Parallel Algorithms in Computational Science

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Techniques and algorithms that have been developed for simulations on parallel computers. Attention is focused on the Monte Carlo and molecular dynamics methods, and examples of replication algorithms, geometrically parallelized algorithms and data parallel algorithms are discussed. For physicists, mathematicians, chemists and biologists who want to make optimum use of the available parallelism. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9783642762673
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 12/8/2011
  • Series: Springer Series in Information Sciences, #24
  • Edition description: Softcover reprint of the original 1st ed. 1991
  • Edition number: 1
  • Pages: 183
  • Product dimensions: 6.14 (w) x 9.21 (h) x 0.42 (d)

Table of Contents

1. Introduction.- 2. Computer Simulation Methods.- 2.1 Essential Features of Simulation Methods.- 2.1.1 Ensemble Averages on a Computer.- 2.1.2 Simulation Algorithms.- 2.2 The Monte Carlo Algorithm.- 2.2.1 Simple Sampling.- 2.2.2 Importance Sampling.- 2.2.3 Interpretation of the Monte Carlo Process as a Dynamical Process.- 2.3 Molecular Dynamics.- 2.3.1 The Microcanonical Ensemble.- 2.3.2 Discretization and Systematic Effects.- 2.3.3 Molecular Dynamics Algorithms.- 2.4 Hybrid Molecular Dynamics.- 2.5 Accuracy Considerations and Finite-Size Problems.- 2.5.1 Choosing the Boundary Conditions.- 2.5.2 Effects of Finite Simulation Time.- 2.5.3 Statistical Errors and Self-Averaging.- 2.5.4 Finite-Size Scaling: Using Finite-Size Effects.- 2.6 Monte Carlo Algorithm for the Ising Model.- 2.6.1 The Ising Model.- 2.6.2 Implementing the Monte Carlo Algorithm for the Ising Model.- 2.6.3 The Swendsen-Wang Algorithm and the Equivalence Between the Ising Model and Percolation.- 2.6.4 Cluster Identification.- 2.6.5 Other Cluster Update Algorithms.- 3. Physics and Parallelism.- 4. Concepts of Parallelism.- 4.1 Some Basic Definitions.- 4.2 The Complexity of Computation.- 4.3 More on Models and Methods.- 4.4 Performance Measurements.- 5. Parallel Machines and Languages.- 5.1 General Purpose Parallel Computers.- 5.1.1 Processor Concepts.- 5.1.2 Communication Networks.- 5.2 Parallel Machines for Special Physics Problems.- 5.2.1 Monte Carlo Machines.- 5.2.2 Molecular Dynamics Computers.- 5.3 Languages for Parallel Computers.- 5.4 The Matching Problem.- 6. Replication Algorithms.- 7. Geometrically Parallel Algorithms.- 7.1 Geometric Parallelization.- 7.2 Strips, Squares and Checker-Boards.- 7.2.1 Detailed Balance and the Checker-Board.- 7.2.2 Strips.- 7.2.3 Squares.- 7.2.4 Communication Procedures.- 7.2.5 Timing and Efficiency Considerations.- 7.2.6 Geometric Parallelism in Higher Dimensions.- 7.3 Non-local and Cluster Algorithms.- 7.3.1 Parallel Algorithms for Cluster Identification.- 7.3.2 The Public Stack Cluster Algorithm.- 7.3.3 The Binary Tree Cluster Algorithm.- 7.3.4 Performance Measurements.- 7.4 Parallel Molecular Dynamics Algorithms.- 7.4.1 Short-Range vs Long-Range Interactions.- 7.4.2 A Geometrically Parallelized Algorithm for Molecular Dynamics.- 7.5 Hybrid Molecular Dynamics.- 7.6 Polymers on the Lattice.- 7.6.1 Single Polymers.- 7.6.2 Dense Polymer Systems.- 7.7 Off-Lattice Polymers.- 7.8 Hybrid Molecular Dynamics for Polymers.- 7.9 Limits of Geometric Parallelization.- 8. Data Parallel Algorithms.- 8.1 Data Parallel Algorithm for Long-Range Interactions.- 8.2 Polymers.- 9. Introduction to a Parallel Language.- 9.1 Transputer-Based Parallel Machines.- 9.2 Parallel Programming in Occam.- 9.2.1 The Process and the Channel Concepts.- 9.2.2 Two Elementary Processes.- 9.2.3 A Trivial Example ..- 9.2.4 Repetition and the Conditional.- 9.2.5 An Occam Program for the Ising Model.- 9.2.6 More on Choices and Selection.- 9.2.7 Further Language Elements.- 9.2.8 Arithmetic.- 9.2.9 Placements.- Appendices.- A. A Parallel Ising Model Program.- B. Random Number Generator.- C. A Parallel Molecular Dynamics Program.- References.
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