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Communication state transfer for the mobility of concurrent heterogeneous computing

IEEE TRANSACTIONS ON COMPUTERS 2004年第10期53卷 1260-1273页

作者： Chanchio, K Sun, XH Oak Ridge Natl Lab Oak Ridge TN 37831 USA IIT Dept Comp Sci Chicago IL 60616 USA

In a dynamic environment where a process can migrate from one host to another host, communication state transfer is a key issue of process coordination. This paper presents a set of data communication and process migration protocols to support communication state transfer in a dynamic, distributed parallel environment. The protocols preserve the semantics of point-to-point communication;they guarantee message delivery, maintain message ordering, and do not introduce deadlock when blocking send or receive operations are performed during process migration. Analytical proofs and prototype implementation are conducted to confirm the correctness of the protocols. Analytical and experimental results show the proposed design is valid and has a true potential in network computing.

关键词： communication protocol process migration distributed and parallel processing point-to-point communication

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MonARCh: an actor based architecture for dynamic linked data monitoring

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2133页

作者： Yonyul, Burak Alatl, Oylum Erdur, Riza Cenk Ege Univ Fac Engn Dept Comp Engn Izmir Turkiye

Monitoring the data sources for possible changes is an important consumption requirement for applications running in interaction with the Web of Data. In this article, MonARCh which is an architecture for monitoring the result changes of registered SPARQL queries in the Linked Data environment, is proposed. MonARCh can be comprehended as a publish/subscribe system in the general sense. However, it differs in how communication with the data sources is realized. Data sources in the Linked Data environment do not publish the changes in the data. MonARCh provides the necessary communication infrastructure between the data sources and the consumers for the notification of changes. Users subscribe SPARQL queries to the system which are then converted to federated queries. MonARCh periodically checks for updates by re-executing SERVICE clauses and notifying users in case of any result change. In addition, to provide scalability, MonARCh takes the advantage of concurrent computation of the actor model. The parallel join algorithm utilized speeds up query execution and result generation processes. The design science methodology is used during the design, implementation and evaluation of the architecture. When compared to the literature MonARCh meets all the sufficient requirements from the linked data monitoring and state of the art perspectives while having many outstanding features from both points of view. The evaluation results show that even while working under the limited two-node cluster setting MonARCh could reach from 300 to 25,000 query monitoring capacity according to the diverse query selectivities executed within our test bench.

关键词： Query monitoring SPARQL Linked data Scalability distributed and parallel processing Concurrent computation Publish/Subscribe architecture Push-pull model Actor model

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Asynchronous and Load-Balanced Union-Find for distributed and parallel Scientific Data Visualization and Analysis

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2021年第6期27卷 2808-2820页

作者： Xu, Jiayi Guo, Hanqi Shen, Han-Wei Raj, Mukund Wang, Xueyun Xu, Xueqiao Wang, Zhehui Peterka, Tom Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Argonne Natl Lab Math & Comp Sci Div Lemont IL 60439 USA Peking Univ Sch Phys Beijing 100871 Peoples R China Lawrence Livermore Natl Lab Phys & Life Sci Directorate Livermore CA 94550 USA Los Alamos Natl Lab Div Phys Los Alamos NM 87545 USA

We present a novel distributed union-find algorithm that features asynchronous parallelism and k-d tree based load balancing for scalable visualization and analysis of scientific data. Applications of union-find include level set extraction and critical point tracking, but distributed union-find can suffer from high synchronization costs and imbalanced workloads across parallel processes. In this study, we prove that global synchronizations in existing distributed union-find can be eliminated without changing final results, allowing overlapped communications and computations for scalable processing. We also use a k-d tree decomposition to redistribute inputs, in order to improve workload balancing. We benchmark the scalability of our algorithm with up to 1,024 processes using both synthetic and application data. We demonstrate the use of our algorithm in critical point tracking and super-level set extraction with high-speed imaging experiments and fusion plasma simulations, respectively.

关键词： Data visualization Synchronization distributed databases parallel processing Data mining Scalability Merging Union-find disjoint set connected component labeling distributed and parallel processing critical point level set

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Enhanced-Sweep: Communication Cost Efficient Top-K Best Region Search

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第2期48卷 2121-2132页

作者： Potluri, Avinash Bhattu, S. Nagesh Kumar, N. V. Narendra Subramanyam, R. B., V IDRBT Hyderabad India NIT Warangal Hanamkonda India NIT AP Tadepalligudem India

The best region search (BRS) is one of the major research problems in geospatial data processing applications. The BRS problem objective is to discover the ideal location of a particular size specified rectangle, with a predetermined end goal of maximizing the user-defined scoring function. The existing solutions for finding the top-k best regions have focused on designing algorithms for centralized settings. These solutions are not suitable for processing massive datasets. In this paper, we enable a Hadoop MapReduce-based parallel and distributed computation to obtain significant improvement in the performance. In addition to the parallel and distributed setting, we also incorporate early pruning strategies to eliminate the need to process rectangles that are not part of the output to minimize the communication cost involved in computing k-BRS. We later introduced a redistribution strategy over the initially proposed methodology that handles skew inherited from the dataset. Our results are obtained from extensive experimentation, both synthetic and real-world datasets.

关键词： Big data MapReduce Best region search distributed and parallel processing

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parallel subspace projection beamforming for autonomous, passive sonar signal processing

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JOURNAL OF COMPUTATIONAL ACOUSTICS 2003年第1期11卷 55-74页

作者： Kim, K George, AD Univ Florida Dept Elect & Comp Engn High Performance Comp & Simulat Res Lab Gainesville FL 32611 USA

Adaptive techniques can be applied to improve performance of a beamformer in a cluttered environment. The sequential implementation of an adaptive beamformer, for many sensors and over a wide band of frequencies, presents a serious computational challenge. By coupling each transducer node with a microprocessor, in-situ parallel processing applied to an adaptive beamformer on a distributed system can glean advantages in execution speed, fault tolerance, scalability, and cost. In this paper, parallel algorithms for Subspace Projection Beamforming (SPB), using QR decomposition on distributed systems, are introduced for in-situ signal processing. Performance results from parallel and sequential algorithms are presented using a distributed system testbed comprised of a cluster of computers connected by a network. The execution times, parallel efficiencies, and memory requirements of each parallel algorithm are presented and analyzed. The results of these analyses demonstrate that parallel in-situ processing holds the potential to meet the needs of future advanced beamforming algorithms in a scalable fashion.

关键词： distributed and parallel processing subspace projection beamforming cluster computing

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MPEG-4 based interactive video using parallel processing

MPEG-4 based interactive video using parallel processing

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International Conference on parallel processing (ICPP)

作者： He, Y Ahmad, I Liou, ML Hong Kong Univ Sci & Technol Dept Elect & Elect Engn Hong Kong Hong Kong

ISBN: (纸本)0818686510

MPEG-4 which is currently being developed by MPEG (Moving Pictures Experts Group), is poised to become a standard for supporting current and emerging interactive multimedia applications. The objective of MPEG-4 is to support content-based compression, communication, access and manipulation of digital objects which can be natural or synthetic. Since MPEG-4 based video consists of objects and provides full interactivity between the client and the server, a software-based implementation seems to be the only viable approach for building an MPEG-4 encoder. parallel processing solves the problem of large computational requirements for building a real-time encoder. In this paper, we describe a parallel implementation of MPEG-4 video encoder using a cluster of workstations collectively working as a virtual machine. parallelization of the MPEG-4 encoder poses an interesting problem since not only can objects be added or deleted from a video scene but their sizes and shapes may vary with time. Moreover, some of the computationally intensive parts of the encoder are non-uniform algorithms, which means their execution times are data dependent and cannot be predicted in advance. In order to guarantee the spatio-temporal relationship between various objects in a video, we propose a real-time scheduling algorithm for exploiting parallelism in the temporal domain. The algorithm divides the workstations into a number of groups and assigns one video object to one group df workstations for encoding. A dynamic shape-adaptive data partitioning strategy is proposed to exploit parallelism in the spatial domain. The partitioning strategy divides the data of an object among the workstations within a group. The scheduling scheme ensures the synchronization requirements among multiple objects while the dynamic data parallel approach adapts to the object shape variations to balance the load for all the workstations. The performance of the encoder can scale according to the number of workstatio

关键词： MPEG-4 video compression distributed and parallel processing data partitioning scheduling MPI

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Complexity Analysis of Multilayer Perceptron Neural Network Embedded into a Wireless Sensor Network

Complexity Analysis of Multilayer Perceptron Neural Network ...

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Conference on Conquering Complexity - Challenges and Opportunities

作者： Serpen, Gursel Gao, Zhenning Univ Toledo Toledo OH 43606 USA

This paper presents computational and message complexity analysis for a multi-layer perceptron neural network, which is implemented in fully distributed and parallel form across a wireless sensor network. Wireless sensor networks offer a promising platform for parallel and distributed neurocomputing as well as potentially benefiting from artificial neural networks for enhancing their adaptation abilities and computational intelligence. Multilayer perceptron (MLP) neural networks are generic function approximators and classifiers with countless domain-specific applications as reported in the literature. Accordingly, embedding a multilayer perceptron neural network in a wireless sensor network in parallel and distributed mode offers synergy and is very promising. Accordingly, assessing the computational and communication complexity of such hybrid designs, namely an artificial neural network such as a multilayer perceptron network embedded within a wireless sensor network, of interest. This paper presents bounds and results of empirical study on the time, space and message complexity aspects of a wireless sensor network and multilayer perceptron neural network design. (C) 2014 The Authors. Published by Elsevier B.V.

关键词： distributed and parallel processing multilayer perceptron neural network wireless sensor network computational complexity communication complexity

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Empirical Analysis of Asymptotic Ensemble Learning for Big Data 16

Empirical Analysis of Asymptotic Ensemble Learning for Big D...

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3rd IEEE/ACM International Conference on Big Data Computing Applications and Technologies (BDCAT)

作者： Salloum, Salman Huang, Joshua Zhexue He, Yulin Shenzhen Univ Coll Comp Sci & Software Engn Big Data Inst Shenzhen 518060 Guangdong Peoples R China

ISBN: (纸本)9781450346177

In many application areas, data that is being generated and processed goes beyond the petabyte scale. Analyzing such an increasing massive volume of data faces computational, as well as, statistical challenges. In order to solve these challenges, distributed and parallel processing frameworks have been used for implementing scalable data analysis algorithms. Nevertheless, processing the whole big data set at one time may exceed the available computing resources and the time requirements for some applications. Thus, approximate approaches can be used to achieve asymptotic analysis results, especially when data analysis algorithms are amenable to an approximate result rather than an exact one. However, most approximation approaches require taking a random sample of the data which is a nontrivial task when working with big data sets. In this paper, we employ ensemble learning as an approach for asymptotic analysis using randomly selected subsets (i.e. data blocks) of a big data set. We propose an asymptotic ensemble learning framework which depends on block-based sampling rather than record-based sampling. In order to demonstrate the feasibility and performance of this framework, we present an empirical analysis on real data sets. In addition to the scalability advantage, the experimental results show that several blocks of a data set are enough to get approximately the same results as those from using the whole data set.

关键词： Big Data distributed and parallel processing Ensemble Learning Randomness Asymptotic Analysis

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distributed Fine-Grained Traffic Speed Prediction for Large-Scale Transportation Networks Based on Automatic LSTM Customization and Sharing 26th

Distributed Fine-Grained Traffic Speed Prediction for Large-...

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26th International Conference on parallel and distributed Computing (Euro-Par)

作者： Lee, Ming-Chang Lin, Jia-Chun Gran, Ernst Gunnar Norwegian Univ Sci & Technol Dept Informat Secur & Commun Technol N-2815 Gjovik Norway Simula Res Lab N-1364 Fornebu Norway

ISBN: (纸本)9783030576752;9783030576745

Short-term traffic speed prediction has been an important research topic in the past decade, and many approaches have been introduced. However, providing fine-grained, accurate, and efficient traffic-speed prediction for large-scale transportation networks where numerous traffic detectors are deployed has not been well studied. In this paper, we propose DistPre, which is a distributed fine-grained traffic speed prediction scheme for large-scale transportation networks. To achieve fine-grained and accurate traffic-speed prediction, DistPre customizes a Long Short-Term Memory (LSTM) model with an appropriate hyperparameter configuration for a detector. To make such a customization process efficient and applicable for large-scale transportation networks, DistPre conducts LSTM customization on a cluster of computation nodes and allows any trained LSTM model to be shared between different detectors. If a detector observes a similar traffic pattern to another one, DistPre directly shares the existing LSTM model between the two detectors rather than customizing an LSTM model per detector. Experiments based on traffic data collected from freeway I5-N in California are conducted to evaluate the performance of DistPre. The results demonstrate that DistPre provides time-efficient LSTM customization and accurate fine-grained traffic-speed prediction for large-scale transportation networks.

关键词： Hyperparameter tuning Lightweight LSTM Large-scale transportation networks Traffic speed prediction distributed and parallel processing The Nelder-Mead method

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distributed intelligent agents: A new approach to distributed artificial intelligence in robotic systems

Distributed intelligent agents: A new approach to distribute...

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International Conference on parallel and distributed processing Techniques and Applications

作者： Eggen, M Pitts, G Trinity Univ Dept Comp Sci San Antonio TX 78284 USA

ISBN: (纸本)1892512459

In this modern era of knowledge explosion, one of the most exciting research areas is Artificial Intelligence (AI). In this paper we investigate more specifically the theory and design of a new area of AI labeled distributed intelligent agents, whose development encompasses a broad variety of applications. From robots to lunar landers, this technology holds great promise for providing intelligence to systems, that has either required human interaction or whose programming was so complex and expensive as to render it useless. The design specifications given in this paper will provide a research and development framework for a marriage of distributed artificial intelligence, intelligent agents, and distributed parallel processing called distributed Intelligent Agents (DIA).

关键词： artificial intelligence intelligent agent forward chaining backward chaining distributed and parallel processing

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