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Two-stage distributed parallel algorithm with message passing interface for maximum flow problem

JOURNAL OF SUPERCOMPUTING 2015年第2期71卷 629-647页

作者： Jiang, Jincheng Wu, Lixin Beijing Normal Univ Acad Disaster Reduct & Emergency Management Beijing 100875 Peoples R China China Univ Min & Technol IoT Percept Mine Res Ctr Xuzhou 221008 Jiangsu Peoples R China

Maximum flow is one of the important and classical combinatorial optimization problems. However, the time complexity of sequential maximum flow algorithms remains high. In this paper, we present a two-stage distributed parallel algorithm (TSDPA) with message passing interface to improve the computational performance. The strategy of TSDPA has two stages, which push excess flows separately along cheap and expensive paths identified by a new distance estimate function. In TSDPA, stage 1 enhances the parallel efficiency by omitting high-cost paths and decentralizing calculations, and stage 2 guarantees the achievement of an optimal solution through divide-and-conquer method. The experimental test demonstrates that TSDPA runs 1.2-15.5 times faster than sequential algorithms and is faster than or almost as fast as the H_PRF and Q_PRF codes.

关键词： Distributed parallel algorithm Maximum flow Message passing interface Two-stage strategy

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A parallel algorithm for Energy Efficiency Maximization in Massive MIMO Networks

A Parallel Algorithm for Energy Efficiency Maximization in M...

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Global Communications Conference

作者： Yang Yang Marius Pesavento Intel Deutschland GmbH Neubiberg Germany Darmstadt University of Technology Darmstadt Germany

In this paper, we propose a novel iterative algorithm based on successive convex approximation for the nonconvex energy efficiency optimization problem in massive MIMO networks. The stationary points of the original problem are found by solving a sequence of successively refined approximate problems, and the proposed algorithm has the following advantages: 1) fast convergence as the structure of the original energy efficiency function is preserved as much as possible in the approximate problem, and 2) easy implementation as each approximate problem is natural for parallel computation and all variable updates have a closed-form expression. The proposed algorithm is guaranteed to converge and its advantages are also illustrated numerically.

关键词： Energy efficiency fractional programming massive MIMO parallel algorithm successive convex approximation

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A parallel multi-objective imperialist competitive algorithm to solve the load offloading problem in mobile cloud computing

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NEURAL COMPUTING & APPLICATIONS 2023年第26期35卷 18905-18932页

作者： Alipour, Sara Saadatfar, Hamid Poor, Mahdi Khazaie Islamic Azad Univ Comp Engn Dept Birjand Branch Birjand Iran Univ Birjand Dept Comp Engn Birjand Iran

Cloud computing is a modern architecture for performing complex and immense processes. It consists of configurable computational resource sets that communicate with each other through communication networks. With the advent of the cloud computing architecture and increasing its applications for mobile devices, the growth rate of mobile data has proliferated exponentially. Consequently, processing the tasks of mobile users has become difficult due to the limitations of these devices, such as low computing power and low capacity. Therefore, the idea of mobile cloud computing (MCC) for mobile devices using cloud-based storage and computing resources was introduced. In MCC, processing information is transferred from the user's mobile devices to the cloud servers. This process is known as the tasks offloading and scheduling of mobile users. In this case, the task execution time, CPU power consumption, network bandwidth, and task allocation time must be specified. Due to many tasks and different resources, the process of task offloading and scheduling is considered a challenging subject in the field of MCC. Therefore, in this paper, a multi-objective parallel imperialist competitive algorithm (MPICA) is proposed. The main objective of this parallel algorithm is to reduce the algorithm's execution time for searching the problem space, reducing processing time, reducing energy consumption, and improving load balance. The simulation results of the proposed algorithm represent that the parallelization of the imperialist competitive algorithm (ICA) has a significant effect on reducing the execution time of the algorithm. In general, the proposed algorithm performs better than the state-of-the-art algorithms based on the proposed criteria.

关键词： Cloud computing Mobile cloud computing Load offloading Task scheduling Imperialist competitive algorithm parallel algorithm

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Design and Improvement of the parallel algorithm in the Computer Games System

Design and Improvement of the Parallel Algorithm in the Comp...

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第28届中国控制与决策会议

作者： Gui Wu Jun Tao Educational Administration office Jianghan University School of Mathematics and Computer Science Jianghan University Department of Electrical & Computer Engineering Rowan University

ISBN: (纸本)9781467397155

The core technology of the computer games is the *** paper is studied to research both the mononuclear search algorithm and the parallel search *** the mononuclear search algorithm,the selective search thought is supplied to the computer games *** the parallel search algorithm,the paper studies the several existing multi-core parallel search methods and analyzes their advantages and *** to the characteristics,the improvement scheme is put forward to the parallel search *** practical results of the computer games system show the comparison effect between the original methods and the improved *** paper finally discusses some special programming skills of the Alpha-Beta implementation in the parallel search *** advanced and modified algorithm is proved to be practical and applicative by experimentations and tests of parallel computer games system provided in this paper.

关键词： parallel algorithm Search algorithm Computer Games Alpha-Beta Search

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A parallel stabilized quadratic equal-order finite element algorithm for the steady Navier-Stokes equations

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2023年第1期100卷 83-104页

作者： Zheng, Bo Shang, Yueqiang Southwest Univ Sch Math & Stat Chongqing 400715 Peoples R China

This paper is concerned with a parallel stabilized quadratic equal-order finite element algorithm for the steady incompressible Navier-Stokes equations where a fully overlapping domain decomposition is used for parallelization. The basic idea of the studied algorithm is that each processor independently calculates a local stabilized solution in an interesting subdomain on a multiscale mesh that is fine around the subdomain and coarse on the rest of subdomain. The present algorithm can be solved simultaneously by the existing Navier-Stokes codes without substantial recoding. In the light of local a priori estimate for stabilized finite element solution, we analyse the error bound of the stabilized solution. Moreover, several parallel iterative stabilized quadratic equal-order finite element algorithms are presented, and three numerical examples which confirm the high efficiency of the parallel stabilized algorithms are provided.

关键词： Navier-Stokes equations stabilized quadratic equal-order method finite element parallel algorithm domain decomposition

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Design and evaluation of a parallel algorithm for inferring topic hierarchies

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INFORMATION PROCESSING & MANAGEMENT 2015年第5期51卷 662-676页

作者： Seshadri, Karthick Shalinie, Mercy S. Kollengode, Chidambaram Thiagarajar Coll Engn Dept Comp Sci & Engn Madurai 625015 Tamil Nadu India Nokia Res Cloud Comp Big Data Analyt Bangalore Karnataka India

The rapid growth of information in the digital world especially on the web, calls for automated methods of organizing the digital information for convenient access and efficient information retrieval. Topic modeling is a branch of machine learning and probabilistic graphical modeling that helps in arranging the web pages according to their topical structure. The topic distribution over a set of documents (web pages) and the affinity of a document toward a specific topic can be revealed using topic modeling. Topic modeling algorithms are typically computationally expensive due to their iterative nature. Recent research efforts have attempted to parallelize specific topic models and are successful in their attempts. These parallel algorithms however have tightly-coupled parallel processes which require frequent synchronization and are also tightly coupled with the underlying topic model which is used for inferring the topic hierarchy. In this paper, we propose a parallel algorithm to infer topic hierarchies from a large scale document corpus. A key feature of the proposed algorithm is that it exploits coarse grained parallelism and the components running in parallel need not synchronize after every iteration, thus the algorithm lends itself to be implemented on a geographically dispersed set of processing elements interconnected through a network. The parallel algorithm realizes a speed up of 53.5 on a 32-node cluster of dual-core workstations and at the same time achieving approximately the same likelihood or predictive accuracy as that of the sequential algorithm, with respect to the performance of Information Retrieval tasks. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Topic modeling Hierarchical clustering Information retrieval parallel algorithm Cluster computing Message passing interface

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Variational Data Assimilation Method Using parallel Dual Populations Particle Swarm Optimization algorithm

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Wuhan University Journal of Natural Sciences 2024年第1期29卷 59-66页

作者： WU Zhongjian LI Junyan Detroit Green Institute of Technology Hubei University of TechnologyWuhan 430068HubeiChina School of Information Management Central China Normal UniversityWuhan 430079HubeiChina

In recent years,numerical weather forecasting has been increasingly *** data assimilation furnishes precise initial values for numerical forecasting models,constituting an inherently nonlinear optimization *** enormity of the dataset under consideration gives rise to substantial computational burdens,complex modeling,and high hardware *** paper employs the Dual-Population Particle Swarm Optimization(DPSO)algorithm in variational data assimilation to enhance assimilation *** harnessing parallel computing principles,the paper introduces the parallel Dual-Population Particle Swarm Optimization(PDPSO)algorithm to reduce the algorithm processing *** were carried out using partial differential equations,and comparisons in terms of time and accuracy were made against DPSO,the Dynamic Weight Particle Swarm algorithm(PSOCIWAC),and the TimeVarying Double Compression Factor Particle Swarm algorithm(PSOTVCF).Experimental results indicate that the proposed PDPSO outperforms PSOCIWAC and PSOTVCF in convergence accuracy and is comparable to *** processing time,PDPSO is 40%faster than PSOCIWAC and PSOTVCF and 70%faster than DPSO.

关键词： parallel algorithm variational data assimilation dual-population particle swarm optimization algorithm diffusion mechanism

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parallel Ant Colony Optimization algorithm for Finding the Shortest Path for Mountain Climbing

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IEEE ACCESS 2023年 11卷 6185-6196页

作者： Alhenawi, Esra'a Abu Khurma, Ruba Sharieh, Ahmad A. Al-Adwan, Omar Al Shorman, Areej Shannaq, Fatima Al Ahliyya Amman Univ Fac Informat Technol Dept Software Engn Amman 19328 Jordan Al Ahliyya Amman Univ Fac Informat Technol Dept Comp Sci Amman 19328 Jordan Univ Jordan Dept Comp Sci Amman 11942 Jordan Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan

The problem of finding the shortest path between two nodes is a common problem that requires a solution in many applications like games, robotics, and real-life problems. Since its deals with a large number of possibilities. Therefore, parallel algorithms are suitable to solve this optimization problem that has attracted a lot of researchers from both industry and academia to find the optimal path in terms of runtime, speedup, efficiency, and cost compared to sequential algorithms. In mountain climbing, finding the shortest path from the start node under the mountain to reach the destination node is a fundamental operator, and there are some interesting issues to be studied in mountain climbing that cannot be found in a traditional two-dimensional space search. We present a parallel Ant Colony Optimization (ACO) to find the shortest path in the mountain climbing problem using Apache Spark. The proposed algorithm guarantees the security of the selected path by applying some constraints that take into account the secure slope angle for the path. A generated dataset with variable sizes is used to evaluate the proposed algorithm in terms of runtime, speedup, efficiency, and cost. The experimental results show that the parallel ACO algorithm significantly $(p < 0.05)$ outperformed the best sequential ACO. On the other hand, the parallel ACO algorithm is compared with one of the most recent research from the literature for finding the best path for mountain climbing problems using the parallel A* algorithm with Apache Spark. The parallel ACO algorithm with Spark significantly outperformed the parallel A* algorithm.

关键词： Apache spark ant colony parallel algorithm path-finding problem optimization

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A parallel grad-div stabilized finite element algorithm for the Stokes equations with damping *

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2023年第1期135卷 171-192页

作者： Jiang, Ye Zheng, Bo Shang, Yueqiang Southwest Univ Sch Math & Stat Chongqing Peoples R China

This work studies a parallel grad-div stabilized finite element algorithm for the damped Stokes equations. In this algorithm, in the light of a fully overlapping domain decomposition technique, we solve a global grad-div stabilized problem to compute a local solution in an intersecting subdomain on a global composite mesh, which is fine in the subdomain and rough elsewhere, making the proposed algorithm easy to implement based on an available sequential solver. We derive error bounds of the approximate solutions from our presented algorithm by the theoretical tool of local a priori estimate for the grad-div stabilized finite element solution. Numerical results verify the validity of the theoretical analysis and demonstrate the benefits of the proposed algorithm. On the one hand, compared with the counterpart one excluding grad-div stabilization, this algorithm can reduce significantly the effect of pressure on the approximate velocities, and hence, yields much better approximate velocities in the case of small viscosities. On the other hand, it takes much less computational time in getting approximate solutions with a comparable accuracy than the standard grad-div stabilization method.

关键词： Stokes equations Damping Grad-div stabilization Finite element parallel algorithm Domain decomposition

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A parallel Discord Discovery algorithm for a Graphics Processor

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PATTERN RECOGNITION AND IMAGE ANALYSIS 2023年第2期33卷 101-112页

作者： Kraeva, Ya. A. Zymbler, M. L. Natl Res Univ South Ural State Univ Chelyabinsk 454080 Russia

The detection of anomalous subsequences in a time series is required today in a wide range of computationally intensive applications such as digital industry, the Internet of Things, personal medicine, etc. One of the most attractive approaches to solving this problem is the concept of discord-the subsequence of a time series with the greatest distance to its nearest neighbor-because it requires the analyst to set only one intuitive parameter (subsequence length). The previously proposed DRAG (discord range aware gathering) algorithm for finding discords is exact, but its parallel versions are absent for any hardware architecture. The paper proposes a new approach to parallelizing this algorithm for a graphics processing unit, which is called PD3 (parallel DRAG-based discord discovery). A data preprocessing phase was added to the PD3 to compute mean values and standard deviations of all subsequences of the time series. The results are used further at the candidate selection and refinement phases to calculate the distances between the subsequences of the time series. Each algorithm phase is parallelized separately on the basis of the data parallelism concept and using vector data structures. Experiments show that the PD3 is much ahead of competing parallel algorithms in terms of the average time taken to find one discord.

关键词： time series detection of anomalies discord parallel algorithm graphics processing unit (GPU)

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