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Exponentially convergent parallel algorithm for nonlinear eigenvalue problems

IMA JOURNAL OF NUMERICAL ANALYSIS 2007年第4期27卷 818-838页

作者： Gavrilyuk, I. P. Klimenko, A. V. Makarov, V. L. Rossokhata, N. O. Natl Acad Sci Ukraine Inst Math Dept Numer Math UA-01601 Kiev 4 Ukraine Staatliche Studienakad Berufsakad Thuringen D-99817 Eisenach Germany

A new algorithm for nonlinear eigenvalue problems is proposed. The numerical technique is based on a perturbation of the coefficients of differential equation combined with the Adomian decomposition method for the nonlinear part. The approach provides an exponential convergence rate with a base which is inversely proportional to the index of the eigenvalue under consideration. The eigenpairs can be computed in parallel. Numerical examples are presented to support the theory. They are in good agreement with the spectral asymptotics obtained by other authors.

关键词： nonlinear eigenvalue problem parallel algorithm exponentially convergent algorithm

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Evaluating Graph Coloring on GPUs 11

Evaluating Graph Coloring on GPUs

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16th ACM Symposium on Principles and Practice of parallel Programming

作者： Grosset, A. V. Pascal Zhu, Peihong Liu, Shusen Venkatasubramanian, Suresh Hall, Mary Univ Utah Sch Comp Salt Lake City UT 84112 USA

ISBN: (纸本)9781450301190

This paper evaluates features of graph coloring algorithms implemented on graphics processing units (GPUs), comparing coloring heuristics and thread decompositions. As compared to prior work on graph coloring for other parallel architectures, we find that the large number of cores and relatively high global memory bandwidth of a GPU lead to different strategies for the parallel implementation. Specifically, we find that a simple uniform block partitioning is very effective on GPUs and our parallel coloring heuristics lead to the same or fewer colors than prior approaches for distributed-memory cluster architecture. Our algorithm resolves many coloring conflicts across partitioned blocks on the GPU by iterating through the coloring process, before returning to the CPU to resolve remaining conflicts. With this approach we get as few color (if not fewer) than the best sequential graph coloring algorithm and performance is close to the fastest sequential graph coloring algorithms which have poor color quality.

关键词： algorithms Performance Graph coloring parallel algorithm GPU CUDA

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Air pollution modelling, sensitivity analysis and parallel implementation

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INTERNATIONAL JOURNAL OF ENVIRONMENT AND POLLUTION 2011年第1-2期46卷 83-96页

作者： Ostromsky, Tzvetan Dimov, Ivan Georgieva, Rayna Zlatev, Zahari Bulgarian Acad Sci Dept Parallel Algorithms Inst Informat & Commun Technol BU-1113 Sofia Bulgaria Aarhus Univ Natl Environm Res Inst DK-4000 Roskilde Denmark

A new approach for Sensitivity Analysis (SA) in the field of air pollution modelling is proposed and applied to the Unified Danish Eulerian Model (UNI-DEM), a large-scale air pollution model. The SA requires numerous model experiments with different values of the studied parameters. By simultaneous variation of these parameters we produce a set of multidimensional discrete functions. These huge computational tasks require extensive resources of storage and CPU time. A highly parallel implementation of UNI-DEM has been created for this purpose and implemented on two powerful supercomputers. Some details of this implementation and numerical results on these supercomputers are presented.

关键词： SA sensitivity analysis Monte Carlo methods air pollution model parallel algorithm

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Rescue Robot Navigation in Grid Computing Environment

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International Conference on Materials Engineering for Advanced Technologies (ICMEAT2011)

作者： Wang, Wei Wang, Huiyan Jia, Shenjie Wei, Shimin Inst Disaster Prevent Sci & Technol Dept Instrument Beijing Peoples R China

ISBN: (纸本)9783037851517

To obtain the optimal path in a unknown disaster field,a rescue robot needs to build an environment map. The information of the disaster field is collected by the sonsors of different robots, all signal from sensors (mounted on all robots and signal form GPS) are sent to the bakeside parllel processors with wireless network. A grid computing environment serves as the backside parallel processors with Globus Toolkit, the grid computing processor process all the signals and construct the global map to help robot for navigation path planning. The rescue robot get control signal from the grid computing processor with wireless network,thus, the robot is not necessary to be sophisticated. New computing methods are given for parallel algorithm on grid environment. The navigation control is implemented with the cooperation among heterogeneous agents, the advantages of large seale computing on grid are shown.

关键词： grid computing globus Toolkit rescue robot wireless network parallel algorithm

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A parallel Approach to Concolic Testing with Low-cost Synchronization

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ELECTRONIC NOTES IN THEORETICAL COMPUTER SCIENCE 2011年 274卷 83-96页

作者： Yu, Xiao Sun, Shuai Pu, Geguang Jiang, Siyuan Wang, Zheng East China Normal Univ Shanghai Key Lab Trustworthy Comp Shanghai Peoples R China

This paper presents a practical approach to parallelize the test data generation algorithm by which computing resources can be fully used. The test data generation approach that we are using is based on the dynamic symbolic execution (concolic testing). The basic idea of parallelizing the algorithm is to distribute analysis processes of different paths to different computing units. Although a centralized scheduler with several sub processes can directly achieve the goal of parallelism, it may cause global idle time when parallel processes frequently end at same time. In our approach, a runtime deterministic scheduler is introduced to reduce the potential global idle time. Our experiments show some notable results when using a proper scheduling function. Compared with the sequential concolic testing, our approach can save nearly 70% computing time in some cases on a system with eight CPU cores from our experiments.

关键词： parallel algorithm Automatic Test Generation Symbolic Execution

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parallel estimation of the cost function for the flexible scheduling problemI

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Procedia Computer Science 2011年 4卷 2236-2245页

作者： Wojciech Boźejko Mariusz Uchroński Mieczysław Wodecki Institute of Computer Engineering Control and Robotics Wrocław University of Technology Janiszewskiego 11-17 50-372 Wrocław Poland Institute of Computer Science University of Wrocław Joliot-Curie 15 50-383 Wrocław 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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Efficient nonlinear optimization with rigorous models for large scale industrial chemical processes

Efficient nonlinear optimization with rigorous models for la...

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作者： Zhu, Yu Texas A&M University

学位级别：Ph.D.

Large scale nonlinear programming (NLP) has proven to be an effective framework for obtaining profit gains through optimal process design and operations in chemical engineering. While the classical SQP and Interior Point methods have been successfully applied to solve many optimization problems, the focus of both academia and industry on larger and more complicated problems requires further development of numerical algorithms which can provide improved computational efficiency. The primary purpose of this dissertation is to develop effective problem formulations and an advanced numerical algorithms for efficient solution of these challenging problems. As problem sizes increase, there is a need for tailored algorithms that can exploit problem specific structure. Furthermore, computer chip manufacturers are no longer focusing on increased clock-speeds, but rather on hyperthreading and multi-core architectures. Therefore, to see continued performance improvement, we must focus on algorithms that can exploit emerging parallel computing architectures. In this dissertation, we develop an advanced parallel solution strategy for nonlinear programming problems with block-angular structure. The effectiveness of this and modern off-the-shelf tools are demonstrated on a wide range of problem classes. Here, we treat optimal design, optimal operation, dynamic optimization, and parameter estimation. Two case studies (air separation units and heat-integrated columns) are investigated to deal with design under uncertainty with rigorous models. For optimal operation, this dissertation takes cryogenic air separation units as a primary case study and focuses on formulations for handling uncertain product demands, contractual constraints on customer satisfaction levels, and variable power pricing. Multiperiod formulations provide operating plans that consider inventory to meet customer demands and improve profits. In the area of dynamic optimization, optimal reference trajectories are d

关键词： nonlinear optimization air separation design under uncertainty parallel algorithm water network rigorous model Thesis

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New parallel N-Input Voting for Large Scale Fault-Tolerant Control Systems

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Journal of Electronic Science and Technology 2011年第2期9卷 174-179页

作者： Abbas Karimi Faraneh Zarafshan Adznan B.Jantan S.A.R.Al-Haddad Department of Computer Engineering Faculty of EngineeringArak BrachIslamic Azad University Department of Computer and Communication Systems Engineering Faculty of EngineeringPutra University

Average （mean） voter is one of the commonest voting methods suitable for decision making in highly-available and long-missions applications where the availability and the speed of the system are *** this paper,a new generation of average voter based on parallel algorithms and parallel random access machine（PRAM） structure are *** analysis shows that this algorithm is optimal due to its improved time complexity,speed-up,and efficiency and is especially appropriate for applications where the size of input space is large.

关键词： Divide and conquer fault-tolerant parallel algorithm voting algorithm.

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The Research and Application of parallel Generalized Minimal Residual algorithm Based on Grid MPI parallel Running Framework 1

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International Conference on Information Computing and Applications

作者： Tang, Yun Luo, Junsong Hao, Yajuan Chengdu Univ Technol Coll Informat Engn Chengdu 610000 Peoples R China Yanshan Univ Coll Sci Qingdao 066004 Peoples R China

ISBN: (数字)9783642161674

ISBN: (纸本)9783642161667

Through the research of MPI's theory and features, the G-MPI parallel program design and running framework have been constructed. Afterwards the design and communication cost of GMRES (m) algorithm has been studied, so one parallel numerical algorithm, with coarse granularity and low communication cost which is applied to solving the large elastic problems by using boundary element method, has been presented. Through the comparison with the result of the traditional parallel GMRES (m) in MPI, the new parallel algorithm in G-MPI has comparatively higher calculation accuracy and calculation efficiency.

关键词： G-MPI parallel Running Framework Boundary Element GMRES (m) algorithm parallel algorithm Communication Cost

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parallel Implementation of Multidimensional Scaling algorithm Based on Particle Dynamics

Parallel Implementation of Multidimensional Scaling Algorith...

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8th International Conference on parallel Processing and Applied Mathematics

作者： Pawliczek, Piotr Dzwinel, Witold AGH Univ Sci & Technol Inst Comp Sci Krakow Poland

ISBN: (纸本)9783642143892

We propose here a parallel implementation of multidimensional scaling (MDS) method which can be used for visualization of large datasets of multidimensional data.. Unlike in traditional approaches, which employ classical minimization methods for finding the global optimum of the "stress function", we use a heuristic based on particle dynamics. This method allows avoiding local minima and is convergent to the global one. However, due to its O(N-2) complexity, the application of this method in data mining problems involving large datasets requires efficient parallel codes. We show that employing both optimized Taylor's algorithm and hybridized model of parallel computations, our solver is efficient enough to visualize multidimensional data sets consisting of 10(4) feature vectors in time of minutes.

关键词： data mining multidimensional scaling method of particles parallel algorithm

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