训练系统使用了算子融合、多流等加速手段,但忽略了训练... 详细信息

在大数据时代,以LSTM为代表的深度神经网络模型具有处理海量数据的能力,在语言处理、语音识别、时序数据预测等领域表现优异。随着模型复杂度的提高,训练成本大幅提升。现有的LSTM训练系统使用了算子融合、多流等加速手段,但忽略了训练算子内部计算的可并行性,导致计算资源的利用率低,整体耗时长。为此,设计了基于细粒度模型划分和多流并行调度方法的LSTM训练系统TurboLSTM,在英伟达GPU和国产昇腾NPU这两种异构硬件上构建的全新底层训练算子实现了任务对计算资源的合理利用。与已有训练系统相比,在GPU上TurboLSTM的单算子训练时间缩短了23%,模型的整体训练时间缩短了17%,在NPU上TurboLSTM的单算子训练时间缩短了15%,且对计算资源的利用率显著提高。这表明提出的加速方案是高效的,具有良好的泛化能力。

关键词： LSTM 训练加速细粒度并行多流调度

China in the eyes of news media:a case study under COVID-19 epidemic

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Frontiers of Information Technology & Electronic Engineering 2021年第11期22卷 1443-1457页

作者： Hong HUANG Zhexue CHEN xuanhua shi Chenxu WANG Zepeng HE Hai JIN Mingxin ZHANG Zongya LI National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan430074China Services Computing Technology and System Lab Huazhong University of Science and TechnologyWuhan430074China Cluster and Grid Computing Lab Huazhong University of Science and TechnologyWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Journalism and Information Communication Huazhong University of Science and TechnologyWuhan430074China

As one of the early COVID-19 epidemic outbreak areas,China attracted the global news media’s attention at the beginning of *** the epidemic period,Chinese people united and actively fought against the ***,in the eyes of the international public,the situation reported about China is not *** better understand how the international public portrays China,especially during the epidemic,we present a case study with big data *** aim to answer three questions:(1)What has the international media focused on during the COVID-19 epidemic period?(2)What is the media’s tone when they report China?(3)What is the media’s attitude when talking about China?In detail,we crawled more than 280000 pieces of news from 57 mainstream media agencies in 22 countries and made some interesting *** example,international media paid more attention to Chinese livelihood during the COVID-19 epidemic *** March and April,“progress of Chinese vaccines,”“specific drugs and treatments,”and“virus outbreak in U.S.”became the media’s most common *** terms of news attitude,Cuba,Malaysia,and Venezuela had a positive attitude toward China,while France,Canada,and the United Kingdom had a negative *** study can help understand China’s image in the eyes of the international media and provide a sound basis for image analysis.

关键词： Country image COVID-19 epidemic Topic mining Entity Tone of news Emotion

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通用网格容错框架研究

华中科技大学学报（自然科学版） 2006年第7期34卷 42-45页

作者：石宣化金海羌卫中华中科技大学计算机科学与技术学院湖北武汉430074

针对网格计算可靠性需求,提出一套网格计算容错框架,该容错框架包括两个方面网格错误检测与网格错误处理.本容错框架通过提供一种层次式错误检测方式以及基于策略的通用错误处理方式来保证网格计算的可靠性.错误检测服务按照层次方式组... 详细信息

针对网格计算可靠性需求,提出一套网格计算容错框架,该容错框架包括两个方面网格错误检测与网格错误处理.本容错框架通过提供一种层次式错误检测方式以及基于策略的通用错误处理方式来保证网格计算的可靠性.错误检测服务按照层次方式组织,最底层是本地错误检测器,它负责收集被检测对象的信息,发送到中间层的数据收集器,中间层数据收集器按照列表方式发送被检测对象的信息到顶层数据收集器.当错误检测器检测到运行错误时,按照决策分析的方法来提供灵活的错误处理方式.对系统的性能评测表明提出的通用网格容错框架具有很好的扩展性、高效性以及较低的额外开销.

关键词：错误检测容错基于策略的错误处理

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资源代理在异构网格互操作中的应用

华中科技大学学报（自然科学版） 2007年第11期35卷 80-83页

作者：石柯代峰石宣化陶永才华中科技大学计算机科学与技术学院湖北武汉430074

提出了一种基于Agent服务的网格资源代理方法,用以实现目前国内最大的教育科研网格CGSP与CNGrid的互操作.网格资源代理通过对Agent服务的部署和控制来进行用户身份认证以及网格服务共享等任务,从而实现异构网格中间件系统的用户单点登... 详细信息

提出了一种基于Agent服务的网格资源代理方法,用以实现目前国内最大的教育科研网格CGSP与CNGrid的互操作.网格资源代理通过对Agent服务的部署和控制来进行用户身份认证以及网格服务共享等任务,从而实现异构网格中间件系统的用户单点登录、资源共享与服务的互操作.本方法具有通用、灵活,结构简单且易扩展的特点.

关键词：异构网格互操作资源代理中国教育科研网格中国国家网格

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基于带权有向图的网格工作流数据传输策略

华中科技大学学报（自然科学版） 2005年第12期33卷 112-114页

作者：韩宗芬何康章勤石宣化华中科技大学计算机科学与技术学院湖北武汉430074

针对网格中数据传输忽略网络状况的问题,提出了一种基于带权有向图的网格工作流数据传输策略.该策略针对不同数据处理率的网格工作流作业,首先根据网格内各结点之间的带宽,然后选择合适的数据传输方式,计算出两点之间的权重从而生成一... 详细信息

针对网格中数据传输忽略网络状况的问题,提出了一种基于带权有向图的网格工作流数据传输策略.该策略针对不同数据处理率的网格工作流作业,首先根据网格内各结点之间的带宽,然后选择合适的数据传输方式,计算出两点之间的权重从而生成一个带权有向图.最后从图中选出最佳的传输路径进行数据传输,从而提高了工作流的执行性能.实验证明了在网络结构及传输速度一定时,该策略的数据传输性能优于传统的利用GridFTP直接传送的数据传输方法.

关键词：带权有向图网格网格工作流数据处理率

一种面向HPC Cloud系统的在线作业调度算法

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华中科技大学学报（自然科学版） 2011年第S1期39卷 47-50,55页

作者：王飞吴松石宣化赵娜华中科技大学计算机科学与技术学院湖北武汉430074 华中科技大学服务计算技术与系统教育部重点实验室湖北武汉430074

针对HPC Cloud中的作业之间对网络I/O资源的竞争问题,提出了一种基于动态规划算法、二分搜索算法和网络最大流算法的在线作业调度算法——DBMF算法.DBMF算法专注于物理节点上网络I/O的负载均衡,通过物理节点之间的网络I/O负载均衡来减... 详细信息

针对HPC Cloud中的作业之间对网络I/O资源的竞争问题,提出了一种基于动态规划算法、二分搜索算法和网络最大流算法的在线作业调度算法——DBMF算法.DBMF算法专注于物理节点上网络I/O的负载均衡,通过物理节点之间的网络I/O负载均衡来减少对网络I/O资源的竞争.通过与FCFS和RSF算法进行对比,DBMF调度算法有效提高了HPC Cloud的系统负载,减少了作业之间对网络I/O资源的竞争.

关键词：高性能计算云计算作业调度最大流算法动态规划