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3rd EAI international conference on Multimedia Technology and Enhanced Learning, ICMTEL 2021

ISBN: (纸本)9783030825645

The proceedings contain 97 papers. The special focus in this conference is on Multimedia Technology and Enhanced Learning. The topics include: Research on the Method of Eliminating Duplicated Encrypted Data in Cloud Storage Based on Generated Countermeasure Network;design of distributed Hybrid Pipeline Multimedia Aided Scheduling System;intelligent Scheduling of distributed Displacement Pipeline Based on Hybrid Discrete Drosophila Optimization Algorithm;research on grid Planning Method of Distribution Network Based on Artificial Intelligence Technology;intelligent Monitoring Method for Backstage Data Security of Tourism Information Promotion Platform Based on Cloud computing;preface;research on Multithreaded Data Scheduling Control Method for Power Communication Based on Wireless Sensor;research on Industrial Product Modeling Design Method Based on Deep Learning;a Frequency Conversion Circuit for Piezoelectric Vibrating Energy Harvesting;an Adaptive Optimization Strict Reverse Navigation Algorithm for Ship Fine Alignment Process;research on Load Feature Extraction Method of Typical Users Based on Deep Learning;enterprise Financial Risk Early Warning System Based on Catastrophe Progression Method;research on Transportation Route Planning Method of Regional Logistics Network Based on Transfer Learning;simultaneous Localization of Multiple Defects in Software Testing Based on Reinforcement Learning;Design of Embedded Network Human Machine Interface Based on VR Technology;Design of Information Security System Based on JSP Technology and Reinforcement Model;sliding Mode Adaptive Control for Sensorless Permanent Magnet Synchronous Motor;recognition Method of Metal Material Pitting Defect Based on Visual Signal Processing;an Improved Detection Method of Safety Helmet Wearing Based on CenterNet;influence Maximization Based on True Threshold in Social Networks;arabic Question-Answering System Using Search Engine Techniques.

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Proceedings of EduHPC 2020: Workshop on Education for High Performance computing, Held in conjunction with SC 2020: The international conference for High Performance computing, Networking, Storage and Analysis

Proceedings of EduHPC 2020: Workshop on Education for High P...

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2020 IEEE/ACM Workshop on Education for High Performance computing, EduHPC 2020

ISBN: (纸本)9781665422963

The proceedings contain 8 papers. The topics discussed include: applying parallel and distributed computing curriculum to cyber security courses;peachy parallel assignments (EduHPC 2020);lightning talks of EduHPC 2020;extending *** as an online self-learning platform for compiler development;teaching software sustainability for high performance computing at ATPESC;towards generic parallel programming in computer science education with Kokkos;and trying to do it all in a single course: a surprisingly good idea.

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Decreasing the Learning Cost of Offline parallel Application Optimization Strategies 28

Decreasing the Learning Cost of Offline Parallel Application...

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28th Euromicro international conference on parallel, distributed and Network-Based Processing (PDP)

作者： Berned, Gustavo Rossi, Fabio D. Luizelli, Marcelo C. Beck, Antonio Carlos S. Lorenzon, Arthur F. Fed Inst Educ Sci & Technol Farroupilha Santa Maria RS Brazil Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Fed Univ Pampa Campus Alegrete Alegrete Brazil

ISBN: (纸本)9781728165820

Many parallel applications do not scale as the number of threads increases, which means that executing them with the maximum possible number of threads will not always deliver the best outcome in performance, energy consumption, or the tradeoff between both (represented by the energy-delay product - EDP). Given that, several strategies, online and offfine, have already been proposed to rightly tune the number of threads according to the application. While the former can capture some behaviors that can only be known at runtime, the latter do not impose any execution overhead and can use more efficient and costly algorithms. However, these learning algorithms in static strategics may take several hours, precluding their use or a smooth migration across different systems. In this scenario, we propose a generic methodology for such offline strategies to significantly decrease the learning time by inferring the execution behavior of parallel applications using smaller input sets than the ones used by the target applications. Through the execution of eighteen well-known benchmarks on two multicore processors, we show that our methodology is capable of converging to results that are very close to those that use the regular input set, but converging 84.7% faster, on average. We also show that such a strategy delivers better results than a dynamic one, presenting an EDP 7.7% lower, on average, when executing the applications with the number of threads found during learning. Finally, we also compare our learning methodology with an exhaustive search. It has an average learning cost (i.e., the time spent by our search algorithm to find the best configuration) of only 3.1% to optimize the EDP of the entire benchmark sett.

关键词： parallel computing runtime optimization systems thread-level parallelism exploitation energy-delay product

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Crypto-Trading. Rechargeable Token-Based Smart Energy Market Enabled by Blockchain and IoT Technology 25th

Crypto-Trading. Rechargeable Token-Based Smart Energy Market...

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25th international conference on parallel and distributed computing (Euro-Par)

作者： Marchesi, Michele Pinna, Andrea Pisu, Francesco Tonelli, Roberto Univ Cagliari Dept Math & Comp Sci Via Osped 72 Cagliari Italy Univ Cagliari Dept Elect & Elect Engn DIEE Piazza Armi Snc I-09123 Cagliari Italy

ISBN: (纸本)9783030483401;9783030483395

This paper presents the definition and the implementation of a decentralized system for the energy trading managed by blockchain technology. The system, called Crypto-Trading, is composed by three interacting subsystems: the trading platform, the blockchain, and the smart meters system. It is conceived to exploit the IoT technology of smart meters and the decentralization of smart contracts working inside the blockchain technology for managing exchange and trading of energy by means of specific tokens. The paper defines the system as a decentralized application, identifying system actors and describing user stories. Then provides the description of the use case concerning the rechargeable token, one of the main feature of our system, and its interaction with the other components of the system. Finally, the paper compares our implementation choice with other ongoing projects in the field of energy trading.

关键词： Energy trading Blockchain Smart contracts Smart grid BOSE

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parallel Multigrid Method on Multicore/Manycore Clusters 2020

Parallel Multigrid Method on Multicore/Manycore Clusters

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international conference on High Performance computing in Asia-Pacific Region (HPC Asia)

作者： Nakajima, Kengo Univ Tokyo Informat Technol Ctr Tokyo Japan

ISBN: (纸本)9781450376501

parallel multigrid method is expected to be a useful algorithm in exascale era because of its scalability. It is widely known that overhead of coarse grid solver in parallel multigrid method is significant, if the number of MPI processes is O(10(4)) or larger. The author proposed the hCGA for avoiding such overhead. Recently, the AM-hCGA, further optimized version of the hCGA, was proposed by the author, and its performance was evaluated on the Oakforest-PACS system (OFP) with IHK/McKernel at JCAHPC using up to 2,048 nodes of Intel Xeon Phi (Knights Landing). In the present work, developed method is also implemented to the Oakbridge-CX system (OBCX) at the University of Tokyo using up to 1,024 nodes (2,048 sockets) of Intel Xeon Platinum 8280 (Cascade Lake). Performance in weak and strong scaling are evaluated for application on 3D groundwater flow through heterogeneous porous media (pGW3D-FVM). The hCGA and the AM-hCGA provide excellent performance on both of OFP and OBCX with larger number of nodes. Especially, it achieved excellent performance in strong scaling on OBCX.

关键词： parallel Multigrid Multicore/Manycore Clusters Ill-conditioned Problems

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Towards On-the-fly Self-Adaptation of Stream parallel Patterns

Towards On-the-fly Self-Adaptation of Stream Parallel Patter...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Adriano Vogel Gabriele Mencagli Dalvan Griebler Marco Danelutto Luiz Gustavo Fernandes School of Technology Pontifical Catholic University of Rio Grande do Sul (PUCRS) Porto Alegre Brazil University of Pisa (UNIPI) Pisa Italy Laboratory of Advanced Research on Cloud Computing (LARCC) Tres de Maio Faculty (SETREM) Três de Maio Brazil

ISBN: (纸本)9781665414555;9781665447645

Stream processing applications compute streams of data and provide insightful results in a timely manner, where parallel computing is necessary for accelerating the application executions. Considering that these applications are becoming increasingly dynamic and long-running, a potential solution is to apply dynamic runtime changes. However, it is challenging for humans to continuously monitor and manually self-optimize the executions. In this paper, we propose self-adaptiveness of the parallel patterns used, enabling flexible on-the-fly adaptations. The proposed solution is evaluated with an existing programming framework and running experiments with a synthetic and a real-world application. The results show that the proposed solution is able to dynamically self-adapt to the most suitable parallel pattern configuration and achieve performance competitive with the best static cases. The feasibility of the proposed solution encourages future optimizations and other applicabilities.

关键词： Runtime Programming parallel processing Acceleration Optimization Monitoring

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Multi-level interactive unit commitment of regional power system

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international JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 125卷 106464-106464页

作者： Ji, Xingquan Zhang, Yumin Han, Xueshan Ye, Pingfeng Xu, Bo Yu, Yongjin Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China State Grid Energy Res Inst Co Beijing 102209 Peoples R China

distributed energy resource (DER) including wind power, solar energy and energy storage system (ESS) are connected to the active distribution network (ADN) in various combination ways, which makes the distribution network have interaction. As a bridge connecting the transmission grid (TG) and micro grid (MG), ADN breaks the traditional operation pattern of TG + ADN + MG. Considering the physical connections and shared information among TG, ADN and MG, this paper proposes a decentralized and parallel analytical target cascading (ATC) algorithm for interactive unit commitment (UC) implementation in regional power systems. To explore the synergistic ability of the TG + ADN + MG coping with uncertainties of DER, i.e., wind power, the primary and secondary frequency regulation of TG are implemented to cope with uncertainties. Furthermore, the distributional uncertainty of wind power is well modeled by data driven, which is proposed in our previous work (Zhang et al., 2019) [1]. Both the startup/shutdown variables of the thermal units and the variables in TG + ADN + MG are integrated into the multi-level interactive UC model to optimize simultaneously, thus realizing the optimal goal of the whole network, resources complementary and optimal allocation of power system. An improved 6-bus system is used to test the proposed model, the numerical results show that the proposed decentralized algorithm is a fully parallelized procedure. And it also demonstrates the parallel implementation significantly enhances computations efficiency of the ATC algorithm.

关键词： Analytical target cascading algorithm distributed parallel computing Primary and secondary frequency regulation Unit commitment

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PARMA-CC: parallel Multiphase Approximate Cluster Combining 20

PARMA-CC: Parallel Multiphase Approximate Cluster Combining

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21st international conference on distributed computing and Networking (ICDCN)

作者： Keramatian, Amir Gulisano, Vincenzo Papatriantafilou, Marina Tsigas, Philippas Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781450377515

Clustering is a common component in data analysis applications. Despite the extensive literature, the continuously increasing volumes of data produced by sensors (e.g. rates of several MB/s by 3D scanners such as LIDAR sensors), and the time-sensitivity of the applications leveraging the clustering outcomes (e.g. detecting critical situations, that are known to be accuracy-dependent), demand for novel approaches that respond faster while coping with large data sets. The latter is the challenge we address in this paper. We propose an algorithm, PARMA-CC, that complements existing density-based and distance-based clustering methods. PARMA-CC, is based on approximate, data parallel cluster combining, where parallel threads can compute summaries of clusters of data (sub)sets and, through combining, together construct a comprehensive summary of the sets of clusters. By approximating clusters with their respective geometrical summaries, our technique scales well with increased data volumes, and, by computing and efficiently combining the summaries in parallel, it enables latency improvements. PARMA-CC combines the summaries using special data structures that enable parallelism through in-place data processing. As we show in our analysis and evaluation, PARMA-CC can complement and outperform well-established methods, with significantly better scalability, while still providing highly accurate results in a variety of data sets, even with skewed data distributions, which cause the traditional approaches to exhibit their worst-case behaviour. In the paper we also describe how PARMA-CC can facilitate time-critical applications through appropriate use of the summaries.

关键词： approximate parallel clustering cluster combining model fitting

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Image Classification Algorithm for Transmission Line Defects Based on Dual-Channel Feature Fusion 6th

Image Classification Algorithm for Transmission Line Defects...

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6th international conference on Human Centered computing, HCC 2020

作者： Liao, Yongli He, Jinqiang Zhu, Dengjie Fan, Xujuan Zhang, Xiancong Electric Power Research Institute CSG Guangzhou Guangdong510663 China Guangzhou Power Supply Bureau of Guangdong Power Grid Limited Liabilities Company Guangzhou Guangdong510000 China

ISBN: (纸本)9783030706258

The power system is of great significance to the normal production of society and the daily life of the people, so regular inspection of transmission lines is essential. However, transmission lines are usually exposed to the outdoors, and the surrounding terrain and environment are complex, which may lead to problems such as structural aging and mechanical strength reduction, which in turn may lead to large area power outages and cause huge economic losses. In this paper, a two-channel feature fusion classification method is proposed to address the transmission line image classification problem. Using a two-channel parallel network structure, a neural network model is constructed to fuse the overall and local feature information, and then determine whether there are defects in the transmission line images. The experimental results show that the classification accuracy of the two-channel parallel convolutional neural network based on ResNet32 is 82.24% and 77.87% for the bird’s nest defect and insulator burst defect, respectively, on the actual transmission line image dataset, which exceeds the classification accuracy of other CNN models. This indicates that the classification accuracy can be effectively improved by fusing feature information. © 2021, Springer Nature Switzerland AG.

关键词： Losses

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parallel simulation of stochastic reward nets using theatre 21

Parallel simulation of stochastic reward nets using theatre

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Proceedings of the 2021 IEEE/ACM 25th international Symposium on distributed Simulation and Real Time Applications

作者： Franco Cicirelli Libero Nigro CNR - National Research Council of Italy Italy University of Calabria Italy

ISBN: (纸本)9781665433266

This work aims at the development of tools for supporting modelling and analysis of timed systems by Stochastic Reward Nets (SRN). In a first approach it was proposed and experimented a formal reduction of SRN over Timed Automata (TA) in the context of the Uppaal popular toolbox. The reduction has the merit to allow both exhaustive model checking of an SRN model, useful for the assessment of qualitative properties (e.g., absence of deadlocks, occurrence of particular event sequences etc.), and quantitative analysis through the statistical model checker, which is based on simulations. However, although Uppaal enabled formal reasoning on the semantics of SRN, its practical usage suffers of scalability problems, that is it can introduce severe limitations in time and space when studying complex models. To cope with this problem, this paper describes a Java implementation of the SRN operational core engine, using the lock-free and efficient Theatre actor system which permits the parallel simulation of large models. The realization can be used for functional property checking on an untimed version of a source SRN model, and quantitative estimation of measurables through simulations. The paper discusses the design and implementation of the core engine of SRN on top of Theatre, together with supported intuitive configuration process of an SRN model, and reports some experimental results using a scalable grid computing model. The experiments confirm Theatre/SRN are capable of exploiting the potential of modern multi-core machines and can deliver good execution performances on large models.

关键词： theatre Java stochastic reward nets actors high-performance computing performability analysis

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