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9th international conference on Environmental and Engineering Geophysics, ICEEG 2020

作者： Wang, Zhiyang Bai, Wenlei Li, Youming Liu, Hong Chen, Chaopu College of Information Science and Technology Beijing University of Chemical Technology Beijing China Key Laboratory of Petroleum Resources Research Institute of Geology and Geophysics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

The staggered-grid finite-difference (SGFD) method is one of the most popular means used in seismic numerical modelling because of its computational efficiency and it is easy to implement. However, it will lead to serious numerical errors while using coarse grids or wavelet with high-frequency. In this work, we proposed an optimal spatial SGFD method based on a new improved particle swarm optimization (PSO) algorithm. Then the dispersion of numerical solution of the first-order spatial derivatives is analyzed. Meanwhile, numerical modelling of elastic wave equations and elastic wave equations including rotational deformation are performed with the optimal spatial SGFD method based on the improved PSO algorithm. Numerical dispersion analysis and numerical modeling results indicate that the optimal spatial SGFD method based on the improved PSO algorithm has high modeling accuracy and can efficiently suppress the numerical dispersion. © Published under licence by IOP Publishing Ltd.

关键词： Numerical models

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Spara: An Energy-Efficient ReRAM-Based Accelerator for Sparse Graph Analytics Applications 34

Spara: An Energy-Efficient ReRAM-Based Accelerator for Spars...

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34th IEEE international parallel and distributed Processing Symposium (IPDPS)

作者： Zheng, Long Zhao, Jieshan Huang, Yu Wang, Qinggang Zeng, Zhen Xue, Jingling Liao, Xiaofei Jin, Hai Huazhong Univ Sci & Technol Cluster & Grid Comp Lab Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst Lab Wuhan Peoples R China UNSW Sydney Sydney NSW Australia

ISBN: (纸本)9781728168760

Resistive random access memory (ReRAM) addresses the high memory bandwidth requirement challenge of graph analytics by integrating the computing logic in the memory. Due to the matrix-structured crossbar architecture, existing ReRAM-based accelerators, when handling real-world graphs that often have the skewed degree distribution, suffer from the severe sparsity problem arising from zero fillings and activation nondeterminism, incurring substantial ineffectual computations. In this paper, we observe that the sparsity sources lie in the consecutive mapping of source and destination vertex index onto the wordline and bitline of a crossbar. Although exhaustive graph reordering improves the sparsity-induced inefficiency, its totally-random (source and destination) vertex mapping leads to expensive overheads. This work exploits the insight in a mid-point vertex mapping with the random wordlines and consecutive bitlines. A cost-effective preprocessing is proposed to exploit the insight by rapidly exploring the crossbar-fit vertex reorderings but ignores the sparsity arising from activation dynamics. We present a novel ReRAM-based graph analytics accelerator, named Spara, which can maximize the workload density of crossbars dynamically by using a tightly-coupled bank parallel architecture further proposed. Results on real-world and synthesized graphs show that Spara outperforms GraphR and GraphSAR by 8.21x and 5.01x in terms of performance, and by 8.97x and 5.68x in terms of energy savings (on average), while incurring a reasonable (<9.98%) pre-processing overhead.

关键词： ReRAM graph analytics energy efficiency

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Research on the Implementation Plan of the Intelligent Substation Based on the Micro System 15th

Research on the Implementation Plan of the Intelligent Subst...

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15th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th international conference on Harmony Search, Soft computing and Applications, ICHSA 2019

作者： Yin, Peng Li, Hailong Liu, Jinhui Liu, Xiaolong Wang, Zhaohua Department of Construction State Grid Liaoning Electric Power Supply Co. Ltd. Shenyang China

ISBN: (纸本)9783030325909

Micro network is one of the important forms of distributed generation, either through micro network distribution network with large power grid and run, forming a large power grid and the joint operation of power grid system, can independently to provide local electricity demand. This pattern has greatly increased the load side of the power supply flexibility, reliability. Energy securities become a serious problem facing in sustainable development of our country. In the aspect of energy utilization in China, also belong to high energy consumption, low efficiency of management mode, building energy consumption accounted for 25% of the total energy consumption in our country. In the future, as the increase of construction area, the building energy consumption could rise to 35%. Adherence to save energy and protect the environment is the basic state policy of our country, the relationship between people’s vital interests and the survival and development of the Chinese nation. In micro network interconnection and the isolated net running related topic research and display function, construction based on the network of the distributed power supply and micro network demonstration projects in line with the SGC smart grid development planning and strategic direction, is also actively responded to the call of the country’s energy conservation and emissions reduction. © 2020, Springer Nature Switzerland AG.

关键词： Energy utilization

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Integration of Photovoltaic Panels with DC grid Using High Gain DC-DC Converter 6

Integration of Photovoltaic Panels with DC Grid Using High G...

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6th IEEE international conference on Electronics, computing and Communication Technologies, CONECCT 2020

作者： Agrawal, Shubham Atmanandmaya Subba, Reddy B. Umanand, L. Bengaluru India

ISBN: (纸本)9781728168289

Integration of solar photovoltaic (PV) with AC grid is gaining more popular in distributed generation. In future, DC grid is likely to play a major role in the distribution system. With this in view the present investigation highlights the integration of solar PV with DC grid. High gain non-isolated DC-DC converter is used to connect two solar PV panels of lower voltage level with grid of higher voltage level. Calculation of gain is done with non-idealities later small signal analysis is conducted in order to get the closed loop Proportional Integrator (PI) controller. Maximum Power Point Tracking (MPPT) is performed to extract the maximum power from the solar PV panels. The MPPT along with closed loop PI controller gives appropriate duty cycle for the converter switches. Simulation of overall system is performed, related waveforms of the current and voltages at different nodes are presented. Some important results obtained from the study are discussed and presented. © 2020 IEEE.

关键词： Boost converter

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distributed Deep Learning on Heterogeneous computing Resources Using Gossip Communication 12th

Distributed Deep Learning on Heterogeneous Computing Resourc...

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12th international conference on Large-Scale Scientific Computations (LSSC)

作者： Georgiev, Dobromir Gurov, Todor Bulgarian Acad Sci Inst Informat & Commun Technol Acad G Bonchev StBlock 25A Sofia 1113 Bulgaria

ISBN: (纸本)9783030410322;9783030410315

With the increased usage of deep neural networks, their structures have naturally evolved, increasing in size and complexity. With currently used networks often containing millions of parameters, and hundreds of layers, there have been many attempts to leverage the capabilities of various high-performance computing architectures. Most approaches are focused on either using parameter servers or a fixed communication network, or exploiting particular capabilities of specific computational resources. However, few experiments have been made under relaxed communication consistency requirements and using a dynamic adaptive way of exchanging information. Gossip communication is a peer-to-peer communication approach, that can minimize the overall data traffic between computational agents, by providing a weaker guarantee on data consistency - eventual consistency. In this paper, we present a framework for gossip-based communication, suitable for heterogeneous computing resources, and apply it to the problem of parallel deep learning, using artificial neural networks. We present different approaches to gossip-based communication in a heterogeneous computing environment, consisting of CPUs and MIC-based co-processors, and implement gossiping via both shared and distributed memory. We also provide a simplistic approach to load balancing in a heterogeneous computing environment, that proves efficient for the case of parallel deep neural network training. Further, we explore several approaches to communication exchange and resource allocation, when considering parallel deep learning using heterogeneous computing resources, and evaluate their effect on the convergence of the distributed neural network.

关键词： Deep learning Gossip communication Heterogeneous high-performance computing

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Simulation and Analysis of Virtual Oscillator Control for parallel Connected Single Phase Inverters 6

Simulation and Analysis of Virtual Oscillator Control for Pa...

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6th IEEE international conference on Electronics, computing and Communication Technologies, CONECCT 2020

作者： Ashwin, K Umanand, L. Subba Reddy, B Icer Indian Institute of Science Bangalore560012 India

ISBN: (纸本)9781728168289

With the increased popularity in the paralleling of voltage sources and inverters, there is a need for simpler yet effective ways of controlling such a system. In this regard a new control scheme using Virtual Oscillator Control (VOC) has been developed. The paper presents some important simulation results conducted on the performance of VOC under stand alone and grid connected applicationsThis paper also proposes a frequency compensation scheme to tackle sub-harmonics in the output voltage arising due to frequency variation in grid-connected mode. The validity of the new scheme is verified using simulation results. © 2020 IEEE.

关键词： Electric inverters

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An Efficient Selection Procedure with an Enhanced Load-Balancing Scheme for gridSim 3rd

An Efficient Selection Procedure with an Enhanced Load-Balan...

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3rd international conference on Advanced computing and Intelligent Engineering, ICACIE 2018

作者： Patel, Deepak Kumar Tripathy, Chitaranjan Bhubaneswar KhordhaOdisha751030 India Department of Computer Science and Engineering Veer Surendra Sai University of Technology Burla SambalpurOdisha768018 India

ISBN: (纸本)9789811514821

A computational grid is a distributed network of heterogeneous resources dedicated to execute the user-defined jobs in a more efficient manner. Therefore, a load-balancing scheme is always required in a grid computing environment for better utilization of the resources and time-efficient execution of the jobs. In this paper, the proposed scheme LBDC provides an efficient selection procedure for better scheduling of jobs among the computational resources so that every resource will remain balanced, and every job will be executed in a time-efficient manner. The proposed scheme LBDC delivers better results in terms of response time as compared to previous load-balancing schemes on gridSim. © 2020, Springer Nature Singapore Pte Ltd.

关键词： grid computing

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Eco-FL: Adaptive Federated Learning with Efficient Edge Collaborative Pipeline Training 22

Eco-FL: Adaptive Federated Learning with Efficient Edge Coll...

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Proceedings of the 51st international conference on parallel Processing

作者： Shengyuan Ye Liekang Zeng Qiong Wu Ke Luo Qingze Fang Xu Chen Sun Yat-sen University China

ISBN: (纸本)9781450397339

Federated Learning (FL) has been a promising paradigm in distributed machine learning that enables in-situ model training and global model aggregation. While it can well preserve private data for end users, to apply it efficiently on IoT devices yet suffer from their inherent variants: their available computing resources are typically constrained, heterogeneous, and changing dynamically. Existing works deploy FL on IoT devices by pruning a sparse model or adopting a tiny counterpart, which alleviates the workload but may have negative impacts on model accuracy. To address these issues, we propose Eco-FL, a novel Edge Collaborative pipeline based Federated Learning framework. On the client side, each IoT device collaborates with trusted available devices in proximity to perform pipeline training, enabling local training acceleration with efficient augmented resource orchestration. On the server side, Eco-FL adopts a novel grouping-based hierarchical architecture that combines synchronous intra-group aggregation and asynchronous inter-group aggregation, where a heterogeneity-aware dynamic grouping strategy that jointly considers response latency and data distribution is developed. To tackle the resource fluctuation during the runtime, Eco-FL further applies an adaptive scheduling policy to judiciously adjust workload allocation and client grouping at different levels. Extensive experimental results using both prototype and simulation show that, compared to state-of-the-art methods, Eco-FL can upgrade the training accuracy by up to 26.3%, reduce the local training time by up to 61.5%, and improve the local training throughput by up to 2.6 ×.

关键词： edge intelligence pipeline parallelism Federated learning parallel processing

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Multi-energy flow calculation based on energy cell and parallel distributed computation

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international JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 131卷 107147-107147页

作者： Yu, Jianshu Yang, Dechang Chen, Zechun China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China

To deal with the deep penetration of renewable energy in the power grid and energy coupling in the distribution and consumption part, improve energy efficiency, and construct the regional integrated energy system (IES) with multiple self-sufficient cells, energy cell has attracted widespread interest. However, the different management systems, diversified energy features, and information barriers increase the difficulties of unified energy flow calculation of IES. Based on the theory, this paper proposes a parallel distributed computing method for multiregion IES with electricity network being considered as a dividing surface. First of all, the basic principle of energy cell is proposed and the large-scale IES is divided into several energy cells according to the energy balance ability and geographic location. Then virtual nodes are added at the boundaries of the energy cells and the adjustment factors are introduced to ensure system convergence. Moreover, the extended Jacobian matrix was constructed to calculate muti-energy flow (MEF) in a single energy cell and decoupled energy cells can be computed in parallel through the limited information exchange of virtual nodes. Finally, two cases are utilized to verify the performances of the proposed energy method. Comparing with the integrated method and the decomposed method, the proposed method in this paper has better abilities in calculation efficiency and accuracy.

关键词： Multi-energy flow Integrated energy system (IES) Combined electricity-heat-natural gas system Energy cell distributed calculation

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Optimum parallelism in Spark Framework on Hadoop YARN for Maximum Cluster Resource Utilization 1st

Optimum Parallelism in Spark Framework on Hadoop YARN for Ma...

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1st international conference on Sustainable Technologies for Computational Intelligence (ICTSCI)

作者： Janardhanan, P. S. Samuel, Philip SunTec Business Solut Pvt Ltd TechnoPk Trivandrum Kerala India Cochin Univ Sci & Technol Dept Comp Sci Kochi Kerala India

ISBN: (纸本)9789811500299;9789811500282

Spark iswidely used as a distributed computing framework for in-memory parallel processing. It implements distributed computing by splitting the jobs into tasks and deploying them on executors on the nodes of a cluster. Executors are JVMs with dedicated allocation of CPU cores and memory. The number of tasks depends on the partitions of input data. Depending on the number of CPU cores allocated to executors, one or more cores get allocated to one task. Tasks run as independent threads on executors hosted on JVMs dedicated exclusively to the executor. One or more executors are deployed on the nodes of the cluster depending on the resource availability. The performance advantage provided by distributed computing on Spark framework depends on the level of parallelism configured at 3 levels, namely node level, executor level, and task level. The parallelism at each of these levels should be configured to fully utilize the available computing resources. This paper recommends optimum parallelism configuration for Apache Spark framework deployed on Hadoop YARN cluster. The recommendations are based on the results of the experiments conducted to evaluate the dependency of parallelism at each of these levels on the performance of Spark applications. For the purpose of the evaluation, a CPU-intensive job and an I/O-intensive job are used. The performance is measured by varying the parallelism at each of the 3 levels. The results presented in this paper help Spark users in selecting optimum parallelism at each of these levels for achieving maximum performance for Spark jobs by maximum resource utilization.

关键词： distributed computing Apache Spark Hadoop YARN SparkBench Spark configuration Multi-level parallelism Resource optimization

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