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On the idle time control for multistep scheduling algorithm in parallel and distributed processing

On the idle time control for multistep scheduling algorithm ...

11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology

作者： Yamazaki, Hitoshi Konishi, Katsumi Sawada, Kenji Shin, Seiichi Univ Electrocommun Dept Mech Engn & Intelligent Syst 1-5-1 Choufugaoka Choufu Shi Tokyo 1828585 Japan Kogakuin Univ Dept Comp Sci Shinjuku Ku Tokyo 1638677 Japan

ISBN: (纸本)9781479929931

This paper considers a task scheduling problem in heterogeneous systems of processors and proposes a multistep scheduling algorithm for the problem. There are two important points for task scheduling problem. First, it is necessarily to consider optimality. Second, it is necessarily to reduce scheduling time which is calculation time for the problem. For first point, idle time and communication costs among processors are very important factors. Idle time is time which a processor does not work. Then, this paper proposes a multistep scheduling algorithm considered idle time and communication costs.

关键词： idle time 0-1 integer linear programming communication costs task scheduling task parallelism task graph graph clustering parallel and distributed processing

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Jobcast - parallel and distributed processing framework Data processing on a cloud style KVS database

Jobcast - Parallel and distributed processing framework Data...

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12th IEEE/IPSJ International Symposium on Applications and the Internet (SAINT) / IEEE Annual Signature Conference on Computer Software and Applications (COMPSAC)

作者： Nakagawa, Ikuo Nagami, Kenichi Intec Inc Adv Technol Res & Dev Inst Tokyo Japan

ISBN: (纸本)9780769547374;9781467320016

In this paper, we propose a new architecture for parallel and distributed processing framework, "Jobcast", which enables data processing on a cloud style KVS database. Nowadays, lots of KVS (as known as Key Value Store) systems exist which achieve high scalability for data spaces among a huge number of computers. Some of KVS implementations use "consistent hash" algorithm to identify the backend data node to store a pair of key and value. Jobcast also uses consistent hash algorithm for a distribution strategy and has a capability to store key and value pairs into huge number of computers as a KVS system. Furthermore, Jobcast also distributes "jobs" into data nodes for parallel and distributed processing. In this paper, we introduce a basic architecture of Jobcast and evaluate a data processing performance for a typical example.

关键词： component parallel and distributed processing cloud database NoSQL KVS

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Multistep Scheduling Algorithm for parallel and distributed processing with Communication Costs

Multistep Scheduling Algorithm for Parallel and Distributed ...

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39th Annual Conference of the IEEE Industrial-Electronics-Society (IECON)

作者： Yamazaki, Hitoshi Konishi, Katumi Shin, Seiichi Sawada, Kenji Univ Electrocommun Dept Mech Engn & Intelligent Syst 1-5-1 Choufugaoka Choufu Shi Tokyo 1828585 Japan Kogakuin Univ Dept Comp Sci Shinjuku Ku Tokyo 1638677 Japan

ISBN: (纸本)9781479902248

This paper considers a task scheduling problem for multicore CPUs and proposes a multistep scheduling algorithm. The existing scheduling algorithms formulated as 0-1 integer linear programming can consider optimality of a task scheduling. However, the existing scheduling algorithms cannot address complicated relations among tasks or cannot consider communication costs among processors. Then, first purpose is to propose a new scheduling algorithm with communication costs formulated as 0-1 integer linear programming. On the other hand, 0-1 integer linear programming is NP-complete and it takes long time to calculate scheduling result. Then, the second purpose is to decrease scheduling time. A solution decreasing scheduling time is a graph clustering which decomposes a large task graph into smaller sub-task graph (cluster). Also, it is important for parallel and distributed processing to find task parallelism in a task graph. Then, this paper proposes a clustering algorithm based on SCAN which is an algorithm for finding clusters in a network. The proposed algorithm can find task parallelism in a task graph. In numerical examples, the multistep scheduling algorithm is superior to the existing scheduling algorithm in terms of calculation time.

关键词： 0-1 integer linear programming communication costs task scheduling task parallelism task graph graph clustering parallel and distributed processing

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Visual programming support for graph-oriented parallel/distributed processing

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SOFTWARE-PRACTICE & EXPERIENCE 2005年第15期35卷 1409-1439页

作者： Chan, F Cao, JN Chan, ATS Zhang, K Hong Kong Polytech Univ Software Management & Dev Lab Dept Comp Hong Kong Hong Kong Peoples R China Univ Texas Dept Comp Sci Richardson TX 75083 USA

GOP is a graph-oriented programming model which aims at providing high-level abstractions for configuring and programming cooperative parallel processes. With GOP, the programmer can configure the logical structure of a parallel/distributed program by constructing a logical graph to represent the communication and synchronization between the local programs in a distributed processing environment. This paper describes a visual programming environment, called VisualGOP, for the design, coding, and execution of GOP programs. VisualGOP applies visual techniques to provide the programmer with automated and intelligent assistance throughout the program design and construction process. It provides a graphical interface with support for interactive graph drawing and editing, visual programming functions and automation facilities for program mapping and execution. VisualGOP is a generic programming environment independent of programming languages and platforms. GOP programs constructed under VisualGOP can run in heterogeneous parallel/distributed systems. Copyright (c) 2005 John Wiley & Sons,Ltd.

关键词： parallel and distributed processing programming environment graph-oriented model cluster computing

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On the idle time control for multistep scheduling algorithm in parallel and distributed processing

On the idle time control for multistep scheduling algorithm ...

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International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology

作者： Hitoshi Yamazaki Katsumi Konishi Kenji Sawada Seiichi Shin Department of Mechanical Engineering and Intelligent Systems the University of Electro-Communications Department of Computer Science Kogakuin University

ISBN: (纸本)9781479929948

关键词： Idle time 0-1 integer linear programming Communication costs Task scheduling Task parallelism Task graph Graph clustering parallel and distributed processing

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Multistep Scheduling Algorithm for parallel and distributed processing with Communication Costs

Multistep Scheduling Algorithm for Parallel and Distributed ...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Hitoshi Yamazaki Katumi Konishi Seiichi Shin Kenji Sawada Department of Mechanical Engineering and Intelligent Systems the University of Electro-Communications Department of Computer Science Kogakuin University

ISBN: (纸本)9781479902255

关键词： 0-1 integer linear programming Communication costs Task scheduling Task parallelism Task graph Graph clustering parallel and distributed processing task graphs distributed processing TASK SCHEDULING Communication Research parallel Lines Scheduling algorithms Integer linear programming Clustering algorithms

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Recent Developments in parallel and distributed Computing for Remotely Sensed Big Data processing

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PROCEEDINGS OF THE IEEE 2021年第8期109卷 1282-1305页

作者： Wu, Zebin Sun, Jin Zhang, Yi Wei, Zhihui Chanussot, Jocelyn Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Univ Grenoble Alpes INRIA Grenoble INP CNRSLJK F-38000 Grenoble France

This article gives a survey of state-of-the-art methods for processing remotely sensed big data and thoroughly investigates existing parallel implementations on diverse popular high-performance computing platforms. The pros/cons of these approaches are discussed in terms of capability, scalability, reliability, and ease of use. Among existing distributed computing platforms, cloud computing is currently the most promising solution to efficient and scalable processing of remotely sensed big data due to its advanced capabilities for high-performance and service-oriented computing. We further provide an in-depth analysis of state-of-the-art cloud implementations that seek for exploiting the parallelism of distributed processing of remotely sensed big data. In particular, we study a series of scheduling algorithms (GSs) aimed at distributing the computation load across multiple cloud computing resources in an optimized manner. We conduct a thorough review of different GSs and reveal the significance of employing scheduling strategies to fully exploit parallelism during the remotely sensed big data processing flow. We present a case study on large-scale remote sensing datasets to evaluate the parallel and distributed approaches and algorithms. Evaluation results demonstrate the advanced capabilities of cloud computing in processing remotely sensed big data and the improvements in computational efficiency obtained by employing scheduling strategies.

关键词： Remote sensing Big Data Cloud computing parallel processing distributed databases Sensors Processor scheduling Big data cloud computing parallel and distributed processing remote sensing task scheduling

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A load-balanced distributed parallel mining algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第3期37卷 2459-2464页

作者： Yu, Kun-Ming Zhou, Jiayi Hong, Tzung-Pei Zhou, Jia-Ling Chung Hua Univ Inst Engn & Sci Hsinchu 300 Taiwan Chung Hua Univ Dept Comp Sci & Informat Engn Hsinchu 300 Taiwan Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung 811 Taiwan Chung Hua Univ Dept Informat Management Hsinchu 300 Taiwan

Due to the exponential growth in worldwide information, companies have to deal with an ever growing amount of digital information. One of the most important challenges for data mining is quickly and correctly finding the relationship among data. The Apriori algorithm has been the most popular technique in finding frequent patterns. However, when applying this method, a database has to be scanned many times to calculate the counts of a huge number of candidate itemsets. parallel and distributed computing is an effective strategy for accelerating the mining process. In this paper, the distributed parallel Apriori (DPA) algorithm is proposed as a solution to this problem. In the proposed method, metadata are stored in the form of Transaction Identifiers (TIDs), such that only a single scan to the database is needed. The approach also takes the factor of itemset counts into consideration, thus generating a balanced workload among processors and reducing processor idle time. Experiments on a PC cluster with 16 computing nodes are also made to show the performance of the proposed approach and compare it with some other parallel mining algorithms. The experimental results show that the proposed approach outperforms the others, especially while the minimum supports are low. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： parallel and distributed processing Cluster computing Frequent patterns Association rules Data mining

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parallel and distributed Algorithms for Frequent Pattern Mining in Large Databases

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IETE TECHNICAL REVIEW 2009年第1期26卷 55-66页

作者： Tanbeer, Syed Khairuzzaman Ahmed, Chowdhury Farhan Jeong, Byeong-Soo Kyung Hee Univ Dept Comp Engn Youngin Si 446701 Kyunggi Do South Korea

Mining frequent patterns (FP) from large-scale databases has emerged as an important problem in the data mining and knowledge discovery research community. A significant number of parallel and distributed FP mining algorithms have been proposed, when the database is large and/or distributed. Among them, parallelization of the FP-growth algorithm using the FP-tree has been proved to be more efficient, when compared to the Apriori-based approaches. However, the FP-tree- based techniques suffer from two major limitations - multiple database scans requirement (i.e., high I/O cost) and huge communication overhead. Therefore, in this paper, we propose a novel tree structure, called PP-tree (parallel Pattern tree) that significantly reduces the I/O cost by capturing the database contents with a single scan and facilitates efficient FP-growth mining on it. Our parallel algorithm works independently at each local site and merges the locally generated global frequent patterns at the final stage, thereby reducing inter-processor communication overhead and getting a high degree of parallelism. Extensive experimental study on datasets of different types reflects that parallel and distributed FP mining with our PP-tree is highly efficient on large databases.

关键词： Data mining Frequent patterns Knowledge discovery Large-scale databases parallel and distributed processing Tree restructuring

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parallel and distributed neurocomputing with wireless sensor networks

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NEUROCOMPUTING 2016年第Part3期173卷 1169-1182页

作者： Serpen, Gursel Liu, Linqian Univ Toledo Elect Engn & Comp Sci Toledo OH 43606 USA

This paper proposes a novel hardware computing platform for fully parallel and distributed computation of artificial neural network (ANN) algorithms. The proposed idea entails leveraging the existing wireless sensor networks (WSN) technology to serve as a parallel and distributed hardware platform to implement computations for artificial neural network algorithms. Feasibility of the proposed neurocomputing architecture is demonstrated through a simulation-based case study, which uses Kohonen's self-organizing map as the neural network algorithm. MATLAB-based PROWLER, which is a protocol and application level simulator for wireless sensor networks, is employed for the simulation study. Findings demonstrated that the proposed neurocomputing architecture was able to train the SOM neural network with competitive accuracy values for the unsupervised clustering task. Conclusions of the simulation study suggest that the WSN-based neurocomputing architecture is a feasible alternative for realizing parallel and distributed computation of artificial neural network algorithms. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Neurocomputing Artificial neural network Wireless sensor network parallel and distributed processing parallel hardware Kohonen's SOM neural network

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