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International Conference on Computational Science (ICCS) on the Ascent of Computational Excellence

作者： Bozejko, Wojciech Uchronski, Mariusz Wodecki, Mieczyslaw Univ Wroclaw Inst Comp Sci Joliot Curie 15 PL-50383 Wroclaw Poland Wroclaw Univ Technol Inst Comp Engn Control & Robot PL-50372 Wroclaw Poland

The aim of this paper is to show how to determine the neighborhood of the complex discrete optimization problem and how to search it in the parallel environment, this being illustrated by an example of the hybrid scheduling, more precisely a flexible job shop problem. We present a parallel single-walk approach in this respect. A theoretical analysis based on PRAM model of parallel computing has been made. We propose a cost-optimal method of neighborhood generation parallelization.

关键词： parallel algorithm scheduling flexible job shop metaheuristics

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Finding Structure with Randomness: Probabilistic algorithms for Constructing Approximate Matrix Decompositions

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SIAM REVIEW 2011年第2期53卷 217-288页

作者： Halko, N. Martinsson, P. G. Tropp, J. A. Univ Colorado Dept Appl Math Boulder CO 80309 USA CALTECH Pasadena CA 91125 USA

Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-revealing QR decomposition, play a central role in data analysis and scientific computing. This work surveys and extends recent research which demonstrates that randomization offers a powerful tool for performing low-rank matrix approximation. These techniques exploit modern computational architectures more fully than classical methods and open the possibility of dealing with truly massive data sets. This paper presents a modular framework for constructing randomized algorithms that compute partial matrix decompositions. These methods use random sampling to identify a subspace that captures most of the action of a matrix. The input matrix is then compressed-either explicitly or implicitly-to this subspace, and the reduced matrix is manipulated deterministically to obtain the desired low-rank factorization. In many cases, this approach beats its classical competitors in terms of accuracy, robustness, and/or speed. These claims are supported by extensive numerical experiments and a detailed error analysis. The specific benefits of randomized techniques depend on the computational environment. Consider the model problem of finding the k dominant components of the singular value decomposition of an m x n matrix. (i) For a dense input matrix, randomized algorithms require O(mnlog(k)) floating-point operations (flops) in contrast to O(mnk) for classical algorithms. (ii) For a sparse input matrix, the flop count matches classical Krylov subspace methods, but the randomized approach is more robust and can easily be reorganized to exploit multiprocessor architectures. (iii) For a matrix that is too large to fit in fast memory, the randomized techniques require only a constant number of passes over the data, as opposed to O(k) passes for classical algorithms. In fact, it is sometimes possible to perform matrix approximation with a single pass over the data.

关键词： dimension reduction eigenvalue decomposition interpolative decomposition Johnson-Lindenstrauss lemma matrix approximation parallel algorithm pass-efficient algorithm principal component analysis randomized algorithm random matrix rank-revealing QR factorization singular value decomposition streaming algorithm

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parallel algorithm for cell dynamics simulation of block copolymers

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MACROMOLECULAR THEORY AND SIMULATIONS 2007年第9期16卷 779-784页

作者： Guo, Xiaohu Pinna, Marco Zvelindovsky, Andrei V. Univ Cent Lancashire Dept Phys Astron & Math Ctr Mat Sci Preston PR1 2HE Lancs England

Cell dynamics simulation (CDS) is a very promising approach to model dynamic processes in block copolymer systems at the mesoscale level. It is difficult to implement a real time and experimental-scale simulation with traditional serial algorithms because of the expensive computation. A parallel, spatial decomposition-based algorithm for large-scale CDS is proposed. With the efficient strategy of domain decomposition and the fast method of neighbouring points location, we greatly reduce the calculating and communicating cost. The numerical results indicate that the proposed parallel algorithm can provide an efficient procedure for computer simulation of block copolymer systems of experimental size.

关键词： block copolymer cell dynamics simulation computer modelling parallel algorithm

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parallel Extended Basic Operations for Conic Curves Cryptography over Ring Zn

Parallel Extended Basic Operations for Conic Curves Cryptogr...

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9th IEEE International Symposium on parallel and Distributed Processing with Applications Workshops (ISPAW)/ICASE/SGH/GSDP

作者： Li, Yongnan Xiao, Limin Chen, Siming Tian, Hongyun Ruan, Li Yu, Binbin Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China

ISBN: (纸本)9780769544298

This paper presents parallel extended operations of point-addition and point-double for cryptosystem on conic curves over ring Zn. The data dependencies are deduced by analyzing the definitions of two extended operations. Take procedure of paralleling extended point-addition as an example. We employ the Chinese Remainder Theorem to divide point-addition over ring Zn into two point-additions over finite field Fp and finite field Fq. Then the temporary values of point-addition over two finite fields are merged to get the final result of extended point-addition over ring Zn. The analysis of parallel methodology is based on our previous works about the basic parallel algorithms used in conic curves cryptosystem. Computing extended point-addition and point-double both need to execute three steps. For getting the average runtime and speedup ratio, different cases are considered in the second step. The performance evaluation demonstrates that our techniques improve the efficiencies of two extended operations. Additionally, the parallel methods introduced in this paper are also more efficient than traditional parallel algorithms.

关键词： conic curves cryptosystem ring Zn Chinese Remainder Theorem parallel algorithm point-addition point-double

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Multi-core CPU Based parallel Cube algorithms 1

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International Conference on Advanced Research on Computer Science and Information Engineering

作者： Zhou, Guoliang Zhang, Han Baodine Elect Power Voc & Tech Coll Dept Informat Baoding 071051 Hebei Peoples R China

ISBN: (数字)9783642214110

ISBN: (纸本)9783642214103

In recent years. computer hardware technology has greatly developed especially large memory and multi-core, but algorithm efficiency is not beneficial from the development of hardware. The fundamental reason is that there is insufficient utilizing CPU cache, as well as the limitations of single-thread programming. In the field of data warehousing and LAP, data cube computing is an important and time-consuming operation, how to improve efficiency of data cube calculation is continuing to pursue goals. Based on the characteristics of modern CPU, we have proposed two parallel algorithms TASK_PMW and DATA_SSMW, TASK_PMW is task-based division of the parallel algorithm, each CPU core is responsible for one Cuboid;DATA_SSMW is data partition, and scanned sharing raw data, ensure load balancing. has good scalability and high efficient. Through experiments on dual-core CPU. TASK_PMW improve 1/3. DATA_SSMW 2/3 than the original algorithm.

关键词： multi-core parallel algorithm Cube algorithm cache conscious

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Revisiting Multiple Pattern Matching algorithms for Multi-Core Architecture

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Journal of Computer Science & Technology 2011年第5期26卷 866-874页

作者：谭光明刘萍卜东波刘燕兵 Key Laboratory of Computer System and Architecture Institute of Computing TechnologyChinese Academy of Sciences Key Laboratory of Network Technology Institute of Computing TechnologyChinese Academy of Sciences

Due to the huge size of patterns to be searched,multiple pattern searching remains a challenge to several newly-arising applications like network intrusion *** this paper,we present an attempt to design efficient multiple pattern searching algorithms on multi-core *** observe an important feature which indicates that the multiple pattern matching time mainly depends on the number and minimal length of *** multi-core algorithm proposed in this paper leverages this feature to decompose pattern set so that the parallel execution time is *** formulate the problem as an optimal decomposition and scheduling of a pattern set,then propose a heuristic algorithm,which takes advantage of dynamic programming and greedy algorithmic techniques,to solve the optimization *** results suggest that our decomposition approach can increase the searching speed by more than 200% on a 4-core AMD Barcelona system.

关键词： parallel algorithm multi-core multiple pattern matching

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parallel Random Number Generations For Monte Carlo Simulation 11

Parallel Random Number Generations For Monte Carlo Simulatio...

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49th Annual Association-for-Computing-Machinery (ACM) Southeast Conference (ACMSE)

作者： Lee, Ingyu Troy Univ Sorrell Coll Business Troy NY 36082 USA

ISBN: (纸本)9781450306867

Modeling and simulation has been popularly used to explain complex phenomena or find knowledge in many areas such as science and engineering, business and economics, and social science. Monte Carlo simulation has been popularly used in modeling and simulation, and generating a sequence of random numbers has been at the center of Monte Carlo simulation. With the advent of parallel computing architecture, parallel modeling and simulation has been popular in many areas and many researches have been done to generate a sequence of random numbers in parallel computing environment. In this paper, we are proposing a framework to generate a sequence of random numbers in parallel and showing the usage of the framework in financial applications.

关键词： parallel algorithm Random Number Generator Option Pricing

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parallel reduction based on condition attributes

Parallel reduction based on condition attributes

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2011 IEEE International Conference on Granular Computing, GrC 2011

作者： Deng, Dayong Yan, Dianxun Chen, Lin College of Mathematics Physics and Information Engineering Zhejiang Normal University Jinhua 321004 China

ISBN: (纸本)9781457703713

In this paper, we propose a parallel algorithm to obtain reducts. The algorithm firstly divides the decision system into a number of subsystems, then reduce conditional attributes in each subsystem, and merge some subsystems together. This process repeats until all subsystems are merged into one. In this way, conditional attributes are reduced efficiently. The effectiveness of our method is analyzed and validated through experiments on seven datasets. © 2011 IEEE.

关键词： Complexity theory Computers Conferences Educational institutions Heuristic algorithms Information systems Rough sets condition attributes conditional attributes decision system decomposition dynamic reducts parallel algorithm parallel algorithms parallel

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An Asynchronous Multithreaded algorithm for the Maximum Network Flow Problem with Nonblocking Global Relabeling Heuristic

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IEEE TRANSACTIONS ON parallel AND DISTRIBUTED SYSTEMS 2011年第6期22卷 1025-1033页

作者： Hong, Bo He, Zhengyu Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

In this paper, we present a novel asynchronous multithreaded algorithm for the maximum network flow problem. The algorithm is based on the classical push-relabel algorithm, which is essentially sequential and requires intensive and costly lock usages to parallelize it. The novelty of the algorithm is in the removal of lock and barrier usages, thereby enabling a much more efficient multithreaded implementation. The newly designed push and relabel operations are executed completely asynchronously and each individual process/thread independently decides when to terminate itself. We further propose an asynchronous global relabeling heuristic to speed up the algorithm. We prove that our algorithm finds a maximum flow with O(vertical bar V vertical bar(2) vertical bar vertical bar E vertical bar) operations, where vertical bar V vertical bar is the number of vertices and vertical bar E vertical bar is the number of edges in the graph. We also prove the correctness of the relabeling heuristic. Extensive experiments show that our algorithm exhibits better scalability and faster execution speed than the lock-based parallel push-relabel algorithm.

关键词： Maximum network flow parallel algorithm multithreading lock-free

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Cooperating local search for the maximum clique problem

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JOURNAL OF HEURISTICS 2011年第2期17卷 181-199页

作者： Pullan, Wayne Mascia, Franco Brunato, Mauro Griffith Univ Sch Informat & Commun Technol Gold Coast Australia Univ Trento Dipartimento Ingn & Sci Informaz Trento Italy

The advent of desktop multi-core computers has dramatically improved the usability of parallel algorithms which, in the past, have required specialised hardware. This paper introduces cooperating local search (CLS), a parallelised hyper-heuristic for the maximum clique problem. CLS utilises cooperating low level heuristics which alternate between sequences of iterative improvement, during which suitable vertices are added to the current clique, and plateau search, where vertices of the current clique are swapped with vertices not in the current clique. These low level heuristics differ primarily in their vertex selection techniques and their approach to dealing with plateaus. To improve the performance of CLS, guidance information is passed between low level heuristics directing them to particular areas of the search domain. In addition, CLS dynamically reconfigures the allocation of low level heuristics to cores, based on information obtained during a trial, to ensure that the mix of low level heuristics is appropriate for the instance being optimised. CLS has no problem instance dependent parameters, improves the state-of-the-art performance for the maximum clique problem over all the BHOSLIB benchmark instances and attains unprecedented consistency over the state-of-the-art on the DIMACS benchmark instances.

关键词： Local search Maximum clique parallel algorithm

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