Extensão da Ferramenta de Apoio à Programação Paralela (F.A.P.P.) para ambientes paralelos virtuais.

Jaquie, Kalinka Regina Lucas

doi:10.11606/d.55.1999.tde-08022001-095456

Cited by 4 publications

(6 citation statements)

References 17 publications

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“…In order to try making the development of parallel programs less complicate, Jaquie [2] presents one of the few environments to produce parallel code for Parallel Virtual Machine (PVM) and Message Passing Interface (MPI) [3] at the end of 90's. Since then, some works have been done, like a visual approach for creating distributed applications in Java [4] and a parallel code generator for C-MPI [5] and [6].…”

Section: Introductionmentioning

confidence: 99%

A tool for creating parallel swarm algorithms automatically on multi-core computers

Costa¹,

Cortes²

2019

Learn. Nonlin. Mod.

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Meta-heuristics are usually bio-inspired algorithms (based on genes or social behaviors) that are used for solving optimization problems in a variety of fields and applications. The basic principle of a meta-heuristic, such as genetic algorithms, differential evolutions, particle swarm optimization, etc., is to simulate the pressure that the environment applies to individuals resulting in the survival of the best ones. Regardless of which meta-heuristic is being used, the more complex the problem, the more time consuming the algorithm. In this context, parallel computing represents an attractive way of tackling the necessity for computational power. On the other hand, parallel computing introduces new issues that the programmers have to deal with, such as synchronization and the proper exploration of parallel algorithms/models. To avoid these problems, and at the same time, to provide a fast development of parallel swarm algorithms, this work presents a tool for creating parallel code using Parallel Particle Swarm Optimization (PSO) Algorithms in Java. The generator considers three models of parallelism: master-slaves, island and hierarchical. Experiments in the created code showed that a speedup of 5.3 could be reached in the Island model with 2000 iterations using Griewank's function. Moreover, using a cost estimation model (COCOMO) we showed that our tool could save from 4.4 to 14.5 person/month on programming effort.

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Section: Introductionmentioning

confidence: 99%

A tool for creating parallel swarm algorithms automatically on multi-core computers

Costa¹,

Cortes²

2019

Learn. Nonlin. Mod.

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“…In order to overcome the problems raised by parallel computing, Jaquie [3], Malacarne [4], Passini & Dotti [5] and Rodrigues et. al.…”

Section: Introductionmentioning

confidence: 99%

A Java-Based Code Generator for Parallel Evolutionary Algorithms

Silva¹,

Cortes²,

Silva³

2016

Anais Do 11. Congresso Brasileiro De Inteligência Computacional

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Evolutionary Algorithms (EAs) are able to find out solutions in many fields and complex disciplines. Parallel Evolutionary Algorithms (PEAs) solve many kinds of problems, as well; moreover it overcomes problems with run time constraints when the problems being solved are much more complex. Thereby, we can state that PEAs can be efficient and faster than a regular EA. On the other hand, parallel programming brings new substantial problems to the developers that have to deal with synchronization and a different debugging process, increasing the learning curve and the programming efforts. In this context, we developed a web-based software that automatically creates java code for parallel genetic algorithms and parallel evolutionary strategies, reducing the required time to develop this kind of application. Two parallel models were implemented, the island model and master-slave. Further, the design patterns strategy and observer were applied in the generation of the PEAs code in order to increase the software maintainability and legibility. An experiment was conducted in order to show how our software can reduce the time consuming of an EA.

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“…Tal arquitetura é hipotética [11], imaginando uma situação em que várias instruções processariam um único dado. Não existe nenhuma implementação prática dessa estrutura (Figura 2.2).…”

Section: Misd -Multiple Instruction Single Dataunclassified

“…Em computação paralela o termo Speedup indica o quão rápido um algoritmo paralelo é se comparado ao mesmo código de programação, porém executando-se de forma sequencial [11].…”

Section: Speedupunclassified

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Técnicas de programação e avaliação de desempenho de solvers de sistemas de equações lineares em sistemas computacionais de alto desempenho.

Ferreira¹

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Agradeço ainda à Fatec por ter concedido afastamento de parte de minhas atividades e a Capes pelo apoio financeiro.Agradeço por fim a todos aqueles que contribuíram direta ou indiretamente para a realização deste trabalho.Muito obrigado a todos. RESUMOOs problemas de engenharia atualmente têm aumentado a sua ordem de grandeza, por conta de diversos fatores. A modelagem em ambiente computacional dos mesmos esbarra em limitações, como grandes quantidades de tempo de processamento gastos com diversas simulações da modelagem e a pouca quantidade de memória disponível para alocar propriamente os problemas.A resolução de grandes sistemas de equações lineares, comumente abordado nos problemas atuais de engenharia, necessita da exploração das duas situações mencionadas anteriormente.A subárea computacional que permite explorar a redução do tempo e a possibilidade de alocação na memória de tais problemas é chamada de computação de alto desempenho.O objetivo deste trabalho é ilustrar o uso de softwares de resolução de sistemas de equações lineares, chamados de solvers, projetados para os ambientes computacionais de alto desempenho, testando-os e avaliando-os em um conjunto de matrizes conhecido, bem como abordar os detalhes computacionais envolvidos em tais procedimentos.Palavras-chave: solver, computação de alto desempenho, sistema de equações lineares. ABSTRACTEngineering problems today have increased their order of magnitude, due to several factors. Modeling these problems with computers brings up certain limitations, as the amount of processing time needed for several simulations and the lack of available memory to properly allocate them.The resolution of large systems of linear equations, commonly discussed in current engineering problems, needs the exploration of the two situations mentioned above.The subarea that allows exploring the computational time reduction and the possibility of allocating memory in such problems is called high performance computing.The aim of this paper is to illustrate the use of software to solve systems of linear equations, called solvers, designed for high performance computing environments, to test and evaluate them for a set of matrices as well as to address the computational details involved in such procedures.Palavras: solver, high performance computing, systems of linear equations.

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Extensão da Ferramenta de Apoio à Programação Paralela (F.A.P.P.) para ambientes paralelos virtuais.

Cited by 4 publications

References 17 publications

A tool for creating parallel swarm algorithms automatically on multi-core computers

A tool for creating parallel swarm algorithms automatically on multi-core computers

A Java-Based Code Generator for Parallel Evolutionary Algorithms

Técnicas de programação e avaliação de desempenho de solvers de sistemas de equações lineares em sistemas computacionais de alto desempenho.

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