Parallel Processing and Parallel Algorithms: Theory and Computation - Softcover

Inhaltsangabe

1 Computer Architecture.- 1.1 Classification of Computer Architectures.- 1.2 Parallel Architectures.- 1.2.1 SIMD Architectures.- 1.2.2 MISD Architectures.- 1.2.3 MIMD Architectures.- 1.2.4 SIMD-MIMD Hybrid Architectures.- 1.3 Data Flow Architectures.- 1.3.1 Static Data Flow Architectures.- 1.3.2 Reconfigurable Static Data Flow Architectures.- 1.3.3 Dynamic Data Flow Architectures.- Summary.- Exercises.- 2 Components of Parallel Computers.- 2.1 Memory.- 2.2 Interconnection Networks.- 2.2.1 Linear and Ring.- 2.2.2 Shuffle Exchange.- 2.2.3 Two-Dimensional Mesh.- 2.2.4 Hypercube or n-Cube.- 2.2.5 Star.- 2.2.6 De Bruijn.- 2.2.7 Binary Tree.- 2.2.8 Delta.- 2.2.9 Butterfly.- 2.2.10 Omega.- 2.2.11 Pyramid/t1>.- 2.3 Goodness Measures for Interconnection Networks.- 2.4 Compilers.- 2.5 Operating Systems.- 2.6 Input and Output Constraints.- Summary.- Exercises.- 3 Principles of Parallel Programming.- 3.1 Programming Languages for Parallel Processing.- 3.2 Precedence Graph of a Process.- 3.3 Data Parallelism Versus Control Parallelism.- 3.4 Message Passing Versus Shared Address Space.- 3.5 Mapping.- 3.5.1 Mapping to Asynchronous Architecture.- 3.5.2 Mapping to Synchronous Architecture.- 3.5.3 Mapping to Distributed Architecture.- 3.6 Granularity.- 3.6.1 Program Level Parallelism.- 3.6.2 Procedure Level Parallelism.- 3.6.3 Statement Level Parallelism.- Summary.- Exercises.- 4 Parallel Programming Approaches.- 4.1 Parallel Programming with UNIX.- 4.2 Parallel Programming with PCN.- 4.3 Parallel Programming with PVM.- 4.4 Parallel Programming with C-Linda.- 4.5 Parallel Programming with EPT.- 4.6 Parallel Programming with CHARM.- Summary.- 5 Principles of Parallel Algorithm Design.- 5.1 Design Approaches.- 5.2 Design Issues.- 5.3 Performance Measures and Analysis.- 5.3.1 Amdahl's and Gustafson's Laws.- 5.3.2 Speedup Factor and Efficiency.- 5.3.3 Cost and Utilization.- 5.3.4 Execution Rate and Redundancy.- 5.4 Complexities.- 5.4.1 Sequential Computation Complexity.- 5.4.2 Parallel Computation Complexity.- 5.5 Anomalies in Parallel Algorithms.- 5.6 Pseudocode Conventions for Parallel Algorithms.- 5.7 Comparison of SIMD and MIMD Algorithms.- Summary.- Exercises.- 6 Parallel Graph Algorithms.- 6.1 Connected Components.- 6.2 Paths and All-Pairs Shortest Paths.- 6.3 Minimum Spanning Trees and Forests.- 6.4 Traveling Salesman Problem.- 6.5 Cycles in a Graph.- 6.6 Coloring of Graphs.- Summary.- Exercises.- 7 Parallel Search Algorithms.- 7.1 Divide and Conquer.- 7.2 Depth-First Search.- 7.3 Breadth-First Search.- 7.4 Best-First Search.- 7.5 Branch-and-Bound Search.- 7.6 Alpha-Beta Minimax Search.- Summary.- Exercises.- 8 Parallel Computational Algorithms.- 8.1 Prefix Computation.- 8.2 Transitive Closure.- 8.3 Matrix Computation.- 8.3.1 Matrix-Vector Multiplication.- 8.3.2 Matrix-Matrix Multiplication.- 8.4 System of Linear Equations.- 8.5 Computing Determinants.- 8.6 Expression Evaluation.- 8.7 Sorting.- Summary.- Exercises.- 9 Data Flow and Functional Programming.- 9.1 Data Flow Programming.- 9.1.1 Data Flow Programming Language Principles.- 9.1.2 Value-Oriented Algorithmic Language (VAL).- 9.2 Functional Programming.- 9.2.1 Functional Programming Language Principles.- 9.2.2 Stream and Iterations in Single Assignment Language (SISAL).- Summary.- 10 Asynchronous Parallel Programming.- 10.1 Parallel Programming with Ada.- 10.2 Parallel Programming with Occam.- 10.3 Parallel Programming with Modula-2.- Summary.- 11 Data Parallel Programming.- 11.1 Data Parallel Programming with C*.- 11.1.1 Parallel Variables.- 11.1.2 Parallel Operations.- 11.1.3 Parallel Communication.- 11.2 Data Parallel Programming with Fortran 90.- 11.2.1 Variable Declarations.- 11.2.2 Array Assignment Statements.- 11.2.3 Array Intrinsic Functions.- Summary.- Exercises.- 12 Artificial Intelligence and Parallel Processing.- 12.1 Production Systems.- 12.2 Reasoning Systems.- 12.3 Parallelism Analysis.- 12.4 Parallelizing AI Algorithms.- 12.5 Parallelizing AI Architectures.- 12.6 Para

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