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30th International Spring Seminar on Electronics Technology

作者： Popa, Ilie Ghita, Constantin Univ Pitesti Elect & Comp Dept Pitesti 110040 Arges Romania

ISBN: (纸本)9781424412174

parallel and distributed computing infrastructure is increasingly being embraced in the context Of manufacturing applications, including real-time scheduling. The paper present the design and implementation of one such framework than can work on the Internet, with applications in automation (manufacturing). The architecture of distributed and parallel Compuring framework (DPAC) has the goal of harnessing the Internet's vast, growing computational capacity for ultra-large, coarse-grained parallel applications. DPAC idea is to bring together diverse, heterogeneous, geographically distributed computing environments in order to attack large-scale computing problems. In paper is describe an implementation and present case studies showing the effectiveness in solving complex combinatorial optimization problems in the context of manufacturing systems.

关键词： distributed and parallel computing applications programming interface client master worker scalability heterogeneity fault tolerance intelligent agents

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Application of distributed and parallel computing in Traffic Network Simulation

Application of Distributed and Parallel Computing in Traffic...

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2006年国际电子、工程及科学领域的分布式计算和应用学术研讨会

作者： Zhicai Juan Institute of Transportation Studies,Shanghai Jiao Tong University Shanghai,China Linjie Gao Institute of Transportation Studies,Shanghai Jiao Tong University Shanghai,China Hongfei Jia Transportation College,Jilin University Changchun,Jilin,China

Traffic network simulation describes movement behaviors and track of individual *** network is scale-up, the process takes more time and computing resources. Moreover,computing capacity of single processor computer cannot satisfy simulation requirement.A distributed and parallel computing approach to such a large-scale traffic system might be economical and *** paper designs framework of parallel traffic simulation system by using dornain decomposition method according to parallel characteristics of traffic network simulation,in other words, traffic network is partitioned to several sub-networks,and each processor of clusters is responsible for a different sub-network of the simulated region.A network-partition algorithm based on the number of vehicle is used to optimize load *** exchange mechanism between sub-networks is *** developed parallel simulation system is implemented in parallel computing platform based on Message Passing *** the results of numerical example,the proposed parallel simulation algorithm can improve speed and efficiency of traffic network *** algorithm provides a foundation for larger scale traffic network simulation and real-time applications.

关键词： Traffic Network Simulation distributed and parallel computing Domain Decomposition Network-Partition Algorithm

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Transform Blockchain into distributed parallel computing Architecture for Precision Medicine 38

Transform Blockchain into Distributed Parallel Computing Arc...

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38th IEEE International Conference on distributed computing Systems (ICDCS)

作者： Shae, Zonyin Tsai, Jeffrey J. P. Asia Univ Dept Comp Sci & Informat Engn Taichung Taiwan Asia Univ Dept Bioinformat & Med Engn Taichung Taiwan

ISBN: (纸本)9781538668719

This paper provides a vision and proposes mechanisms to transform the blockchain duplicated computing into distributed parallel computing architecture by transforming smart contract which features data driven from the ground up to support moving computing to native data strategy. This new distributed parallel computing architecture can be employed to build a large size of data set from various distributed hosted medical data sets which might consist of personal electronic medical record (EMR) and various medical data. This large medical data set will enable researchers to jump start the deep learning research for medical domain. distributed data management, distributed data sharing, and distributed learning are the core mechanisms in the new architecture. The required new researches and developments to employ Google federated learning and transfer learning algorithms in this new architecture are discussed. The approach and mechanism enabled by the new architecture is illustrated to build a real world evidence of clinical trial toward personal and precision medicine. Research issues and technical challenges are provided

关键词： blockchain distributed and parallel computing data ownership data integration health information exchange big data analytics deep learning clinical trial precision medicine

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Towards parallel and distributed computing on GPU for American Basket Option Pricing

Towards Parallel and Distributed Computing on GPU for Americ...

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4th IEEE International Conference on Cloud computing Technology and Science (CloudCom)

作者： Benguigui, Michael Baude, Francoise Univ Nice Sophia Antipolis CNRS I3S INRIA Sophia Antipolis Mediterranee Sophia Antipolis France

ISBN: (纸本)9781467345101;9781467345118

This article presents a GPU adaptation of a specific Monte Carlo and classification based method for pricing American basket options, due to Picazo. Some optimizations are exposed to get good performance of our parallel algorithm on GPU. In order to benefit from different GPU devices, a dynamic strategy of kernel calibration is proposed. Future work is geared towards the use of distributed computing infrastructures such as Grids and Clouds, equipped with GPUs, in order to benefit for even more parallelism in solving such computing intensive problem in mathematical finance.

关键词： component distributed and parallel computing Grid Cloud GPU OpenCL machine learning mathematical finance option pricing

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distributed parallel deep learning with a hybrid backpropagation-particle swarm optimization for community detection in large complex networks

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INFORMATION SCIENCES 2022年 600卷 94-117页

作者： Al-Andoli, Mohammed Nasser Tan, Shing Chiang Cheah, Wooi Ping Multimedia Univ Fac Informat Sci & Technol Jalan Ayer Keroh Lama Bukit Beruang Melaka Malaysia Univ Nottingham Ningbo China Fac Sci & Engn Sch Comp Sci Taikang East Rd Ningbo 315100 Peoples R China

In this paper, a parallel deep learning-based community detection method in large complex networks (CNs) is proposed. First, a CN partitioning method is employed to divide the CN into multiple chunks to improve the efficiency in terms of space and time complexities. Next, the method is integrated with two optimization algorithms: (1) backpropagation (BP), which optimizes deep learning locally within each local chunk of the CN;(2) particle swarm optimization (PSO), which is used to improve the BP optimization involving all CN chunks. PSO utilizes a multi-objective function to improve the effectiveness of the proposed method. In addition, a distributed environment is set up to conduct parallel optimization of the proposed method so that multi-local optimizations could be performed simultaneously. A set of 16 real-world CNs in a range from small to large size are used to verify the effectiveness and efficiency of the method in a benchmark study. The proposed method is implemented in multi-machines with central processing unit (CPU) and graphics processing unit (GPU) devices. The results reveal the effective role of the proposed deep learning with hybrid BP-PSO optimization in detecting communities in large CNs, which requires minimum execution time on both CPU and GPU devices.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Community detection Deep learning distributed and parallel computing Particle swarm optimization Backpropagation algorithm Complex networks

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distributed load balancing frequent colossal closed itemset mining algorithm for high dimensional dataset

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JOURNAL OF parallel AND distributed computing 2020年 144卷 136-152页

作者： Vanahalli, Manjunath K. Patil, Nagamma Natl Inst Technol Karnataka Dept Informat Technol Mangalore 575025 India

The focus of extracting colossal closed itemsets from high dimensional biological datasets has been great in recent times. A massive set of short and average sized mined itemsets do not confine complete and valuable information for decision making. But, the traditional itemset mining algorithms expend a gigantic measure of time in mining a massive set of short and average sized itemsets. The greater interest of research in the field of bioinformatics and the abundant data across the variety of domains paved the way for the generation of the high dimensional dataset. These datasets are depicted by an extensive number of features and a smaller number of rows. Colossal closed itemsets are very significant for numerous applications including the field of bioinformatics and are influential during the decision making. Extracting a huge amount of information and knowledge from the high dimensional dataset is a nontrivial task. The existing colossal closed itemsets mining algorithms for the high dimensional dataset are sequential and computationally expensive. distributed and parallel computing is a good strategy to overcome the inefficiency of the existing sequential algorithm. Balanced distributed parallel Frequent Colossal Closed Itemset Mining (BDPFCCIM) algorithm is designed for high dimensional datasets. An efficient closeness checking method to check the closeness of the rowset and an efficient pruning strategy to snip the row enumeration mining search space is enclosed with the proposed BDPFCCIM algorithm. The proposed BDPFCCIM algorithm is the first distributed load balancing algorithm to mine frequent colossal closed itemsets from high dimensional biological datasets. The experimental results demonstrate the efficient performance of the proposed BDPFCCIM algorithm in comparison with the state-of-the-art algorithms. (C) 2020 Elsevier Inc. All rights reserved.

关键词： distributed and parallel computing Load balancing Bioinformatics High dimensional datasets Closeness checking

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BIM-Centric Daylight Profiler for Simulation (BDP4SIM): A methodology for automated product model decomposition and recomposition for climate-based daylighting simulation

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BUILDING AND ENVIRONMENT 2012年 58卷 114-134页

作者： Welle, Benjamin Rogers, Zack Fischer, Martin Stanford Univ Dept Civil & Environm Engn Ctr Integrated Facil Engn CIFE Stanford CA 94305 USA Daylighting Innovat Lafayette CO 80026 USA

Flexible problem formulation is required for product model-based thermal analysis using multidisciplinary design optimization (MDO) environments for speed, accuracy, scalability, and cost-effectiveness in the Architecture, Engineering, and Construction (AEC) industry. The integration of daylighting simulation into an MDO process, however, presents several implementation challenges. In current practice, the process of an architect, engineer, or daylighting consultant to determine how to analyze a given building design for daylighting performance is frequently subjective, time-consuming, and inconsistent. Furthermore, long simulation time requirements for daylighting significantly hinder the realization of many benefits from MDO. The determination of which spaces in a building are sufficiently different to warrant an independent daylighting analysis is based primarily on building physics, building design criteria, and operating schedules (e.g. occupancy schedules). This characteristic of daylighting analysis creates the opportunity to develop intelligent mechanisms to automate the identification of the building spaces for analysis using performance-based methods, simulation of spatial results, and the scaling of spatial simulation results to whole building performance metrics in a fraction of the time it takes in current practice. Such methods would result in improved speed, accuracy, scalability, and cost-effectiveness for MDO- and non-MDO-based daylighting simulation. Currently, no such methods exist in literature or in practice. This paper fills these gaps by presenting a methodology for automated product model decomposition and recomposition for climate-based daylighting simulation using Radiance. The authors validate the research with the method's application to several test cases and a large federal office building industry case study. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Conceptual building design Multidisciplinary design optimization (MDO) Day lighting simulation Design decomposition distributed and parallel computing Process integration and design automation

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Performance analysis of a Linux PC cluster using a direct numerical simulation of fluid turbulence code

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE computing APPLICATIONS 2005年第4期19卷 365-374页

作者： Liu, CH Woo, CM Leung, DYC Univ Hong Kong Dept Mech Engn Hong Kong Hong Kong Peoples R China Univ Hong Kong Ctr Comp Hong Kong Hong Kong Peoples R China

In this paper, we have tested the parallel performance of an Intel Xeon-based Linux PC cluster using a finite element code for direct numerical simulation (DNS) of incompressible fluid turbulence. The parallel performance of the PC cluster, which used up to 64 2.8 GHz processors, was evaluated by comparing three scales of DNS trial runs consisting of 3.3, 5.8, and 10.1 million elements. Subroutines of different natures were contrasted to investigate the scalability of the DNS code. For DNS calculation of sufficiently large scale, the subroutines showed reasonable parallel efficiency. Doubling the number of processors reduced the CPU time by about 40%. Of particular interest was the CPU time required by the two subroutines handling interprocessor communication that was fairly constant within the range of processors tested. PC Linux clusters are thus affordable platforms, compared with more expensive supercomputers, to conduct large-scale scientific computing for fluid turbulence research.

关键词： computational fluid dynamics direct numerical simulation fluid turbulence Linux cluster distributed and parallel computing finite element method

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Latency-Aware Horizontal Computation Offloading for parallel Processing in Fog-Enabled IoT

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IEEE SYSTEMS JOURNAL 2022年第2期16卷 2537-2544页

作者： Deb, Pallav Kumar Misra, Sudip Mukherjee, Anandarup Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur 721302 W Bengal India

In this article, we propose a two-step distributed horizontal architecture for computation offloading in a fog-enabled Internet of Things environment-HD-Fog-to minimize the overall energy consumption, and latency while executing hard real-time applications. HD stands for the horizontal distribution of the tasks in the fog layer. Each sensor in the user devices independently captures data of varying formats. parallel execution on these data is possible based on its directed acyclic task graph (DATG), and the corresponding results facilitate the ease of decision-making. Toward this, in HD-Fog, the sensor nodes in user devices offload their tasks to a nearby fog node based on a greedy selection criterion. This fog node then further offloads the smaller subtasks, based on the DATG, among other fog nodes for parallel execution. Through extensive real-life metric-based emulation and comparison against traditional Fog and Cloud computing schemes, we observe that our approach 1) reduces the overall operational delays by 29% and 96%, and 2) offers promising speedup values. The proposed HD-Fog scheme also indicates a reduction in energy consumption by 30% compared to traditional fog computing schemes.

关键词： Task analysis Reliability Real-time systems Computer architecture Optimization Internet of Things Delays Computation offloading distributed and parallel computing Fog computing Internet of Things (IoT) queueing theory

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Delay-Optimal distributed Edge Computation Offloading With Correlated Computation and Communication Workloads

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IEEE TRANSACTIONS ON MOBILE computing 2023年第10期22卷 5846-5857页

作者： Chen, Mingyu Gong, Xiaowen Cao, Yang Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Hubei Peoples R China Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA

distributed edge computation offloading makes use of distributed wireless edge devices to perform offloaded computation in parallel, which can substantially reduce the computation time. In this article, we explore distributed edge computation offloading where the computation workloads of edge devices are correlated with their communication workloads. In particular, we study the fundamental problem of computation workload allocation and communication scheduling for minimizing the total completion time of the computation offloading. To solve this problem, we need to tackle several challenges due to the precedence constraints of computations and communications, the interference constraints of wireless edge devices, and the correlation between computation and communication workloads. We consider preemptive, half-preemptive, and non-preemptive networks for the formulated problem, respectively. For each setting, we first develop a simplified problem of computation allocation, based on which we then devise an efficient and feasible policy that can arbitrarily approach the optimal policy. For half-preemptive and non-preemptive networks, we also characterize the optimal communication orders. Our results provides useful insights for the computation-communication co-design of distributed edge computation offloading. We evaluate the proposed algorithms using simulation results, which corroborate the advantages of the algorithms.

关键词： Processor scheduling Wireless communication Performance evaluation Image edge detection Resource management Task analysis Scheduling Edge computation offloading distributed and parallel computing communication scheduling

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