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Towards Optimal Matrix partitioning for Data Parallel Computing on a Hybrid Heterogeneous Server

IEEE ACCESS 2021年 9卷 17229-17244页

作者： Malik, Tania Lastovetsky, Alexey Univ Coll Dublin Sch Comp Sci Dublin D04 V1W8 4 Ireland

Optimal partitioning of a square computational domain over several heterogeneous processors, balancing the load of the processors and minimizing the inter-processor communication cost, is crucial for data parallel dense linear algebra and other applications having similar communication pattern on modern hybrid servers. Although a solution has been found for two processors, the cases of three and more processors are still open. The state of-the-art solution for three processors uses an approximation communication cost function which fails to accurately account for the total amount of data moved between processors, leaving thus the question of its global optimality unanswered. In this work, we formulate and solve a mathematical problem of optimal partitioning a real-valued square over three heterogeneous processors using a new cost function, which accurately accounts for the total amount of data communicated between processors. We also develop an original method for accurate experimental evaluation of the communication time of data movement between memories of the compute devices in the hybrid platform during the execution of data parallel applications. We successfully use this method in the experimental validation of our mathematical results. Finally, we propose a communication energy model predicting the dynamic energy consumption of data movement between processors and experimentally validate its accuracy. This model predicts, and the experiments confirm, that the performance-optimal partition is not necessarily energy optimal.

关键词： Program processors Shape partitioning algorithms Computational modeling Approximation algorithms Mathematical model Data models Data partitioning communication optimization non-rectangular partitioning matrix multiplication heterogeneous computing performance model data parallelism energy model energy of communication

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Geometric partitioning Mode in Versatile Video Coding: Algorithm Review and Analysis

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第9期31卷 3603-3617页

作者： Gao, Han Esenlik, Semih Alshina, Elena Steinbach, Eckehard Huawei Technol D-80992 Munich Germany Tech Univ Munich Dept Elect & Comp Engn D-80333 Munich Germany

This paper presents an overview of the geometric partitioning mode (GPM) algorithm that is a part of the most recent Versatile Video Coding (VVC) standard. The GPM algorithm aims to increase the partitioning precision of moving objects using non-rectangular and asymmetric rectangular partitions on top of the conventional rectangular block partitioning structure of VVC. Novel features of GPM contributing to the increase in coding efficiency and the reduction in encoder and decoder complexity are detailed and analyzed in this paper. Evaluated with VVC test model version 8.0 under the joint video experts team common test conditions, experimental results show that the presented GPM algorithm provides luma Bjontegaard Delta rate reduction of 0.70% for random access and of 1.55% for low delay with B slices configurations, with roughly 3% to 5% additional encoding time and negligible decoder runtime change. Furthermore, as GPM provides more precise partitions for the boundaries of the moving objects, an improvement of visual quality is seen in GPM coded sequences.

关键词： Encoding Decoding partitioning algorithms Complexity theory Video coding Standards Motion segmentation Geometric partitioning mode (GPM) interpicture prediction joint video experts team (JVET) partitioning versatile video coding (VVC) video compression

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Latency Minimization of Reverse Offloading in Vehicular Edge Computing

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第5期71卷 5343-5357页

作者： Feng, Weiyang Zhang, Ning Li, Shichao Lin, Siyu Ning, Ruirui Yang, Shuzhong Gao, Yuan Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China China Acad Railway Sci Beijing 100081 Peoples R China

Cooperative Vehicle-Infrastructure System (CVIS) can provide innovative services for traffic management and enable trips to be safer, more coordinated, and smarter. In the CVIS, the vehicles upload crowd sensing data to the Vehicular Edge Computing (VEC) server for quick data fusion and informed decision-making. However, with the ever-increasing number of vehicles, the VEC server cannot undertake massive computation-intensive tasks due to the limited edge computing capabilities. In this paper, we propose a reverse offloading framework that can fully utilize the vehicular computation resource to relieve the burden of the VEC server and further reduce the system latency. Under the proposed offloading framework, the binary reverse offloading (BRO) and partial reverse offloading (PRO) strategies are designed for two types of tasks, i.e., non-partitioned tasks and partitioned tasks. We formulate the system latency minimization problem by optimizing reverse offloading decisions, and the communication and computation resources allocation. Due to the non-convex and existing variables coupling, the original problem is transformed into the equivalent weighted-sum optimization problem. Based on the alternative optimization, we decouple the weighted-sum optimization problems into the two subproblems, and the closed-form expressions of transmission power and computation frequency of vehicles and RSU are derived. Low complexity greedy based efficient searching (GES) algorithm and joint alternative optimization based bi-section searching (JAOBSS) algorithm are proposed for BRO and PRO strategies, respectively. The algorithm complexity and performance bounds are analyzed. Simulation results show that the proposed GES algorithm can achieve optimal performance with low complexity. Besides, the proposed GES and JAOBSS algorithms can significantly improve the performance compared with other baseline schemes by 6.14% and 13.46% at least.

关键词： Task analysis Servers Resource management Sensors Edge computing Optimization partitioning algorithms Binary partial reverse offloading latency minimization resource allocation vehicular edge computing

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Block-Sparse Signal Recovery via General Total Variation Regularized Sparse Bayesian Learning

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 1056-1071页

作者： Sant, Aditya Leinonen, Markus Rao, Bhaskar D. Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA Univ Oulu Ctr Wireless Commun Radio Technol Oulu 90570 Finland

One of the main challenges in block-sparse signal recovery, as encountered in, e.g., multi-antenna mmWave channel models, is block-patterned estimation without knowledge of block sizes and boundaries. We propose a novel Sparse Bayesian Learning (SBL) method for block-sparse signal recovery under unknown block patterns. Contrary to conventional approaches that impose block-promoting regularization on the signal components, we apply two classes of hyperparameter regularizers for the SBL cost function, inspired by total variation (TV) denoising. The first class relies on a conventional TV difference unit and allows performing the SBL inference iteratively through a set of convex optimization problems, enabling a flexible choice of numerical solvers. The second class incorporates a region-aware TV penalty to penalize the signal and zero blocks in a dissimilar manner, enhancing the performance. We derive an alternating optimization algorithm based on expectation-maximization to perform the SBL inference through computationally efficient parallel updates for both the regularizer classes. The numerical results show that the proposed TV-regularized SBL algorithm is robust to the nature of the block structure and is capable of recovering signals with both block-patterned and isolated components, proving effective for various signal recovery systems.

关键词： Signal processing algorithms TV Estimation Inference algorithms Bayes methods partitioning algorithms Cost function Compressed sensing block-sparsity sparse Bayesian learning total variation expectation-maximization majorization-minimization cyclic optimization

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An Ensemble Learning Algorithm Based on Density Peaks Clustering and Fitness for Imbalanced Data

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IEEE ACCESS 2022年 10卷 116120-116128页

作者： Xu, Hui Liu, Qicheng Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China

In view of the low classification accuracy of the minority class in imbalanced data, an algorithm called DPF-EL (density peaks and fitness combined with ensemble learning) based on density peaks clustering and fitness is proposed. Firstly, this method uses the density peaks clustering algorithm to divide the majority class into different sub-clusters, the local density calculated in the clustering process is used to assign weights to each sub-cluster, and the number of under-sampling is determined by the weights. Secondly, the concept of fitness is introduced into the sub-clusters, the selection probability of the samples is calculated according to the size of their fitness, and the majority class is under-sampled based on the selection probability. Finally, combined with boosting algorithm, iterative training is performed on the balanced data set. Experimental tests were conducted with KEEL imbalanced data sets, and the experimental results show that the performance of DPF-EL algorithm is better than other algorithms, which indicates the feasibility of the proposed algorithm.

关键词： Classification algorithms Clustering algorithms Probability Ensemble learning partitioning algorithms Machine learning algorithms Decision trees Imbalanced data density peaks clustering fitness under-sampling classification

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Checking Phylogenetics Decisiveness in Theory and in Practice

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第6期19卷 3307-3316页

作者： Parvini, Ghazaleh Braught, Katherine Fernandez-Baca, David Univ Massachusetts Coll Informat & Comp Sci Amherst MA 01003 USA Univ Illinois Dept Comp Sci Urbana IL 61801 USA Iowa State Univ Dept Comp Sci Ames IA 50011 USA

Suppose we aim to build a phylogeny for a set of taxa X using information from a collection of loci, where each locus offers information for only a fraction of the taxa. The question is whether, based solely on the pattern of data availability, called a taxon coverage pattern, one can determine if the data suffices to construct a reliable phylogeny. The problem can be expressed combinatorially as follows. Let us call a taxon coverage pattern decisive if, for any binary phylogenetic tree T for X, the collection of phylogenetic trees obtained by restricting T to the subset of X covered by each locus uniquely determines T. Here we relate the problem of checking whether a taxon coverage pattern is decisive to a hypergraph coloring problem. Using this connection, we (1) show that checking decisiveness is co-NP complete;(2) obtain lower bounds on the amount of coverage needed to achieve decisiveness;(3) devise an exact algorithm for decisiveness;(4) develop problem reduction rules, and use them to obtain efficient algorithms for inputs with few loci;and (5) devise Boolean satisfiability (SAT) and integer linear programming formulations (ILP) of decisiveness that allow us to analyze data sets that arise in practice. For data sets that are not decisive, we use our SATand ILP formulations to obtain decisive subsets of the data.

关键词： Phylogeny partitioning algorithms Color Terminology Steel Software Reliability Phylogenetic tree taxon coverage algorithms no-rainbow hypergraph coloring integer linear programming satisfiability

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Fuzzy Community Detection Based on Elite Symbiotic Organisms Search and Node Neighborhood Information

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第7期30卷 2500-2514页

作者： Xiao, Jing Wang, Yan-Jiao Xu, Xiao-Ke Dalian Minzu Univ Coll Informat & Commun Engn Dalian 116600 Peoples R China Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Jilin Peoples R China

In the last decade, fuzzy community detection has received increasing attention, since it cannot only uncover the community structure of a network, but also reflect the membership degrees of each node to multiple communities. Although some pioneers proposed a few algorithms for finding fuzzy communities, there is still room for further improvement in the quality of detected fuzzy communities. In this study, a metaheuristic-based modularity optimization algorithm, named symbiotic organisms search fuzzy community detection (SOSFCD) is proposed. On the one hand, an improved bioinspired metaheuristic algorithm, elite symbiotic organisms search, is designed as optimization strategy to improve the global convergence of fuzzy modularity optimization. On the other hand, a neighbor-based membership modification operation is proposed to intensify exploitation ability and speed up convergence, by efficiently utilizing local information (i.e., node neighborhood) of network topology. Experimental results on both of synthetic and real-life networks with different scales and characteristics show that SOSFCD can find max-modularity fuzzy partitions and coverings, and outperforms many state-of-the-art algorithms in terms of accuracy and stability.

关键词： partitioning algorithms Optimization Symbiosis Organisms Q measurement Convergence Tools Fuzzy community detection fuzzy covering fuzzy partition modularity optimization symbiotic organisms search (SOS)

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A Distributed Optimization Algorithm Based on Multiagent Network for Economic Dispatch With Region partitioning

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第5期51卷 2466-2475页

作者： Liu, Qingshan Le, Xinyi Li, Kaixuan Southeast Univ Sch Math Nanjing 210096 Peoples R China Southeast Univ Jiangsu Prov Key Lab Networked Collect Intelligen Nanjing 210096 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

In this article, a discrete-time distributed optimization algorithm is proposed for solving the economic dispatch (ED) problem with some groups of generator units to communicate over a connected graph, which is independent of the power system. The ED problem is converted to a distributed optimization problem with an objective of the sum of individual convex functions and constraints of local generators. Based on the optimal conditions, a class of distributed algorithms is designed to find the solution to the ED problem. The distributed algorithm can be realized as a multiagent system with a connected graph, whose convergence can be proved using the dynamic analysis method. Moreover, experiments with simulations are presented to demonstrate the performance of the proposed algorithm.

关键词： Optimization Distributed algorithms partitioning algorithms Heuristic algorithms Power generation Multi-agent systems Generators Convergence distributed algorithm economic dispatch (ED) multiagent system

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Group Reassignment for Dynamic Edge partitioning

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2021年第10期32卷 2477-2490页

作者： Li, He Yuan, Hang Huang, Jianbin Cui, Jiangtao Ma, Xiaoke Wang, Senzhang Yoo, Jaesoo Yu, Philip S. Xidian Univ Sch Comp Sci & Technol Xian 710126 Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Chungbuk Natl Univ Dept Informat & Commun Engn Cheongju 361763 South Korea Univ Illinois Chicago IL 60607 USA Tsinghua Univ Inst Data Sci Beijing 100083 Peoples R China

Graph partitioning is a mandatory step in large-scale distributed graph processing. When partitioning real-world power-law graphs, the edge partitioning algorithm performs better than the traditional vertex partitioning algorithm, because it can cut a single vertex into multiple replicas to apportion the computation. Many advanced edge partitioning methods are designed for partitioning a static graph from scratch. However, the real-world graph structure changes continuously, which leads to a decrease in partition quality and affects the performance of the graph applications. Some studies are devoted to offline repartitioning or batch incremental partitioning, but how to deal with dynamics in real-time is still worthy of in-depth study. In this article, we discuss the impact of dynamic change on partition and discover that both insertion and deletion will lead to local suboptimal partitioning, which is the reason for the degradation of partition quality. As a solution, a dynamic edge partitioning algorithm is proposed to partition dynamics in real-time. Specifically, we deal with dynamics by a distributed stream and improve partition quality by reassigning some closely connected edges. Experiments show that it is robust to initial partition quality, dynamic scale and type, and distributed scale. Compared with the state-of-the-art dynamic partitioner, it can reduce vertex-cuts by 29.5 percent. Compared with the repartitioning algorithms, it can save the partitioning time by 91.0 percent. Applied on the graph task, it can reduce the increase of communication cost and the increase of the total time of task by 41.5 and 71.4 percent.

关键词： Heuristic algorithms partitioning algorithms Real-time systems Task analysis Electronic mail Aerodynamics Social networking (online) Dynamic graph edge partitioning distributed system edge group

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A Stochastic Geo-partitioning Problem for Mobile Edge Computing

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2021年第4期9卷 2189-2200页

作者： Tran, Duc A. Do, Thuy T. Zhang, Ting Univ Massachusetts Dept Comp Sci Boston MA 02125 USA McDaniel Coll Dept Math & Comp Sci Westminster MD 21157 USA

The success of a mobile edge computing network depends critically on how to assign the edge servers to the user cells. The criterion for this assignment is application-specific. In many practical applications, the workload demanded by each cell is unknown and time-varying. So are the effective capacities of the servers. We need an assignment incurring minimum backhaul cost that is robust to these uncertainties. We also want to make the cells assigned to the same server form a geographically compact cluster. This challenge motivates us to introduce a novel stochastic geo-aware partitioning problem. As it is computationally hard, we propose a heuristic algorithm that can produce a range of solutions representing different tradeoffs between cost minimization versus geographical awareness. We evaluate the proposed algorithm using a real-world dataset.

关键词： Servers Cloud computing Robustness Delays Edge computing partitioning algorithms Cellular networks Mobile edge computing partitioning geographical awareness

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