Models for Parallel and Distributed Computation

1. Front Matter

   Pages i-xx

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2. Models of Parallel Computation

   1. Front Matter

      Pages 1-1

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   2. Introduction to the Complexity of Parallel Algorithms

      • Michel Cosnard

      Pages 3-25

   3. The Combinatorics of Resource Sharing

      • Valmir C. Barbosa

      Pages 27-52

   4. Solving the Static Task Scheduling Problem for Real Machines

      • Cristina Boeres, Vinod E. F. Rebello

      Pages 53-84

   5. Predictable Parallel Performance: The BSP Model

      • D. B. Skillicorn

      Pages 85-115

   6. Discrete Computing with Coarse Grained Parallel Systems: An Algorithmic Approach

      • Afonso Ferreira, Isabelle Guérin-Lassous

      Pages 117-143

3. Parallel Applications

   1. Front Matter

      Pages 145-145

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   2. Parallel Graph Algorithms for Coarse-Grained Multicomputers

      • S. W. Song

      Pages 147-178

   3. Parallel Metaheuristics for Combinatorial Optimization

      • Sandra Duni Ekşiog̃lu, Panos M. Pardalos, Mauricio G. C. Resende

      Pages 179-206

   4. Parallelism in Logic Programming and Scheduling Issues

      • Cláudio F. R. Geyer, Patrícia Kayser Vargas, Inês de Castro Dutra

      Pages 207-241

   5. Parallel Asynchronous Team Algorithms

      • Benjamín Barán

      Pages 243-277

   6. Parallel Numerical Methods for Differential Equations

      • Carlos A. de Moura

      Pages 279-313

4. Back Matter

   Pages 315-323

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Parallel and distributed computation has been gaining a great lot of attention in the last decades. During this period, the advances attained in computing and communication technologies, and the reduction in the costs of those technolo­ gies, played a central role in the rapid growth of the interest in the use of parallel and distributed computation in a number of areas of engineering and sciences. Many actual applications have been successfully implemented in various plat­ forms varying from pure shared-memory to totally distributed models, passing through hybrid approaches such as distributed-shared memory architectures. Parallel and distributed computation differs from dassical sequential compu­ tation in some of the following major aspects: the number of processing units, independent local dock for each unit, the number of memory units, and the programming model. For representing this diversity, and depending on what level we are looking at the problem, researchers have proposed some models to abstract the main characteristics or parameters (physical components or logical mechanisms) of parallel computers. The problem of establishing a suitable model is to find a reasonable trade-off among simplicity, power of expression and universality. Then, be able to study and analyze more precisely the behavior of parallel applications.

• Scheduling
• algorithms
• combinatorial optimization
• complexity
• computer
• computer science
• logic
• logic programming
• numerical methods
• optimization
• programming
• combinatorics

• Universidade Federal do Ceará, Brazil

  Ricardo Corrêa, Mario Fiallos, Fernando Gomes

• Universidade Federal do Rio de Janeiro, Brazil

  Inês Dutra

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