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Evolutionary Rough parallel Multi-Objective Optimization algorithm

FUNDAMENTA INFORMATICAE 2010年第1期99卷 13-27页

作者： Maulik, Ujjwal Sarkar, Anasua Jadavpur Univ Dept Comp Sc & Engg Kolkata 700032 India Govt Coll Engg & Leather Technol Dept Informat Technol Kolkata 700098 India

A hybrid unsupervised learning algorithm, which is termed as parallel Rough-based Archived Multi-Objective Simulated Annealing (PARAMOSA), is proposed in this article. It comprises a judicious integration of the principles of the rough sets theory and the scalable distributed paradigm with the archived multi-objective simulated annealing approach. While the concept of boundary approximations of rough sets in this implementation, deals with the incompleteness in the dynamic classification method with the quality of classification coefficient as the classificatory competence measurement, the time-efficient parallel approach enables faster convergence of the Pareto-archived evolution strategy. It incorporates both the rough set-based dynamic archive classification method and the distributed implementation as a two-phase speedup strategy in this algorithm. A measure of the amount of domination between two solutions has been incorporated in this work to determine the acceptance probability of a new solution with an improvement in the spread of the non-dominated solutions in the Pareto-front by adopting rough sets theory. A complexity analysis of the proposed algorithm is provided. An extensive comparative study of the proposed algorithm with three other existing and well-known Multi-Objective Evolutionary algorithms (MOEAs) demonstrate the effectiveness of the former with respect to four existing performance metrics and eleven benchmark test problems of varying degrees of difficulties. The superiority of this new parallel implementation over other algorithms also has been demonstrated in timing, which achieves a near optimal speedup with a minimal communication overhead.

关键词： Amount of domination archive multi-objective optimization parallel algorithm rough sets theory simulated annealing

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An algorithm using the finite volume element method and its splitting extrapolation

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2011年第13期235卷 3734-3742页

作者： Cao, Yong He, Xiaoming Lue, Tao Shenzhen Grad Sch Harbin Inst Technol Dept Mech Engn & Automat Shenzhen 518055 Guangdong Peoples R China Virginia Tech Dept Math Blacksburg VA 24061 USA Sichuan Univ Dept Math Chengdu 610064 Sichuan Peoples R China

This paper is to present a new efficient algorithm by using the finite volume element method and its splitting extrapolation. This method combines the local conservation property of the finite volume element method and the advantages of splitting extrapolation, such as a high order of accuracy, a high degree of parallelism, less computational complexity and more flexibility than a Richardson extrapolation. Because the splitting extrapolation formulas only require us to solve a set of smaller discrete subproblems on different coarser grids in parallel instead of on the globally fine grid, a large scale multidimensional problem is turned into a set of smaller discrete subproblems. Additionally, this method is efficient for solving interface problems if we regard the interfaces of the problems as the interfaces of the initial domain decomposition. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Extrapolation Finite volume element method parallel algorithm Domain decomposition

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A FAST ITERATIVE METHOD FOR SOLVING THE EIKONAL EQUATION ON TRIANGULATED SURFACES

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2011年第5期33卷 2468-2488页

作者： Fu, Zhisong Jeong, Won-Ki Pan, Yongsheng Kirby, Robert M. Whitaker, Ross T. Univ Utah Sci Comp & Imaging Inst Salt Lake City UT 84112 USA UNIST Ulsan 689798 South Korea

This paper presents an efficient, fine-grained parallel algorithm for solving the Eikonal equation on triangular meshes. The Eikonal equation, and the broader class of Hamilton-Jacobi equations to which it belongs, have a wide range of applications from geometric optics and seismology to biological modeling and analysis of geometry and images. The ability to solve such equations accurately and efficiently provides new capabilities for exploring and visualizing parameter spaces and for solving inverse problems that rely on such equations in the forward model. Efficient solvers on state-of-the-art, parallel architectures require new algorithms that are not, in many cases, optimal, but are better suited to synchronous updates of the solution. In previous work [W. K. Jeong and R. T. Whitaker, SIAM J. Sci. Comput., 30 (2008), pp. 2512-2534], the authors proposed the fast iterative method (FIM) to efficiently solve the Eikonal equation on regular grids. In this paper we extend the fast iterative method to solve Eikonal equations efficiently on triangulated domains on the CPU and on parallel architectures, including graphics processors. We propose a new local update scheme that provides solutions of first-order accuracy for both architectures. We also propose a novel triangle-based update scheme and its corresponding data structure for efficient irregular data mapping to parallel single-instruction multiple-data (SIMD) processors. We provide detailed descriptions of the implementations on a single CPU, a multicore CPU with shared memory, and SIMD architectures with comparative results against state-of-the-art Eikonal solvers.

关键词： Hamilton-Jacobi equation Eikonal equation triangular mesh parallel algorithm shared memory multiple-processor computer system graphics processing unit

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A parallel algorithm based on Galerkin theory for block-tridiagonal linear systems

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APPLIED MATHEMATICS AND COMPUTATION 2007年第2期187卷 1277-1285页

作者： Quanyi, Lue Manyu, Xiao Min, Zhou Northwestern Polytech Univ Dept Appl Math Xian 710072 Peoples R China

The work presented in this paper focuses on parallel iterative method for solving block-tridiagonal linear systems. Being based on Galerkin theory predetermining W-m = V-m, and forming a suitable matrix V-m a parallel iterative method on distributed-memory multi-computer is established. Then Eq. (3.1)-(3.3) are provided for carrying out computation required by our parallel algorithm. Furthermore, convergence is proved when the coefficient matrix A is a symmetric positive definite matrix, and the sufficient condition is given. In the end, two illustrative examples implemented on HP rx2600 cluster show that our algorithm's parallel acceleration rates and efficiency are higher. (c) 2006 Elsevier Inc. All rights reserved.

关键词： parallel algorithm block-tridiagonal linear systems Galerkin theory HP rx2600 cluster

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An algorithm using the finite volume element method and its splitting extrapolation

An algorithm using the finite volume element method and its ...

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International Conference on Engineering and Computational Mathematics (ECM2009)

关键词： Extrapolation Finite volume element method parallel algorithm Domain decomposition

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Transient Stability Online Simulation by Symplectic Runge-Kutta Method and FGMRES Solver

Transient Stability Online Simulation by Symplectic Runge-Ku...

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Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Wang Fangzong He Yifan Ye Jing China Three Gorges Univ Elect Engn & Renewable Energy Sch Yichang 443002 Hubei Province Peoples R China

ISBN: (纸本)9781424462551

This paper proposes the application of the symplectic Runge-Kutta algorithm combined with the flexible GMRES method to solve the differential algebraic equations encountered in the power system transient stability simulation. In the proposed method, the s-stage 2s-order symplectic Runge-Kutta is used to convert the differential algebraic system in to a set of nonlinear algebraic equations, and then the large algebraic system is solved using a flexible inner-outer preconditioned GMRES method. The proposed method is of inherently parallel. The numerical simulation results obtained on IEEE 145-bus test system show that, the proposed method can achieve significant improvement in speed as compared to the conventional approach.

关键词： transient stability simulation parallel algorithm symplectic algorithm flexible GMRES method preconditioner

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High-Performance Diagnostic Fault Simulation on GPUs

High-Performance Diagnostic Fault Simulation on GPUs

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16th IEEE European Test Symposium (ETS)

作者： Li, Min Hsiao, Michael S. Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA 24061 USA

ISBN: (纸本)9780769544335

In this paper, we present an efficient diagnostic fault simulator based on a state-of-the-art graphics processing unit (GPU). Diagnostic fault simulation plays an important role to identify and locate the causes of circuit failures. However, today's complex VLSI circuits pose ever higher computational demand for such simulators. Our GPU based diagnostic fault simulator (GDSim) is based on a novel two-stage simulation framework which exploits high computation efficiency on the GPU. The fault pair based simulation is proposed to overcome the limited capacity of GPU memory as well as achieve a substantial fine-grained parallelism. Multi-fault-signature and dynamic load balancing techniques are introduced for the best usage of computing resources on-board. Experimental results demonstrate a speedup of up to 121x (with average speedup of 38.43x) compared to a state-of-the-art CPU-based diagnostic fault simulator.

关键词： compute unified device architecture (CUDA) diagnostic faut simulation general-purpose computing on graphics processing unit (GPGPU) parallel algorithm

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An exact parallel algorithm to compare very long biological sequences in clusters of workstations

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2007年第2期10卷 187-202页

作者： Boukerche, Azzedine Magalhaes, Alba Cristina de Melo, Alves Sandes, Edans Flavius de Oliveira Ayala-Rincon, Mauricio Univ Ottawa SITE Ottawa ON Canada Univ Brasilia Brasilia DF Brazil

Biological Sequence Comparison is one of the most important operations in Computational Biology since it is used to determine how similar two sequences are. Smith and Waterman proposed an exact algorithm (SW), based on dynamic programming, that is able to obtain the best local alignment between two sequences in quadratic time and space. In order to compare long biological sequences, SW is rarely used since the computation time and the amount of memory required becomes prohibitive. For this reason, heuristic methods like BLAST are widely used. Although faster, these heuristic methods do not guarantee that the best result will be produced. In this paper, we propose an exact parallel variant of the SW algorithm that obtains the best local alignments in quadratic time and reduced space. The results obtained in two clusters (8-machine and 16-machine) for DNA sequences longer than 32 KBP (kilo base-pairs) were very close to linear and, in some cases, superlinear. For very Iona DNA sequences (1.6 MBP), we were able to reduce execution time from 12.25 hours to 1.54 hours, in our 8-machine cluster. As far as we know, this is the first time 1.6 MBP sequences are compared with an exact SW variant. In this case, 30240 best local alignments were obtained.

关键词： biological sequence comparison parallel algorithm

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Binary Addition Chain on EREW PRAM 1

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11th International Conference on algorithms and Architectures for parallel Processing (ICA3PP)

作者： Fathy, Khaled A. Bahig, Hazem M. Bahig, Hatem M. Ragb, A. A. Sinai Univ Fac Engn Dept Basic Sci Cairo Egypt Ain Shams Univ Fac Sci Dept Math Div Comp Sci Cairo Egypt Al Azhar Univ Fac Sci Dept Math Cairo Egypt

ISBN: (数字)9783642246692

ISBN: (纸本)9783642246685

An addition chain for a natural number x of n bits is a sequence of numbers a(0), a(1), ... , a(l), such that a(0) = 1, a(l) = x, and a(k) = a(i) + a(j) with 0 <= i, j < k <= l. The addition chain problem is what is the minimal number of additions needed to compute X starting from 1? In this paper, we present a new parallel algorithm to generate a short addition chain for x. The algorithm has running time O(log(2) n) using polynomial number processors under EREW PRAM (exclusive read exclusive write parallel random access machine). The algorithm is faster than previous algorithms and is based on binary method.

关键词： Addition chain binary method parallel algorithm EREW PRAM

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Software Implementation of Binary Elliptic Curves: Impact of the Carry-Less Multiplier on Scalar Multiplication

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13th International Workshop on Cryptographic Hardware and Embedded Systems (CHES 2011)

作者： Taverne, Jonathan Faz-Hernandez, Armando Aranha, Diego F. Rodriguez-Henriquez, Francisco Hankerson, Darrel Lopez, Julio Univ Lyon 1 ISFA F-69622 Villeurbanne France CINVESTAV IPN Comp Sci Dept Mexico City DF Mexico Univ Estadual Campinas Inst Comp Campinas Brazil Auburn Univ Auburn AL 36849 USA

ISBN: (纸本)9783642239502;9783642239519

The availability of a new carry-less multiplication instruction in the latest Intel desktop processors significantly accelerates multiplication in binary fields and hence presents the opportunity for reevaluating algorithms for binary field arithmetic and scalar multiplication over elliptic curves. We describe how to best employ this instruction in field multiplication and the effect on performance of doubling and halving operations. Alternate strategies for implementing inversion and half-trace are examined to restore most of their competitiveness relative to the new multiplier. These improvements in field arithmetic are complemented by a study on serial and parallel approaches for Koblitz and random curves, where parallelization strategies are implemented and compared. The contributions are illustrated with experimental results improving the state-of-the-art performance of halving and doubling-based scalar multiplication on NIST curves at the 112- and 192-bit security levels, and a new speed record for side-channel resistant scalar multiplication in a random curve at the 128-bit security level.

关键词： Elliptic curve cryptography finite field arithmetic parallel algorithm efficient software implementation

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