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A novel parallel accelerated CRPF algorithm

APPLIED INTELLIGENCE 2020年第3期50卷 849-859页

作者： Wang, Jinhua Cao, Jie Li, Wei Yu, Ping Huang, Kaijie Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Gansu Peoples R China Lanzhou Univ Technol Key Lab Gansu Adv Control Ind Proc Lanzhou 730050 Gansu Peoples R China Lanzhou Univ Technol Natl Expt Teaching Ctr Elect & Control Engn Lanzhou 730050 Gansu Peoples R China

Particle filtering is one of the most important algorithms for solving state estimation of nonlinear systems and has been widely studied in many fields. However, due to the unknown complex noise in the actual system, its estimation performance is degraded. Moreover, when the number of particles increase, the real-time performance of the algorithm is poor. For these two problems above, this paper proposed a parallel acceleration CRPF (cost-reference particle filter) algorithm based on CUDA (Compute Unified Device Architecture). CRPF does not need known noise statistics in nonlinear system state estimation, which can reduce the influence of unknown noise on state estimation accuracy. Combined with GPU's (Graphics Processing Unit) multi-thread parallel computing capability, CRPF parallel acceleration can be realized. Since the data association can't be parallel resampled, all the particles are evenly distributed to multiple blocks, and resampling process can be parallelized by block parallel computing, so as to improve the speed of the algorithm. At the same time, in order to reduce the global particle performance degradation caused by block resampling, the particles with low probability mass in each block are optimized by using a portion of global high-quality particles. Through two sets of simulation experiments, it is proved that the proposed method has improved in estimation accuracy and the real-time performance has been improved significantly, which can provide a new idea for the practical application of nonlinear filtering method.

关键词： Particle filter CRPF GPU Accelerated parallel processing CUDA

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3D electrical impedance tomography system for real-time annular multiphase flow metering

3D electrical impedance tomography system for real-time annu...

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The 3rd International Conference on distributed Sensing and Intelligent systems (ICDSIS 2022)

作者： M. Tarek M. Meribout V. K. Tiwari Department of Electrical and Computer Engineering Khalifa University Abu Dhabi UAE

With the rapid progress of tomography systems, a need has emerged for innovation that can provide high accuracy measurements, fast acquisition rate, and low-cost robust systems for multi-phase flow and medical instruments. The main objective of this work is to design and implement a 3D Electrical Impedance Tomography (EIT) system to effectively monitor a multi-phase flow which consists of an annular flow comprising of an air-core of unknown diameter, surrounded by a liquid phase, such as oil and water mixture of high water-cut value. The system consists of an array of electrode sensors evenly distributed around the pipe confining the three-phase flow. Subsequently, a dedicated hardware module (GPU) is used to reconstruct these signals, captured from the sensors, into a 3D image of the flow by solving the forward-inverse problem. The annular flow is generated using a flow conditioner consisting of a swirl cage followed by a bluff body that generates vortices that push the high dense liquid phase to the outer side of the pipeline. The 3D EIT system is designed to accurately measure the gas liquid fraction and the liquid-liquid fraction and provide the 3D volume image of the flow. Hence, it can be utilized for pipeline and reservoirs characterization to achieve high efficiency, cost reduction, and safer production. The approach is to design a dedicated, highly parallel hardware architecture that can sustain the high computation complexity of the 3D EIT algorithm. Coincidentally, advanced software development tools are available to help effectively develop these parallel hardware algorithms. The main novelty is utilizing GPUs to outperform the existing real-time tomography hardware platforms, mainly based on Field Programmable Gate Array (FPGAs), which have the disadvantage of being less dense and feature lower precise arithmetic operations. Matrix multiplication and matrix inversion are considered the most intensive computation in Electric Impedance Tomography (EIT). T

关键词： computational complexity computerised instrumentation electric impedance imaging flow measurement flowmeters image reconstruction inverse problems matrix multiplication multi-threading parallel processing

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Secondary-Frequency and Voltage-Regulation Control of Multi-parallel Inverter Microgrid System

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ENERGIES 2022年第22期15卷 8533-8533页

作者： Dong, Jiawei Gong, Chunyang Bao, Jun Zhu, Lihua Hou, Yuanjun Wang, Zhixin Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Shanghai Univ Elect Power Coll Elect Engn Shanghai 200090 Peoples R China Shanghai Xilong Technol Co Ltd Shanghai 201109 Peoples R China Shanghai Chint Power Syst Co Ltd Shanghai 201614 Peoples R China Shanghai Da Yu Informat Tech Co Ltd Shanghai 200000 Peoples R China

As an important form of distributed renewable energy utilization and consumption, the multi-parallel inverter microgrid system works in both an isolated and grid-connected operation mode. Secondary-frequency and voltage-regulation control are very important in solving problems that appears in these systems, such as the distributed secondary-frequency regulation real-time scheme, voltage and reactive power balancing, and the secondary-frequency regulation control under the disturbances and unbalanced conditions of a microgrid system. This paper introduces key technologies related to these issues, such as the consensus algorithm and event-triggered technique, the dynamic and adaptive virtual impedance technique, and the robust and self-anti-disturbance control technique. Research and design methods such as small-signal state-space analysis, the Lyapunov function design method, the impedance analysis method, mu-synthesis design, and the LMI matrix design method are adopted to solve the issues in secondary-frequency regulation and voltage regulation. As the number of inverters increases, the structure of the microgrid becomes more and more complex. Suggestions and prospects for future research are provided to realize control with low-communication technology and a distributed scheme. Finally, for the case study, the droop-control model and primary frequency/voltage deviation of a multi-parallel inverter microgrid system is analyzed, and a state-space model of a multi-parallel inverter microgrid system with a droop-control loop is established. Then, the quantitative relationship between the primary frequency/voltage deviation and the active and reactive power output in the system is discussed. The methods and problems of centralized and decentralized secondary-frequency regulation methods, secondary-frequency regulation methods based on a consensus algorithm and an event-triggered mechanism, reactive power and voltage equalization, power distribution, and small-signal st

关键词： multi-parallel inverter microgrid secondary frequency and voltage regulation consensus algorithm event-triggered mechanism virtual impedance robust design

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Wireless distributed Edge Learning: How Many Edge Devices Do We Need?

Wireless Distributed Edge Learning: How Many Edge Devices Do...

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作者： Song, Jaeyoung Kountouris, Marios Department of Communication Systems Eurecom Biot06904 France

We consider distributed machine learning at the wireless edge, where a parameter server builds a global model with the help of multiple wireless edge devices that perform computations on local dataset partitions. Edge devices transmit the result of their computations (updates of current global model) to the server using a fixed rate and orthogonal multiple access over an error prone wireless channel. In case of a transmission error, the undelivered packet is retransmitted until successfully decoded at the receiver. Leveraging on the fundamental tradeoff between computation and communication in distributed systems, our aim is to derive how many edge devices are needed to minimize the average completion time while guaranteeing convergence. We provide upper and lower bounds for the average completion and we find a necessary condition for adding edge devices in two asymptotic regimes, namely the large dataset and the high accuracy regime. Conducted experiments on real datasets and numerical results confirm our analysis and substantiate our claim that the number of edge devices should be carefully selected for timely distributed edge learning. © 1983-2012 IEEE.

关键词： Mobile edge computing

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parallel co-location mining with MapReduce and NoSQL systems

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KNOWLEDGE AND INFORMATION systems 2020年第4期62卷 1433-1463页

作者： Yoo, Jin Soung Boulware, Douglas Kimmey, David Purdue Univ Ft Wayne Dept Comp Sci Ft Wayne IN 46805 USA Air Force Res Lab Rome Res Site New York NY USA

With the rapid growth of georeferenced data, large-scale data processing and analysis methods are needed for spatial big data. Spatial co-location pattern mining is an interesting and important issue in spatial data mining area which discovers the subsets of features whose objects are frequently located together in geographic proximity. There are several works for efficiently processing co-location pattern discovery;however, they may be insufficient for large dense spatial data because the mining task takes up a lot of processing time and memory. In this work, we leveraged the power of a modern distributed computing platform, Hadoop, and developed an algorithm (called ParColoc) for parallel co-location mining on the MapReduce framework. This study explored challenge issues in designing the parallel co-location mining algorithm and solved them with adopting a spatial declusteirng technique and a NoSQL system. We conducted an experimental evaluation with real-world data and synthetic data to examine the effectiveness of proposed methods. The experiment result shows that ParColoc is a promising method for parallel co-location mining in cloud computing environment.

关键词： Spatial data mining parallel co-location mining Cloud computing MapReduce NoSQL

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Coloring the Vertices of 9-pt and 27-pt Stencils with Intervals

Coloring the Vertices of 9-pt and 27-pt Stencils with Interv...

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International Symposium on parallel and distributed Processing (IPDPS)

作者： Dante Durrman Erik Saule Dept. Mathematics UNC Charlotte Charlotte NC Dept. Computer Science UNC Charlotte Charlotte NC

Graph coloring is commonly used to schedule computations on parallel systems. Given a good estimation of the computational requirement for each task, one can refine the model by adding a weight to each vertex. Instead of coloring each vertex with a single color, the problem is to color each vertex with an interval of colors. In this paper, we are interested in studying this problem for particular classes of graphs, namely stencil graphs. Stencil graphs appear naturally in the parallelisation of applications where the location of an object in a space affects the state of neighboring objects. Rectilinear decompositions of a space generate conflict graphs that are 9-pt stencils for 2D problems and 27-pt stencils for 3D problems. We show that the 5-pt stencil and 7-pt stencil relaxations of the problem can be solved in polynomial time. We prove that the decision problem on 27-pt stencil is NP-Complete. We discuss approximation algorithms with a ratio of 2 for the 9-pt stencil case, and 4 for the 27-pt stencil case. We identify two lower bounds for the problem that are used to design heuristics. We evaluate the effectiveness of several different algorithms experimentally on a set of real instances. Furthermore, these algorithms are integrated into a real application to demonstrate the soundness of the approach.

关键词： Schedules distributed processing Three-dimensional displays Runtime Heuristic algorithms Computational modeling Estimation

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A fast local community detection algorithm in complex networks

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION systems 2021年第6期24卷 1929-1955页

作者： Tang, Zhikang Tang, Yong Li, Chunying Cao, Jinli Chen, Guohua Lin, Ronghua South China Normal Univ Sch Comp Guangzhou 510631 Peoples R China GuangDong Polytech Normal Univ Sch Comp Sci Guangzhou 510665 Peoples R China LA TROBE Univ Sch Engn & Math Sci 00115M Melbourne Vic Australia South China Normal Univ Network Ctr Guangzhou 510631 Peoples R China

Considering the problems of communication overhead between nodes and the challenges brought by large-scale complex network in distributed system cluster management, we propose a local community detection algorithm in complex network called FLCDA (Fast Local Community Detection Algorithm). FLCDA can detect communities in a large-scale complex network on a single PC. The algorithm uses a parallel Sliding Windows (PSW) method to break a large-scale network into smaller sub-networks, and load sub-network into a PC memory. This method conforms to the local characteristics of the community. FLCDA first finds out disjoint Maximum Influence k-clique in each sub-network, and then assigns the same label and weight to all nodes in the same Maximum Influence k-clique. These labels and weights are used as seeds at the label propagation phase of FLCDA. During the labeling propagation phase, FLCDA applies a synchronous update strategy while removes meaningless labels after each iteration. When all node labels are updated, the update process will be ended. This method can reduce the calculation cost and improve the stability. The experiment results show that non-parameter FLCDA can self-adaptively detect communities on various scales and types of complex networks. By comparing with other algorithms, FLCDA has gained higher community detection accuracy and less running time. Therefore, FLCDA algorithm can be better adapted to community detection in real complex networks without prior knowledge.

关键词： Complex networks Local community detection Label propagation parallel sliding windows model Overlapping community

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Hierarchical Extended B-splines for Approximations on Sparse Grids 1

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5th workshop on Sparse Grids and Applications, SGA 2018

作者： Rehme, Michael F. Zimmer, Stefan Pflüger, Dirk Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart Germany

ISBN: (数字)9783030813628

ISBN: (纸本)9783030813611

B-splines on sparse grids are a versatile method for higher-dimensional approximation, especially in smooth settings or if gradient information is advantageous. With increasing dimension, grid points on the boundary become prohibitively expensive and have to be avoided. To compensate for the missing boundary points, the basis functions are typically modified according to natural boundary conditions. However, the resulting splines do not span the polynomial space anymore. In this work we introduce hierarchical extended B-splines that are based on the polynomial basis property. We apply them on spatially adaptive sparse grids and show improved convergence results for analytical objective functions and a real-world subsurface flow model. © 2021, Springer Nature Switzerland AG.

关键词： Interpolation

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Scalable and efficient graph traversal on high-throughput cluster

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CCF TRANSACTIONS ON HIGH PERFORMANCE COMPUTING 2021年第1期3卷 101-113页

作者： Fan, Dongrui Cao, Huawei Wang, Guobo Nie, Na Ye, Xiaochun Sun, Ninghui Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing Peoples R China State Key Lab Math Engn & Adv Comp Wuxi Jiangsu Peoples R China

Graph is one of the most important data structures in modern big data applications and is widely used in various fields. Among many graph algorithms, the Breadth-First Search (BFS) algorithm is a classic algorithm to solve the graph traversal problem and also the key kernel of Graph500 benchmark. On modern CPU architecture, the implementation of graph traversal on single-node systems has achieved significant improvement. However, due to the low resource utilization and high communications overhead, graph traversal on distributed clusters suffers from poor performance and energy inefficiency. High-throughput cluster (HTCs) adopt High-Throughput many-core architecture, which has the characteristics of high concurrency, strong real-time, and low-power consumption. In this work, we propose several techniques, including asynchronous virtual ring method, thread caching scheme and vertex ID reordering to solve above problems and improve BFS performance on HTCs. We systematically evaluate optimized BFS algorithm and achieve 249.74 giga-traversed edges per second (GTEPS) on 72 nodes (2880 cores) HTCs. Compared with results on Graph500 list, the optimized algorithm achieves the highest node efficiency under the same cluster scale and the performance shows weakly linear scalability as the number of cluster nodes increases. With regard to efficiency, the average performance on HTCs is 3.47 GTEPS/node, which is the best among CPU-based distributed systems on the November 2019 Graph500 list.

关键词： BFS parallel computing Graph500 Graph traversal

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Intelligent Cloud Data Indexing and Retrieval Through Random Forest Algorithms

Intelligent Cloud Data Indexing and Retrieval Through Random...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： K. Mohan Raj A. Karthikeyan Department of CSE. Mahendra Engineering College Namakkal Tamil Nadu India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Another significant research issue is that of data indexing and retrieval in cloud computing context given that the virtues of velocity, variety, and volume are escalating pervasively. Indexing techniques have in the past proved difficult for real-time cloud applications requirements. This research focuses on the utilization of developed Random Forest algorithms as a new conception of smart cloud data indexing and search. The reported method exploits the strict parallelism and scaling properties of Random Forest algorithms to design a distributed and robust indexing scheme that can efficiently process lager cloud data. During the indexing phase, feature extraction takes place followed by partitioning of the data, construction of several decision trees for search capability. For data retrieval experiments performed on benchmark cloud data sets, the results indicate that the proposed Random Forest-based indexing achieves average accuracy equal to 92.4%, the efficiency is 15-20% higher than traditional indexing methods. The method also stands high on parameters of data skew and node failure for providing reliable performance with frequent change in cloud-based infrastructure. The introduction of Random Forest algorithms into cloud data indexing serves as a boiling prospect in creating scalability and efficiency in the discovery process amidst the growing big data problem. The developments proposed within this research point to the future developments regarding intelligent cloud data management and create new opportunities for this topic within the field of cloud computing.

关键词： Cloud computing Scalability Signal processing algorithms Signal processing parallel processing real-time systems Partitioning algorithms Reliability Random forests Indexing

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