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A memory consistency model for a language defines the order of memory operations performed by each thread in a parallel execution. Such a constraint is necessary to prevent the compiler and hardware optimizations from reordering certain memory operations, since such reordering might lead to unintuitive results. In this thesis, we propose a memory consistency model for Chapel, a parallel programming language from Cray Inc. Our memory model for Chapel is based on the idea of multiresolution and aims to provide a migration path from a program that is easy to reason about to a program that has better performance efficiency. Our model allows a programmer to write a parallel program with sequential consistency semantics, and then migrate to a performance-oriented version by increasingly changing different parts of the program to follow relaxed semantics. Sequential semantics helps in reasoning about the correctness of the parallel program and is provided by the strict sequential consistency model in our proposed memory model. The performance-oriented versions can be obtained either by using the compiler sequential consistency model, which maintains the sequential semantics, or by the relaxed consistency model, which maintains consistency only at global synchronization points. Our proposed memory model for Chapel thus combines strict sequential consistency model, compiler sequential consistency model and relaxed consistency model. We analyze the performance of the three consistency models by implementing three applications: Barnes-Hut, FFT and Random-Access in Chapel, and the hybrid model of MPI and Pthread. We conclude the following: The strict sequential consistency model is the best model to determine algorithmic errors in the applications, though it leads to the worst performance; the relaxed consistency model gives the best performance among the three models, but relies on the programmer to enforce synchronization correctly; the performance of the compiler sequential model depends on accuracy of the dependence analysis performed by the compiler; the relative performance of the consistency models across Chapel and the hybrid programming model of MPI and Pthread are the same. This shows that our model is not tightly bound to Chapel and can be applied on other programming models/languages. / text

A execução de uma aplicação paralela, utilizando grades computacionais, necessita de um ambiente que permita a sua execução, além de realizar o seu gerenciamento, escalonamento e monitoramento. O ambiente de execução deve prover um modelo de processamento, composto pelos modelos de programação e de execução, no qual o objetivo é a exploração adequada das características das grades computacionais. Este trabalho objetiva a proposta de um modelo de processamento paralelo, baseado em variáveis compartilhadas, para grades computacionais, sendo composto por um modelo de execução apropriado para grades e pelo modelo de programação da linguagem paralela CPAR. O ambiente CPAR-Grid foi desenvolvido para executar aplicações paralelas em grades computacionais, abstraindo do usuário todas as características presentes em uma grade computacional. Os resultados obtidos mostram que este ambiente é uma solução eficiente para a execução de aplicações paralelas. / The execution of parallel applications, using grid computing, requires an environment that enables them to be executed, managed, scheduled and monitored. The execution environment must provide a processing model, consisting of programming and execution models, with the objective appropriately exploiting grid computing characteristics. This paper proposes a parallel processing model, based on shared variables for grid computing, consisting of an execution model that is appropriate for the grid and a CPAR parallel language programming model. The CPAR-Grid environment is designed to execute parallel applications in grid computing, where all the characteristics present in grid computing are transparent to users. The results show that this environment is an efficient solution for the execution of parallel applications.

A execução de uma aplicação paralela, utilizando grades computacionais, necessita de um ambiente que permita a sua execução, além de realizar o seu gerenciamento, escalonamento e monitoramento. O ambiente de execução deve prover um modelo de processamento, composto pelos modelos de programação e de execução, no qual o objetivo é a exploração adequada das características das grades computacionais. Este trabalho objetiva a proposta de um modelo de processamento paralelo, baseado em variáveis compartilhadas, para grades computacionais, sendo composto por um modelo de execução apropriado para grades e pelo modelo de programação da linguagem paralela CPAR. O ambiente CPAR-Grid foi desenvolvido para executar aplicações paralelas em grades computacionais, abstraindo do usuário todas as características presentes em uma grade computacional. Os resultados obtidos mostram que este ambiente é uma solução eficiente para a execução de aplicações paralelas. / The execution of parallel applications, using grid computing, requires an environment that enables them to be executed, managed, scheduled and monitored. The execution environment must provide a processing model, consisting of programming and execution models, with the objective appropriately exploiting grid computing characteristics. This paper proposes a parallel processing model, based on shared variables for grid computing, consisting of an execution model that is appropriate for the grid and a CPAR parallel language programming model. The CPAR-Grid environment is designed to execute parallel applications in grid computing, where all the characteristics present in grid computing are transparent to users. The results show that this environment is an efficient solution for the execution of parallel applications.