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On link-disjoint Hamiltonian cycles of torus networks

COMPUTERS & ELECTRICAL ENGINEERING 1997年第1期23卷 25-32页

作者： Latifi, S Zheng, SQ LOUISIANA STATE UNIV DEPT COMP SCIBATON ROUGELA 70803

The problem of embedding link-disjoint Hamiltonian cycles into torus networks is addressed. The maximum number of link-disjoint cycles is limited to half the degree of the node in a regular network. Simple methods are presented to embed the maximum number of link-disjoint Hamiltonian cycles in an r-dimensional torus network. An algorithm for finding a Hamiltonian cycle in an r-dimensional torus in the presence of a set of faulty links is also given. Copyright (C) 1997 Elsevier Science Ltd

关键词： fault-tolerant computing network embedding parallel and distributed computing ring

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On the restricted connectivity of the arrangement graph

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JOURNAL OF SUPERcomputing 2017年第8期73卷 3669-3682页

作者： Cheng, Eddie Qiu, Ke Shen, Zhizhang Oakland Univ Dept Math & Stat Rochester MI 48309 USA Brock Univ Dept Comp Sci St Catharines ON L2S 3A1 Canada Plymouth State Univ Dept Comp Sci & Technol MSC 41 Plymouth NH 03264 USA

We derive a general upper bound of the -restricted connectivity for the arrangement graph, namely the minimum cardinality of a vertex set, whose removal disconnects the graph, but every remaining vertex has at least neighbors in the survival graph. We show that this upper bound is exact when and provide an asymptotic lower bound for the cases where .

关键词： Fault tolerance property R-h-restricted connectivity Arrangement graph parallel and distributed computing

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distributed localized bi-objective search

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2014年第3期239卷 731-743页

作者： Derbel, Bilel Humeauc, Jeremie Liefooghe, Arnaud Verel, Sebastien Univ Lille 1 CNRS LIFL F-59655 Villeneuve Dascq France Inria Lille Nord Europe F-59650 Villeneuve Dascq France Ecole Mines Douai Dept IA F-59508 Douai France Univ Littoral Cote Dopale LISIC F-62228 Calais France

We propose a new distributed heuristic for approximating the Pareto set of bi-objective optimization problems. Our approach is at the crossroads of parallel cooperative computation, objective space decomposition, and adaptive search. Given a number of computing nodes, we self-coordinate them locally, in order to cooperatively search different regions of the Pareto front. This offers a trade-off between a fully independent approach, where each node would operate independently of the others, and a fully centralized approach, where a global knowledge of the entire population is required at every step. More specifically, the population of solutions is structured and mapped into computing nodes. As local information, every node uses only the positions of its neighbors in the objective space and evolves its local solution based on what we term a 'localized fitness function'. This has the effect of making the distributed search evolve, over all nodes, to a high quality approximation set, with minimum communications. We deploy our distributed algorithm using a computer cluster of hundreds of cores and study its properties and performance on rho MNK-landscapes. Through extensive large-scale experiments, our approach is shown to be very effective in terms of approximation quality, computational time and scalability. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Multiple objective programming Combinatorial optimization parallel and distributed computing Evolutionary computation

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Performance evaluation of parallel iterative deepening A^* on clusters of workstations

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PERFORMANCE EVALUATION 2005年第1-4期60卷 223-236页

作者： Al-Ayyoub, AE Faculty of Computer Studies Arab Open University P.O. Box 18211 Bahrain

In this paper we investigate the performance of distributed heuristic search methods based on a well-known heuristic search algorithm, the iterative deepening A* (IDA*). The contribution of this paper includes proposing and assessing a distributed algorithm for IDA*. The assessment is based on space, time and solution quality that are quantified in terms of several performance parameters such as generated search space and real execution time among others. The experiments are conducted on a cluster computer system consisting of 16 hosts built around a general-purpose network. The objective of this research is to investigate the feasibility of cluster computing as an alternative for hosting applications requiring intensive graph search. The results reveal that cluster computing improves on the performance of IDA* at a reasonable cost. (c) 2004 Elsevier B.V. All rights reserved.

关键词： heuristic search parallel and distributed computing IDA(*) performance evaluation

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Effects of network topology on the performance of consensus and distributed learning of SVMs using ADMM

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PEERJ COMPUTER SCIENCE 2021年 7卷 e397页

作者： Tavara, Shirin Schliep, Alexander Chalmers Univ Technol Dept Comp Sci & Engn Data Sci & AI Div Gothenburg Sweden Univ Gothenburg Gothenburg Sweden

The Alternating Direction Method of Multipliers (ADMM) is a popular and promising distributed framework for solving large-scale machine learning problems. We consider decentralized consensus-based ADMM in which nodes may only communicate with one-hop neighbors. This may cause slow convergence. We investigate the impact of network topology on the performance of an ADMM-based learning of Support Vector Machine using expander, and mean-degree graphs, and additionally some of the common modern network topologies. In particular, we investigate to which degree the expansion property of the network influences the convergence in terms of iterations, training and communication time. We furthermore suggest which topology is preferable. Additionally, we provide an implementation that makes these theoretical advances easily available. The results show that the performance of decentralized ADMM-based learning of SVMs in terms of convergence is improved using graphs with large spectral gaps, higher and homogeneous degrees.

关键词： Machine learning parallel and distributed computing SVMs ADMM Expander graphs distributed optimization Convergence

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A parallel procedure for nonlinear analysis of reinforced concrete three-dimensional frames

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COMPUTERS & STRUCTURES 2002年第16-17期80卷 1337-1350页

作者： Romero, ML Miguel, PF Cano, JJ Univ Jaume 1 Dept Technol Castellon de La Plana 12071 Spain Univ Politecn Valencia Dept Civil Engn Valencia Spain

The paper discusses the parallelisation of complex three-dimensional software for nonlinear analysis of R/C buildings structures. It presents a comparative study for handling the nonlinear response in different parallel architectures. The nonlinear finite element model adopts a fiber decomposition approach for the cross-section of beam elements to capture nonlinear behavior of concrete. The parallelisation strategy is designed regarding three items: the numerical stability of the nonlinear procedure, the parallel sparse equation solver and the application on heterogeneous hardware: dedicated shared memory machines or clusters of networked personal computers. (C) 2002 Published by Elsevier Science Ltd.

关键词： nonlinear analysis reinforced concrete parallel and distributed computing cluster of personal computers

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Adaptive computing on the grid using AppLeS

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IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2003年第4期14卷 369-382页

作者： Berman, F Wolski, R Casanova, H Cirne, W Dail, H Faerman, M Figueira, S Hayes, J Obertelli, G Schopf, J Shao, G Smallen, S Spring, N Su, A Zagorodnov, D San Diego Supercomp Ctr La Jolla CA 92093 USA Univ Calif Santa Barbara Dept Comp Sci Santa Barbara CA 93106 USA Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92093 USA Dept Sistemas & Comp BR-58109970 Campina Grande PB Brazil Santa Clara Univ Dept Comp Engn Santa Clara CA 95053 USA Argonne Natl Lab Math & Comp Sci Div Distributed Syst Lab Argonne IL 60439 USA Univ Washington Seattle WA 98195 USA

Ensembles of distributed, heterogeneous resources, also known as Computational Grids, have emerged as critical platforms for high-performance and resource-intensive applications. Such platforms provide the potential for applications to aggregate enormous bandwidth, computational power, memory, secondary storage, and other resources during a single execution. However, achieving this performance potential in dynamic, heterogeneous environments is challenging. Recent experience with distributed, applications indicates that adaptivity is fundamental to achieving application performance in dynamic grid environments. The AppLeS (Application Level Scheduling) project provides a methodology, application software, and software environments for adaptively scheduling and deploying applications in heterogeneous, multiuser grid environments. In this article, we discuss the AppLeS project and outline our findings.

关键词： scheduling parallel and distributed computing heterogeneous computing grid computing

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A robust framework for real-time distributed processing of satellite data

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JOURNAL OF parallel and distributed computing 2006年第3期66卷 403-418页

作者： Tehranian, S Zhao, YS Harvey, T Swaroop, A Mckenzie, K AC Technol Inc Adv Computat & Informat Serv Lanham MD 20706 USA Natl Environm Satellite Data & Informat Serv NOAA US Dept Commerce Suitland MD 20746 USA

It is estimated that future satellite instruments such as the Advanced Baseline Imager (ABI) and the Hyperspectral Environmental Suite (HES) on the GOES-R series of satellites will provide raw data volume of about 1.5 Terabyte per day. Due to the high data rate, satellite ground data processing will require considerable computing power to process data in real-time. Cluster technologies employing a multi-processor system present the only current economically viable option. To sustain high levels of system reliability and operability in a cluster-oriented operational environment, a fault-tolerant data processing framework is proposed to provide a platform for encapsulating science algorithms for satellite data processing. The science algorithms together with the framework are hosted on a Linux cluster. In this paper we present an architectural model and a system prototype for providing performance, reliability, and scalability of candidate hardware and software for a satellite data processing system. Furthermore, benchmarking results are presented for a selected number of science algorithms for the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) instrument showing that considerable performance can be gained without sacrificing the reliability and high availability constraints imposed on the operational cluster system. (C) 2006 Elsevier Inc. All rights reserved.

关键词： real-time systems parallel and distributed computing task scheduling reliability

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parallel Multi-Block ADMM with o(1 / k) Convergence

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JOURNAL OF SCIENTIFIC computing 2017年第2期71卷 712-736页

作者： Deng, Wei Lai, Ming-Jun Peng, Zhimin Yin, Wotao Rice Univ Dept Computat & Appl Math Houston TX 77005 USA Univ Georgia Dept Math Athens GA 30602 USA Univ Calif Los Angeles Dept Math Los Angeles CA 90095 USA

This paper introduces a parallel and distributed algorithm for solving the following minimization problem with linear constraints: minimize f(1)(x(1))+ . . . + f(N) (x(N)) subject to A(1)x(1) + . . . + A(N)x(N) = c, x(1) is an element of chi(1), . . . , x(N) is an element of chi(N), where , are convex functions, are matrices, and are feasible sets for variable . Our algorithm extends the alternating direction method of multipliers (ADMM) and decomposes the original problem into N smaller subproblems and solves them in parallel at each iteration. This paper shows that the classic ADMM can be extended to the N-block Jacobi fashion and preserve convergence in the following two cases: (i) matrices are mutually near-orthogonal and have full column-rank, or (ii) proximal terms are added to the N subproblems (but without any assumption on matrices ). In the latter case, certain proximal terms can let the subproblem be solved in more flexible and efficient ways. We show that converges at a rate of o(1 / k) where M is a symmetric positive semi-definte matrix. Since the parameters used in the convergence analysis are conservative, we introduce a strategy for automatically tuning the parameters to substantially accelerate our algorithm in practice. We implemented our algorithm (for the case ii above) on Amazon EC2 and tested it on basis pursuit problems with > 300 GB of distributed data. This is the first time that successfully solving a compressive sensing problem of such a large scale is reported.

关键词： Alternating direction method of multipliers ADMM parallel and distributed computing Convergence rate

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Optimizing FHEW With Heterogeneous High-Performance computing

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第8期16卷 5335-5344页

作者： Lei, Xinya Guo, Ruixin Zhang, Feng Wang, Lizhe Xu, Rui Qu, Guangzhi China Univ Geosci Sch Comp Sci Wuhan 430078 Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430078 Peoples R China Oakland Univ Sch Engn & Comp Sci Rochester MI 48309 USA

The latest implementation of the fully homomorphic encryption algorithm (FHEW), FHEW-V2, takes about 0.12 s for a bootstrapping on a single-node computer. It seems much faster than the previous implementations. However, the 30-bit homomorphic addition requires 270 times of bootstrapping;plus those spent on key generation, the total elapsed time climbs to 55 seconds, which is unacceptable. In this article, we reveal how to further optimize FHEW-V2 by focusing on efficiently constructing homomorphic full adders. We tackle inefficiency in FHEW-V2 by massive efforts: First, we explore FHEW-V2 and locate hotspots;second, we leverage the heterogeneous parallel computing model of multicore CPU and GPUs to remove the hotspots to improve performance. The empirical results show that a 30-bit homomorphic addition is completed in 23.8753 s after optimization, gaining an overall speedup of 2.2845;and a 6-bit homomorphic multiplication costs 25.8438, gaining an overall speedup of 2.2435. The 2.2845 speedup is a rough integration of a 13.248 speedup for the key generation and a 1.672 speedup for the bootstrapping;the 2.2435 speedup is a rough integration of the same key generation and a 1.675 speedup for the bootstrapping. We also reveal the strengths and weaknesses of FHEW-V2 by comparing it with a state-of-the-art somewhat homomorphic encryption algorithm, microsoft's simple encrypted arithmetic library (SEAL).

关键词： Encryption Multicore processing Computational modeling Informatics Optimization Graphics processing units FHEW homomorphic encryption high-performance computing (HPC) parallel and distributed computing

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