Thesis (Ph. D.)--Mississippi State University. Department of Computational Engineering. / Title from title screen. Includes bibliographical references.

Thesis (M. Phil.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 63-65).

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

We take a novel perspective on real-life decision making problems involving binary activity-selection decisions that compete for scarce resources. The current literature in operations research approaches these problems by forming an optimal portfolio of activities that meets the specified resource constraints. However, often practitioners in industry and government do not take the optimal-portfolio approach. Instead, they form a rank-ordered list of activities and select those that have the highest priority. The academic literature tends to discredit such ranking schemes because they ignore dependencies among the activities. Practitioners, on the other hand, sometimes discredit the optimal-portfolio approach because if the problem parameters change, the set of activities that was once optimal no longer remains optimal. Even worse, the new optimal set of activities may exclude some of the previously optimal activities, which they may have already selected. Our approach takes both viewpoints into account. We rank activities considering both the uncertainty in the problem parameters and the optimal portfolio that will be obtained once the uncertainty is revealed. We use stochastic integer programming as a modeling framework. We develop several mathematical formulations and discuss their relative merits, comparing them theoretically and computationally. We also develop cutting planes for these formulations to improve computation times. To be able to handle larger real-life problem instances, we develop parallel branch-and-price algorithms for a capital budgeting application. Specifically, we construct a column-based reformulation, develop two branching strategies and a tabu search-based primal heuristic, propose two parallelization schemes, and compare these schemes on parallel computing environments using commercial and open-source software. We give applications of prioritization in facility location and capital budgeting problems. In the latter application, we rank maintenance and capital-improvement projects at the South Texas Project Nuclear Operating Company, a two-unit nuclear power plant in Wadsworth, Texas. We compare our approach with several ad hoc ranking schemes similar to those used in practice. / text

published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy

En este trabajo se realiza un análisis comparativo de las técnicas de programación paralela, en el que puede observarse cómo la evolución en el diseño de la solución a un mismo problema permite maximizar la performance de algoritmos paralelos en arquitecturas clúster de multicore. Este análisis se realiza utilizando un caso de estudio ampliamente estudiado en el área como lo es la multiplicación de matrices. Para ello, se implementaron diferentes soluciones para resolver el problema, partiendo de soluciones de alto nivel y bajo acoplamiento con la arquitectura, hasta llegar a soluciones de bajo nivel, dependientes de la arquitectura de prueba subyacente y que aprovechan las características de la misma, como por ejemplo la jerarquía de memoria. Las soluciones implementadas son una solución utilizando pasaje de mensajes, y tres soluciones híbridas teniendo en cuenta diferentes librerías de programación paralela (OpenMPI, Pthreads y OpenMP), y diferentes esquemas de descomposición de los datos. Asimismo, las pruebas de los algoritmos implementados se analizan desde distintos puntos de vista. Por un lado, escalando el tamaño del problema a resolver y por otro, escalando la cantidad de núcleos de procesamiento utilizados.

Šio darbo tikslai: sukurti, išanalizuoti ir suprojektuoti grafinę scenarijų kalbą, kuri leistų vartotojui nesunkiai piešimo būdu kurti sudėtingas programas, skirtas Grid sistemoms, suprojektuoti ir sukurti vartotojo sąsaja su programavimo galimybėmis, išanalizuoti tiriamos Grid sistemos trūkumus ir pasiūlyti alternatyvų sprendimą. / Goals of this work are: to analyze, make project of visual scenario language, which will provide ability for user easily create complicated programs for Grid system with drawing method, to analyze and make project of graphical user interface for the Grid system with ability to write program source, to analyze the Grid systems limitations and suggest alternative solution.

No description available.

No description available.

The development of multimedia technology, along with the emergence of parallel architectures, has revived the interest on dataflow programming for designing embedded systems. Indeed, dataflow programming offers a flexible development approach in order to build complex applications while expressing concurrency and parallelism explicitly. Paradoxically, most of the studies focus on static dataflow models of computation, even if a pragmatic development process requires the expressiveness and the practicality of a programming language based on dynamic dataflow models, such as the language included in the Reconfigurable Video Coding framework. In this thesis, we describe a complete development environment for dataflow programming that eases multimedia development for embedded multi-core platforms. This development environment is built upon a modular software architecture that benefits from modern software engineering techniques such as meta modeling and aspect-oriented programming. Then, we develop an optimized software implementation of dataflow programs targeting desktop and embedded multi-core platforms. Our implementation aims to bridge the gap between the practicality of the programming language and the efficiency of the execution. Finally, we present a set of runtime actors mapping/scheduling algorithms that enable the execution of dynamic dataflow programs over multi-core platforms with scalable performance.