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Performance optimization of a de-centralized task allocation protocol via bandwidth and buffer management

Performance optimization of a de-centralized task allocation...

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international workshop on Challenges of Large applications in Distributed Environments

作者： B. Hong V.K. Prasanna Department of Electrical Engineering University of Southern California Los Angeles CA USA

Sharing the resources among various users and the lack of a centralized control are two key characteristics of many distributed heterogeneous computing systems. A critical challenge for designing applications in such systems is to coordinate the resources in a decentralized fashion while adapting to the changes in the system. In this paper, we consider the computation of a large set of equal-sized independent tasks. This represents the computation paradigm for a variety of large scale applications such as SETI@home and Monte Carlo simulations. We focus on the performance optimization for a decentralized adaptive task allocation protocol. We develop a bandwidth allocation strategy based on our decentralized task allocation algorithm, and a simple task buffer management policy. Simulation results show that our task allocation protocol achieves close to the optimal system throughput.

关键词： optimization Protocols Bandwidth Centralized control Throughput Signal processing algorithms Distributed computing Computer networks Data flow computing Resource management

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workshop 14: iWAPT Automatic Performance Tuning

Workshop 14: iWAPT Automatic Performance Tuning

引用

IEEE international Symposium on Parallel and Distributed Processing workshops and Phd Forum (IPDPSW)

作者： I-Hsin Chung Kazuhiko Komatsu IBM T. J. Watson Research Center USA Tohoku University Japan

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

iWAPT (international workshop on Automatic Performance Tuning) is a series of workshops that focus on research and techniques related to performance sustainability issues. The series provides an opportunity for researchers and users of automatic performance tuning (AT) technologies to exchange ideas and experiences acquired when applying such technologies to improve the performance of algorithms, libraries, and applications; in particular, on cutting edge computing platforms. Topics of interest include performance modeling; adaptive algorithms; autotuned numerical algorithms; libraries and scientific applications; empirical compilation; automated code generation; frameworks and theories of AT and software optimization; autonomic computing; and context-aware computing. iWAPT 2020 is the fifteenth in a series of successful workshops devoted to AT. The series started in Tokyo in 2006.

关键词： Conferences Tuning Software algorithms Libraries Software Computer science Distributed processing

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Rule-based parallel query optimization for OQL using a parallelism extraction technique

Rule-based parallel query optimization for OQL using a paral...

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international workshop on Database and Expert Systems applications

作者： S.F. Mendes P.R. Falcone Sampaio Departmento de Computacao Campus do Pici Universidade Federal do Ceara Fortaleza Brazil Computer Science Department University of Manchester Institute of Science and Technology UK

This work deals with query optimization far parallel object-oriented databases using rule-based optimizers. The solution enacted departs from an existing non-parallel database query optimizer and extends it with a parallelism extraction module that modifies the original query optimizer to generate query plans for a parallel OODBMS architecture. The ideas are applied in the design of a parallel optimizer for OQL. The implementation approach shows that the parallelism extraction technique combined with a rule-based optimizer generator tool can help to produce a parallel optimizer from a non-parallel one with minimum effort.

关键词： Query processing Algebra Design optimization Resource management Parallel architectures Parallel processing Computer science Read only memory Data models Parallel algorithms

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Determination of microscopic parameters of piezoceramic materials under electrical loading using genetic algorithm

Determination of microscopic parameters of piezoceramic mate...

引用

IEEE international Symposium on applications of Ferroelectrics (ISAF)

作者： J. Bustillo M. Domenjoud J. Fortineau G. Gautier M. Lethiecq GREMAN UMR 7347 University of Tours CEA Tours France

The purpose of this study is the characterization of microscopic parameters of piezoceramic materials, such as spontaneous polarization and spontaneous strain. In previous works, a model has been developed by bridging characteristics of microscopic domain distribution into the macroscopic behavior. It reproduces longitudinal strain and electrical displacement as a function of uniaxial electrical loading, according to material parameters and applied electrical field. optimization is performed between theoretical and experimental hysteresis curves. This method is based on a genetic algorithm procedure in order to ensure robust convergence even if the system is multimodal. Materials used in this study are PLZT8/65/35 and PZT-5A. After optimization, experimental curves are well fitted to theoretical curves and a good agreement has been shown between retrieved parameters and values reported in literature. This validates domain wall modeling and genetic algorithm as an efficient way to characterize piezoceramic materials under harsh operating conditions.

关键词： Genetic algorithms Materials optimization Strain Loading Convergence Piezoelectric polarization

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PARMETAOPT — Parallel metaheuristics framework for combinatorial optimization problems

PARMETAOPT — Parallel metaheuristics framework for combinat...

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Plamenka Borovska Ognian Nakov Milena Lazarova Department of Computer Systems Technical University of Sofia Sofia Bulgaria

The paper presents an experimental parallel metaheuristics framework for solving combinatorial optimization of grand challenge scientific and engineering problems that has been developed based on biologically inspired metaheuristics, modeling of social behavior and cultural evolution as well as trajectory-based methods. A prototype class library for metaheuristics is developed and several parallel computational models of metaheuristics for solving combinatorial optimization problems are implemented. The library contains implementations in C++ of parallel computational models for both population based and trajectory based metaheuristics. Some improvements in the parallel models are suggested and implemented in the library PARMETAOPT. The influence of the parameters on the performance of some of the parallel algorithms is analyzed using the developed parallel metaheuristics framework and performance tuning rules are suggested. The implementations are based on message passing with MPICH2 for the flat programming models and OpenMP API is used for multithreading in the hybrid programming models.

关键词： Biological system modeling Libraries Biology computing Concurrent computing Computational modeling optimization methods Design engineering Cultural differences Evolution (biology) Prototypes

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Offset-polygon annulus placement problems 5th

Offset-polygon annulus placement problems

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5th international workshop on algorithms and Data Structures, WADS 1997

作者： Barequet, Gill Briggs, Amy J. Dickerson, Matthew T. Goodrich, Michael T. Center for Geometric Computing Dept. of Computer Science Johns Hopkins University BaltimoreMD21218 United States Department of Mathematics and Computer Science Middlebury College MiddleburyVT05753 United States

ISBN: (纸本)3540633073

In this paper we address several variants of the polygon annulus placement problem: given an input polygon P and a set S of points, find an optimal placement of P that maximizes the number of points in S that fall in a certain annulus region defined by P and some offset distance δ > 0. We address the following variants of the problem: placement of a convex polygon as well as a simple polygon;placement by translation only, or by a translation and a rotation;off-line and on-line versions of the corresponding decision problems;and decision as well as optimization versions of the problems: We present efFicient algorithms in each case. © Springer-Verlag Berlin Heidelberg 1997.

关键词： Robot applications

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Three Sub-Swarm Particle Swarm optimization Algorithm

Three Sub-Swarm Particle Swarm Optimization Algorithm

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international workshop on Intelligent Systems and applications, ISA

作者： Ming-Ming Bai Hui Sun Lie-Yang Wu Ze-Tao Jiang Wen-Huan Wu Dept. of Comput. Sci. & Technol. NanChang Inst. of Technol. Nanchang Nanchang Hangkong University Nanchang Hangkong University Nanchang Jiangxi CN

In order to overcome the drawback of the standard PSO, such as being subject to falling into local optimization, an improved PSO algorithm based on three sub-swarms exchange is proposed. Firstly the method divides the whole swarm into three sub-swarms which evolve jointly according to three different models, that is, one evolves with the standard PSO model, and the second with social only model and the third with cognition only model respectively. When the community evolution achieves the equilibrium state, we exchange some particles between the three different sub-swarms, which can increase the information exchange between the sub-swarms, improve the population diversity and reduce the possibility of getting local extreme value. The results of simulation show that the proposed algorithm in the paper has the better optimization performance than the standard PSO.

关键词： Particle swarm optimization Convergence Iterative algorithms Acceleration Sun Computer science Cognition Cultural differences Birds Testing

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Multi-Agent in Ant Colony Algorithm Approach for Solving Traveling Salesman Problem

Multi-Agent in Ant Colony Algorithm Approach for Solving Tra...

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international workshop on Intelligent Systems and applications, ISA

作者： Dong-Sheng Xu Shi-Liang Yan Department of Information Technology Yulin University Yulin China Engineering & Technology Center Southwest University of Science and Technology Mianyang China

Traveling salesman problem (TSP) is a very hard and classical optimization problem in the field of operations research, and often-used benchmark for new optimization techniques. This paper will to bring up multi-agent approach for solving the TSP based on data mining algorithm, for the extraction of knowledge from a large set of TSP. The proposed approach supports the distributed solving to the TSP. It divides into three-tier, the first tier is ant colony optimization agent; the second-tier is genetic algorithm agent; and the third tier is fast local searching agent. In using an ant colony algorithm (ACA) for the TSP, an attribute-oriented induction methodology was used to explore the relationship between an operations' sequence and its attributes and a set of rules has been developed. These rules can duplicate the ACA's performance on identical problems. Ultimately, the experimental results have shown that the proposed hybrid approach has good performance with respect to the quality of solution and the speed of computation.

关键词： Traveling salesman problems Data mining Ant colony optimization Genetic algorithms Cities and towns Neural networks Information technology Operations research Data engineering Benchmark testing

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Hardware Software Partitioning using Particle Swarm optimization Technique

Hardware Software Partitioning using Particle Swarm Optimiza...

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IEEE international workshop on System-on-Chip for Real-Time applications

作者： M. B. Abdelhalim A. E. Salama S. E.-D. Habib Faculty of Engineering Cairo University Giza Egypt

ISBN: (纸本)1424408989

In this paper the authors investigate the application of the particle swarm optimization (PSO) technique for solving the hardware/software partitioning problem. The PSO is attractive for the hardware/software partitioning problem as it offers reasonable coverage of the design space together with O(n) main loop's execution time, where n is the number of proposed solutions that will evolve to provide the final solution. The authors carried out several tests on a hypothetical, relatively-large hardware/software partitioning problem using the PSO algorithm as well as the genetic algorithm (GA), which is another evolutionary technique. The authors found that PSO outperforms GA in the cost function and the execution time. For the case of unconstrained design problem, the authors tested several hybrid combinations of PSO and GA algorithm; including PSO then GA, GA then PSO, GA followed by GA, and finally PSO followed by PSO. We found that a PSO followed by GA algorithm gives small or no improvement at all, while a GA then PSO algorithm gives the same results as the PSO alone. The PSO algorithm followed by another PSO round gave the best result as it allows another round of domain exploration. The second PSO round assign new randomized velocities to the particles, while keeping best particle positions obtained in the first round. The paper proposes to name this successive PSO algorithm as the re-excited PSO algorithm

关键词： Hardware Particle swarm optimization Partitioning algorithms Genetic algorithms Costs Clustering algorithms Application software Embedded system Software algorithms Embedded software

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A Memetic Differential Evolution Algorithm for Continuous optimization

A Memetic Differential Evolution Algorithm for Continuous Op...

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international Conference on Intelligent Systems Design and applications (ISDA)

作者： Santiago Muelas Antonio La Torre José-María Peña CeSViMa Parque Tecnológico UPM Campus de Montegancedo Facultad de Informática Universidad Politécnica de Madrid Boadilla del Monte Madrid Spain CeSViMa Pozuelo de Alarcon Spain

Continuous optimization is one of the most active research lines in evolutionary and metaheuristic algorithms. Since CEC 2005 and CEC 2008 competitions, many different algorithms have been proposed to solve continuous problems. Despite there exist very good algorithms reporting high quality results for a given dimension, the scalability of the search methods is still an open issue. Finding an algorithm with competitive results in the range of 50 to 500 dimensions is a difficult achievement. This contribution explores the use of a hybrid memetic algorithm based on the differential evolution algorithm, named MDE-DC. The proposed algorithm combines the explorative/exploitative strength of two heuristic search methods, that separately obtain very competitive results in either low or high dimensional problems. This paper uses the benchmark problems and conditions required for the workshop on ¿evolutionary algorithms and other metaheuristics for "Continuous optimization Problems - A Scalability Test¿ chaired by Francisco Herrera and Manuel Lozano.

关键词： Evolution (biology) Search methods Large-scale systems Design optimization Scalability Evolutionary computation optimization methods Intelligent systems Benchmark testing Biology

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