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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Cheng Chen, Liangyu Zeng, Zhenbing Lin, Dang East China Normal University Shanghai Key Lab of Trustworthy Computing Shanghai200062 China Shanghai University Department of Mathematics Shanghai200444 China

ISBN: (纸本)9781665494571

In this paper, we present an algorithm using the GPGPU machine to compute the interval solutions of isolated real zeros of multivariate polynomial functions in given ranges. To overcome the state space explosion in the process of searching zero points, we combine the branch-and-bound method and the Hansen-Sengupta method, and the interval arithmetic has been used throughout the computation to guarantee the reliability of results. The computation is implemented on GPGPU system, and experiments for 55 benchmark problems have been done. The result shows our method can produce reliable isolation for real zeros in accepted time. © 2021 IEEE.

关键词： Smart cities Benchmark testing Explosions Reliability parallel algorithms Optimization Arithmetic

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Advances in Asynchronous parallel and Distributed Optimization

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PROCEEDINGS OF THE IEEE 2020年第11期108卷 2013-2031页

作者： Assran, By Mahmoud Aytekin, Arda Feyzmahdavian, Hamid Reza Johansson, Mikael Rabbat, Michael G. McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 0G4 Canada Ericsson AB S-16440 Stockholm Sweden ABB S-72226 Stockholm Sweden KTH Royal Inst Technol S-10044 Stockholm Sweden Facebook Inc Dept AI Res Montreal PQ H2S 3G9 Canada

Motivated by large-scale optimization problems arising in the context of machine learning, there have been several advances in the study of asynchronous parallel and distributed optimization methods during the past decade. Asynchronous methods do not require all processors to maintain a consistent view of the optimization variables. Consequently, they generally can make more efficient use of computational resources than synchronous methods, and they are not sensitive to issues like stragglers (i.e., slow nodes) and unreliable communication links. Mathematical modeling of asynchronous methods involves proper accounting of information delays, which makes their analysis challenging. This article reviews recent developments in the design and analysis of asynchronous optimization methods, covering both centralized methods, where all processors update a master copy of the optimization variables, and decentralized methods, where each processor maintains a local copy of the variables. The analysis provides insights into how the degree of asynchrony impacts convergence rates, especially in stochastic optimization methods.

关键词： Program processors Optimization methods Machine learning Computational modeling Convergence Computational efficiency Distributed algorithms machine learning machine learning algorithms optimization methods parallel algorithms

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parallel computation of Watershed Transform in weighted graphs on shared memory machines

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2020年第3期17卷 527-542页

作者： Braham, Yosra Elloumi, Yaroub Akil, Mohamed Bedoui, Mohamed Hedi ESIEE Paris LIGM A3SI BP 992 Bd Blaise Pascal F-93162 Noisy Le Grand France Univ Monastir Fac Med Med Technol & Image Proc Lab Ave Avicenne Monastir 5019 Tunisia

Watershed Transform is a widely used image segmentation technique that is known to be very data intensive and time consuming. The M-border Kernel Algorithm computes watersheds in the framework of Edge-Weighted Graphs and allows to preserve the topology of the initial map. parallelization represents an effective solution to accelerate it. However, this task remains challenging due to the nature of this technique. In this paper, we address this problem. We start by analyzing the data dependency issues that this algorithm raises when dealing with parallel execution. With respect to that, we propose a parallelization strategy that opts for vertex scanning instead of edges scanning of the graph while preserving the thinning paradigm on which the M-border Kernel Algorithm is based. We show that this strategy overcomes the problem of the simultaneous lowering of two adjacent M-border edges that may occur when edge scan is used. The implementation of the proposed algorithm on a shared memory multicore architecture proves its effectiveness in terms of speedup. In fact, the experimental results show that a speedup factor of 5.55 is achieved using eight processors for 2048x2048 images over the performance of the sequential algorithm using a single processor on the same architecture. Furthermore, the gain in terms of execution time and thus speedup is guaranteed whatever is the size of images on which the algorithm is applied. In fact, a speedup factor of 5.55 is obtained for 2048x2048 images, 5.11 for 1024x1024 images and 4.45 for 512x512 images using eight cores.

关键词： Image segmentation parallel algorithms Real-time performance Watershed cuts Shared memory multicore architecture

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Fast parallel algorithms for Edge-Switching to Achieve a Target Visit Rate in Heterogeneous Graphs

Fast Parallel Algorithms for Edge-Switching to Achieve a Tar...

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International Conference on parallel Processing (ICPP)

作者： Hasanuzzaman Bhuiyan Jiangzhuo Chen Maleq Khan Madhav V. Marathe Department of Computer Science Virginia Bioinformatics Institute Virginia Tech Blacksburg VA USA

An edge switch is an operation on a network (graph) where two edges are selected randomly and one of their end vertices are swapped with each other. Usually, a sequence of these operations are performed to generate network perturbations having the same degree sequence of the original network. Edge switch operations have important applications in graph theory and network analysis, such as in generating random networks with a given degree sequence, modeling and analyzing dynamic networks (e.g., peer-to-peer networks), studying various dynamic phenomena over a network (e.g., disease dynamics over a social contact network). The growth of real-world networks motivates the need to develop efficient parallel algorithms for performing a large sequence of edge switch operations. The dependencies among successive edge switch operations and the requirement of keeping the graph simple (i.e., no self-loops or parallel edges) as the edges are switched lead to significant challenges in designing a parallel algorithm. Addressing these challenges requires complex synchronization and communication among the processors. In this paper, we present a distributed memory parallel algorithm for switching edges in massive networks (networks with billions of edges) and achieve a speedup factor of 85 with 1024 processors. One of the steps in our edge switch algorithm requires the computation of multinomial random variables in parallel. The paper presents the first non-trivial parallel algorithm for the problem. The algorithm achieves a speedup of 925 using 1024 processors.

关键词： Switches Program processors parallel algorithms Image edge detection Random variables Partitioning algorithms Synchronization

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parallel implementation of the Image Block Representation using OpenMP

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JOURNAL OF parallel AND DISTRIBUTED COMPUTING 2020年第0期137卷 134-147页

作者： Spiliotis, Iraklis M. Bekakos, Michael P. Boutalis, Yiannis S. Democritus Univ Thrace Dept Elect & Comp Engn GR-67100 Xanthi Greece

Herein, a parallel implementation in OpenMP of the Image Block Representation (IBR) for binary images is investigated. The IBR is a region-based image representation scheme that represents the binary image as a set of non-overlapping rectangular areas with object level, called blocks. The IBR permits the execution of operations on image areas instead of image points and therefore leads to a substantial reduction of the required computational complexity. The experimental and the analytically derived results from parallel implementation in OpenMP, on a multicore computer, proved that a very good overall performance can be achieved. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Image Block Representation Karp-Flatt metric parallel computing parallel algorithms OpenMP

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Improved parallel construction of wavelet trees and rank/select structures ?

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INFORMATION AND COMPUTATION 2020年 273卷 104516-104516页

作者： Shun, Julian MIT CSAIL 32 Vassar St Cambridge MA 02139 USA

Existing parallel algorithms for wavelet tree construction have a work complexity of O(nlog⁡σ). This paper presents parallel algorithms for the problem with improved work complexity. Our first algorithm is based on parallel integer sorting and has either O(nlog⁡log⁡n⌈log⁡σ/log⁡nlog⁡log⁡n⌉) work and polylogarithmic depth, or O(n⌈log⁡σ/log⁡n⌉) work and sub-linear depth. We also describe another algorithm that has O(n⌈log⁡σ/log⁡n⌉) work and O(σ+log⁡n) depth. We then show how to use similar ideas to construct variants of wavelet trees (arbitrary-shaped binary trees and multiary trees) as well as wavelet matrices in parallel with lower work complexity than prior algorithms. Finally, we show that the rank and select structures on binary sequences and multiary sequences, which are stored on wavelet tree nodes, can be constructed in parallel with improved work bounds, matching those of the best existing sequential algorithms for constructing rank and select structures.

关键词： Wavelet tree Rank and select Wavelet matrix parallel algorithms Succinct data structures

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BFS and Coloring-Based parallel algorithms for Strongly Connected Components and Related Problems

BFS and Coloring-Based Parallel Algorithms for Strongly Conn...

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International Symposium on parallel and Distributed Processing (IPDPS)

作者： George M. Slota Sivasankaran Rajamanickam Kamesh Madduri Computer Science and Engineering The Pennsylvania State University Scalable Algorithms Department Sandia National Laboratories

Finding the strongly connected components (SCCs) of a directed graph is a fundamental graph-theoretic problem. Tarjan's algorithm is an efficient serial algorithm to find SCCs, but relies on the hard-to-parallelize depth-first search (DFS). We observe that implementations of several parallel SCC detection algorithms show poor parallel performance on modern multicore platforms and large-scale networks. This paper introduces the Multistep method, a new approach that avoids work inefficiencies seen in prior SCC approaches. It does not rely on DFS, but instead uses a combination of breadth-first search (BFS) and a parallel graph coloring routine. We show that the Multistep method scales well on several real-world graphs, with performance fairly independent of topological properties such as the size of the largest SCC and the total number of SCCs. On a 16-core Intel Xeon platform, our algorithm achieves a 20X speedup over the serial approach on a 2 billion edge graph, fully decomposing it in under two seconds. For our collection of test networks, we observe that the Multistep method is 1.92X faster (mean speedup) than the state-of-the-art Hong et al. SCC method. In addition, we modify the Multistep method to find connected and weakly connected components, as well as introduce a novel algorithm for determining articulation vertices of biconnected components. These approaches all utilize the same underlying BFS and coloring routines.

关键词： Color Arrays Image color analysis Partitioning algorithms parallel algorithms Multicore processing

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Multidimensional frontier visualization based on optimization methods using parallel computations

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JOURNAL OF GLOBAL OPTIMIZATION 2020年第3期76卷 563-574页

作者： Afanasiev, Alexander P. Krivonozhko, Vladimir E. Lychev, Andrey V. Sukhoroslov, Oleg V. Russian Acad Sci Inst Informat Transmiss Problems Ctr Distributed Comp Nakhimovsky Prospekt 36-1 Moscow 117218 Russia Natl Univ Sci & Technol MISiS Leninskiy Prospekt 4 Moscow 119049 Russia Lomonosov Moscow State Univ GSP 1 Moscow 119991 Russia Natl Res Univ Higher Sch Econ Myasnitskaya Str 20 Moscow 101000 Russia Russian Acad Sci Fed Res Ctr Comp Sci & Control Vavilov St 44-2 Moscow 119333 Russia

In data envelopment analysis, methods for constructing sections of the frontier have been recently proposed to visualize the production possibility set. The aim of this paper is to develop, prove and test the methods for the visualization of production possibility sets using parallel computations. In this paper, a general scheme of the algorithms for constructing sections (visualization) of production possibility set is proposed. In fact, the algorithm breaks the original large-scale problems into parallel threads, working independently, then the piecewise solution is combined into a global solution. An algorithm for constructing a generalized production function is described in detail.

关键词： Data envelopment analysis (DEA) parallel algorithms Efficient frontier Frontier visualization

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An Event-trigger Optimization Framework for Congestion Management 37

An Event-trigger Optimization Framework for Congestion Manag...

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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Li, Jianxin Fan, Weiqin Huang, Qingqiang Zhang, Lele Lin, Xiuhan Yang, Xiaoting State Grid Rizhao Electric Power Company Rizhao China University of Jinan School of Electrical Engineering Jinan China

ISBN: (纸本)9781665465366

This paper proposed an event-triggered framework to solve network congestions caused by microgrids (MGs) in regional distributed networks. Two processes are included in this framework: congestion validation process and power rescheduling process. In order to relieve the computation burden, rescheduling process is triggered only when congestions are detected in congestion validation process. DC optimal power flow based optimization model is used to describe congestion validation process. And then power rescheduling process can be formulated distributed optimization problem for multiple microgrids, which can be solved by the alternating direction method of multipliers (ADMM) algorithm. Finally, simulations are implemented to illustrate the reasonability and effectiveness of the proposed framework. Results show that the proposed framework could effectively solve the congestions with transaction diversity guaranteed. © 2022 IEEE.

关键词： parallel algorithms

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An efficient parallel implementation for training supervised optimum-path forest classifiers

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NEUROCOMPUTING 2020年 393卷 259-268页

作者： Culquicondor, Aldo Baldassin, Alexandro Castelo-Fernandez, Cesar de Carvalho, Joao P. L. Papa, Joao Paulo Univ Catolica San Pablo Arequipa Peru UNESP Sao Paulo State Univ Rio Claro Brazil UNESP Sao Paulo State Univ Bauru SP Brazil Univ Estadual Campinas Inst Comp Campinas Brazil

In this work, we propose and analyze parallel training algorithms for the Optimum-Path Forest (OPF) classifier. We start with a naive parallelization approach where, following traditional sequential training that considers the supervised OPF, a priority queue is used to store the best samples at each learning iteration. The proposed approach replaces the priority queue with an array and a linear search aiming at using a parallel-friendly data structure. We show that this approach leads to less competition among threads, thus yielding a more temporal and spatial locality. Additionally, we show how the use of vectorization in distance calculations affects the overall speedup and also provide directions on the situations one can benefit from that. The experiments are carried out on five public datasets with a different number of samples and features on architectures with distinct levels of parallelism. On average, the proposed approach provides speedups of up to 11.8 x and 26 x in a 24-core Intel and 64-core AMD processors, respectively. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Optimum-path forest parallel algorithms Graph algorithms

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