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检索条件"任意字段=International Symposium on Parallel and Distributed Processing and Applications, ISPA 2003"

共 1194 条记录，以下是61-70 订阅

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An efficient hybrid MPI/OpenMP parallelization of the asynchronous ADMM algorithm 19

An efficient hybrid MPI/OpenMP parallelization of the asynch...

引用

19th IEEE international symposium on parallel and distributed processing with applications (IEEE ispa)

作者： Qiu, Qinnan Lei, Yongmei Wang, Dongxia Wang, Guozheng Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China

ISBN: (纸本)9781665435741

Alternating direction method of multipliers (ADMM) is an efficient algorithm to solve large- scale machine learning problems in a distributed environment. To make full use of the hierarchical memory model in modern highperformance computing systems, this paper implements a hybrid MPI/OpenMP parallelization of the asynchronous ADMM algorithm (AH-ADMM). The AH-ADMM algorithm updates local variables in parallel by OpenMP threads and exchanges information between MPI processes, which relieves memory and communication pressure by replacing multiprocessing with multi- threading. Furthermore, for the SVM problem, the AH-ADMMalgorithm speeds up the calculation of sub- problems through an efficient parallel optimization strategy. This paper effectively combines the features of both algorithm design and programming model. Experiments on the Ziqiang4000 high-performance cluster demonstrate that the AH- ADMM algorithm scales better and run faster than the existing distributed ADMM algorithms implemented by pure MPI. The AH-ADMM can reduce the communication overhead by up to 91.8% and increase the convergence rate by up to 36x. For large datasets, the AH-ADMM can scale well on the cluster which over 129 cores.

关键词： distributed ADMM algorithm asynchronous communication hybrid parallel programming model MPI OpenMP

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A novel Optimization Technique for Mastering Energy Consumption in Cloud Data center 15

A novel Optimization Technique for Mastering Energy Consumpt...

引用

15th IEEE international symposium on parallel and distributed processing with applications (ispa) / 16th IEEE international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Ben Maaouia, Omar Fkaier, Hazem Jemni, Mohamed Cerin, Christophe Univ Tunis LATICE ENSIT Tunis Tunisia Univ Tunis El Manar FST Tunis Tunisia Univ Paris 13 LIPN UMR 7030 CNRS Villetaneuse France

ISBN: (纸本)9781538637906

Cloud computing is one of the most popular technologies nowadays because of its wide utilities and various benefits in several IT companies all over the world. However, in front of the increasing users' requests for computing services, cloud providers are encouraged to deploy large data centers, which consumes very large amount of energy and contribute to high operational costs. Among the effects, carbon dioxide emission rate is growing each day due to the huge amount of power consumption. This energy efficiency is an important issue in cloud computing, mainly due to the required electrical power to run these systems and to cool them. Therefore, energy consumption has become a major concern for the widespread deployment of Cloud data centers. The growing importance for parallel applications in the Cloud introduces significant challenges in reducing energy consumption from hosted servers. This paper addresses the problem of placing independent applications on the physical servers (hosts) of a Cloud infrastructure. We proposed a novel heuristic to allocate applications so that total energy consumption is reduced. Our proposal respects various constraints e.g. the machines availability, capability and the duplication of applications. Experiments are illustrated to validate the potential of our approach.

关键词： Resource management for parallel and distributed systems Cloud computing Energy consumption Minimization

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An Efficient Scheduling Algorithm for distributed Heterogeneous Systems with Task Duplication Allowed 19

An Efficient Scheduling Algorithm for Distributed Heterogene...

引用

19th IEEE international symposium on parallel and distributed processing with applications (IEEE ispa)

作者： Shi, Hao Chen, Yixiang Xu, Jinyi East China Normal Univ Dept Embedded Software & Syst Software Engn Inst Shanghai Peoples R China MOE Engn Res Ctr Software Hardware Codesign Techn Shanghai Peoples R China

ISBN: (纸本)9781665435741

Due to the restriction of data transmission speed and bandwidth between processing units, communication delays are significantly impacting the complete time of applications in distributed heterogeneous systems. Most of the existing methods considering communication delays are approximate. The performance of solutions given by these heuristic methods are depends on the input instance. Another common choice is optimization methods, such as genetic algorithms and branch-and-bound search. However, these algorithms solve general situations with low efficiency. This paper proposes an efficient scheduling algorithm with task duplication allowed, called DHSA. Task duplication allows tasks executing multiple times to trade extra computation for communication delays, which is widely used in parallel system scheduling approximately. To address the complexity explosion caused by allowing all-task duplication, we decompose the scheduling into allocation and ordering stages and optimize them separately with solving optimization model and slack-interval aware ordering approach. Extensive experiments show that DHSA gives more optimal solutions within feasible time comparing to two state-of-the-art methods, particularly for applications with medium to high communication load.

关键词： Duplication-based Scheduling distributed Heterogeneous Systems Optimization Model Slack-interval Aware Ordering

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An approach of anomaly diagnosis with logs for distributed services in communication network information system 15

An approach of anomaly diagnosis with logs for distributed s...

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15th IEEE international symposium on parallel and distributed processing with applications (ispa) / 16th IEEE international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Sun, Kang Meng, Luo-Ming Guo, Shao-Yong Xu, Si-Ya Wang, Ying Li, Wei-Jian Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Power Grid Co Ltd Elect Power Dispatching Control Ctr Guangdong Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538637906

Anomaly diagnosis for distributed service plays an important role in communication network information system. Log analysis is the main method to undertake anomaly detection. In order to reduce the manual detection, we propose an anomaly detection method based on the time-weighted control flaw graph model. The border is split by a discrete degree strategy based on analyzing the time interval distribution and the time weight is selected to be k-means. Experiments show that our algorithm has good precision and recall in anomaly diagnosis. In real-world scenarios, it has a precision of 80% and a recall rate of 65% on average.

关键词： anomaly diagnosis logs distributed services time weight

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A Three-Level parallel Algorithm For MrBayes 3.2 15

A Three-Level Parallel Algorithm For MrBayes 3.2

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15th IEEE international symposium on parallel and distributed processing with applications (ispa) / 16th IEEE international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Zhao, Mingjie Ren, Qiang Wang, Yilin Deng, Ruikang Ren, Mingming Wang, Gang Liu, Xiaoguang Nankai Univ Nankai Baidu Joint Lab Tianjin Peoples R China

ISBN: (纸本)9781538637906

MrBayes is a popular bioinformatics software that is widely used in phylogenetic analysis. The core algorithm of Mrbayes is Metropolis Coupled Markov Chain Monte Carlo (MC3). However, when dealing with large data sets, MC3 algorithm is too slow to meet researcher's requirements. Although several parallelizations have been proposed for MrBayes, such as MPI (Message Passing Interface) based MrBayes, GPU (Graphics processing Unit) based MrBayes, there is still no efficient parallel algorithm to fully utilize computing power of modern CPU and computer architecture. This paper (a) presents a new three-level hybrid parallel algorithm, include data-level parallelism (DLP), thread-level parallelism (TLP), and process-level parallelism (PLP), which can be used on most modern multi-core computers;(b) compares the performance of different combinations of parallel strategies on real-world protein data sets. The experimental results show that, this hybrid parallel algorithm does convert more computing powers into higher speedup. Furthermore, the proposed algorithm's speedup is near the speedup on one GPU at the same data sets. This algorithm is fit for practical use in phylogenetic inferences.

关键词： MrBayes SIMD hybrid parallel computing

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A Privacy-Aware Conceptual Framework for Coordination 15

A Privacy-Aware Conceptual Framework for Coordination

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15th IEEE international symposium on parallel and distributed processing with applications (ispa) / 16th IEEE international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Elahi, Haroon Wang, Guojun Zhang, Wei Guangzhou Univ Sch Comp Sci & Educ Software Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781538637906

Enterprise architects and information system designers need to understand and manage workflows, data flows, and social interactions to design tools and systems for well-coordinated organizational operations. However, the organizational-nature has drastically transformed over the recent years due to wide-scale use of new computing technologies. Disintegrated structures, large quantities of frequently-generated data, and dubious system and interaction boundaries are some of the obvious identifiers of a modern enterprise, where poorly designed coordination can lead to serious privacy risks. Old coordination modeling frameworks do not set well for the new organizational settings, and a need for alternative models and frameworks has been felt. In this paper, we propose a privacy-aware conceptual framework for understanding coordination by identifying and mapping work, data, and interaction patterns in organizational environments. These propositions intend to help practitioners in developing an updated understanding of the coordination that serves privacy needs, as well.

关键词： Coordination Computer Supported Cooperative Work Information System Design Privacy distributed Environments

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Dynamic Load Balancing and Channel Strategy for Apache Flume Collecting Real-time Data Stream 15

Dynamic Load Balancing and Channel Strategy for Apache Flume...

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15th IEEE international symposium on parallel and distributed processing with applications (ispa) / 16th IEEE international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Shu, Buqing Chen, Haopeng Sun, Meng Shanghai Jiao Tong Univ Sch Software Shanghai Peoples R China

ISBN: (纸本)9781538637906

With the development of information technology, real-time data stream processing(RTDSP) has become a popular research topic. The first step of RTDSP is collecting data, requiring a data collector to receive data from the source and send them to the sink. Apache Flume, a distributed and reliable framework, used for this purpose, has some limitations and drawbacks on load balancing and storage. In this paper, we aim to improve performance and availability for collecting unstable real-time big data stream. So we propose a new load balancing strategy based on the free memory size and a storage strategy of integration memory channel with the multi-file channel to reduce the overhead of disk and network. Finally, the experimental results show that the availability and performance are improved under the condition of a poor network, high availability requirements, intense competition in memory resources and large data size. Specifically, the availability is higher than 99.999%, and the performance can be improved by 10%-50% under different conditions.

关键词： Load management Real-time systems distributed databases Throughput Monitoring Dynamic scheduling Memory management

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OMOPredictor: An Online Multi-step Operator Performance Prediction Framework in distributed Streaming processing 17

OMOPredictor: An Online Multi-step Operator Performance Pred...

引用

IEEE Int Conf on parallel and distributed processing with applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking (ispa/BDCloud/SocialCom/SustainCom)

作者： Mu, Weimin Jin, Zongze Liu, Fan Zhu, Weilin Wang, Weiping Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9781728143286

Recently, with the development of distributed stream processing systems, the elastic resource scaling technique has been significantly improved. Many researchers focus on leveraging the approaches based on predicting the trend of data load to implement the elastic scaling. However, the existing predicting methods cannot track and predict the fluctuation of performance online accurately, and they need to utilize more dimensions of the raw data and resources to enhance the performance of prediction. To address these issues, we propose a framework named OMOPredictor to make an accurate prediction of operator performance online. The experimental results show that OMOPredictor can enhance the prediction of the operator performance on three real-world datasets.

关键词： data stream processing elasticity operator performance prediction deep learning

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parallel analysis of TACLeBench kernel benchmark's loop and procedure level speculation 19

Parallel analysis of TACLeBench kernel benchmark's loop and ...

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19th IEEE international symposium on parallel and distributed processing with applications (IEEE ispa)

作者： Meng, Huiling Wang, Yaobin Li, Ling Musariri, Manasah Wang, Xinyi Southwest Univ Sci & Technol Mianyang Sichuan Peoples R China

ISBN: (纸本)9781665435741

Thread level speculation (TLS) technology has gained substantial international recognition due to its unique parallel program execution. It uses the potential parallel execution of programs to improve the utilization of multi-core resources. However, the kernel benchmark of TACLeBench has not effectively been analyzed using TLS parallelization. In response to this problem, we select 7 programs in the TACLeBench kernel benchmark to analyze its loop and procedure level speculation executions to measure their maximum potential parallelism. Furthermore, we discuss its runtime characteristics (thread size, speculative parallelism coverage, dependency feature) and the influence of program source code on speedup. Our experimental results illustrate that: 1) Most applications achieve impressive results. Bsort reaches 20.79x speedup in loop level speculation, and lms has 9.51x speedup in procedure level speculation;2) By using TLS to accelerate the TACLeBench kernel benchmark, most applications effectively utilize computing resources from 4 to 16 cores;3) The kernel benchmark is more suitable for developing parallelism in loop level speculation.

关键词： Thread level speculation (TLS) multi-core TACLeBench

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A distributed Computation Offloading Scheduling Framework based on Deep Reinforcement Learning 19

A Distributed Computation Offloading Scheduling Framework ba...

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19th IEEE international symposium on parallel and distributed processing with applications (IEEE ispa)

作者： Dai, Bin Ren, Tao Niu, Jianwei Hu, Zheyuan Hu, Shucheng Qiu, Meikang Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China Zhengzhou Univ Res Inst Ind Technol Sch Informat Engn Zhengzhou 450001 Peoples R China Texas A&M Univ Commerce Dept Comp Sci Commerce TX 75428 USA

ISBN: (纸本)9781665435741

Recent years have witnessed the rapid growth of smart devices and mobile applications. However, mobile applications are typically computation-intensive and delay-sensitive, while User Devices (UDs) are usually resource-limited. Mobile Edge Computing (MEC) has been proposed as a promising paradigm to mitigate the tension, where UDs' tasks could be executed either locally on itself or remotely on the edge server via computation offloading. Lots of efficient computation offloading scheduling approaches have been proposed, whereas most of them are based on centralized scheduling which could face troubles in large-scale MEC. To address the issue, this paper proposes a distributed scheduling framework by leveraging the idea of `centralized training and distributed scheduling'. Furthermore, the Actor-Critic reinforcement learning is adopted to build the framework where the Actor and Critic play the roles of distributed scheduling and centralized training, respectively. Extensive simulations are conducted and the experimental results verify the effectiveness and efficiency of the proposed framework.

关键词： distributed scheduling computation offloading deep reinforcement learning mobile edge computing

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