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      Introduction to parallel computing : design and analysis of algorithms

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      https://www.riss.kr/link?id=M646451

      • 저자
      • 발행사항

        Redwood City, Calif. : Benjamin/Cummings Pub. Co., c1994

      • 발행연도

        1994

      • 작성언어

        영어

      • 주제어
      • DDC

        005.2 판사항(20)

      • ISBN

        0805331700

      • 자료형태

        일반단행본

      • 발행국(도시)

        California

      • 서명/저자사항

        Introduction to parallel computing : design and analysis of algorithms / Vipin Kumar ... [et al.].

      • 형태사항

        xv, 597 p. : ill. ; 25 cm.

      • 일반주기명

        Includes bibliographical references and indexes.

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      목차 (Table of Contents)

      • CONTENTS
      • Preface = xiii
      • CHAPTER 1 Introduction = 1
      • Introduction = 1
      • 1.1 What is parallel Computing? = 2
      • CONTENTS
      • Preface = xiii
      • CHAPTER 1 Introduction = 1
      • Introduction = 1
      • 1.1 What is parallel Computing? = 2
      • 1.2 The Scope of Parallel Computing = 3
      • 1.3 Issues in Parallel Computing = 4
      • 1.4 Organization and Contents of the Text = 5
      • 1.5 Bibliographic Remarks = 8
      • Problems = 9
      • References = 10
      • CHAPTER 2 Models of Parallel Computers = 15
      • 2.1 A Taxonomy of Parallel Architectures = 16
      • 2.1.1 Control Mechanism = 16
      • 2.1.2 Address-Space Organization = 18
      • 2.1.3 Interconnection Networks = 22
      • 2.1.4 Processor Granularity = 22
      • 2.2 An Idealized Parallel Computer = 23
      • 2.3 Dynamic Interconnection Networks = 24
      • 2.3.1. Crossbar Switching Networks = 24
      • 2.3.2 Bus-Based Networks = 25
      • 2.3.3 Multistage Interconnection Networks = 26
      • 2.4 Static Interconnection Networks = 30
      • 2.4.1 Types of Static Interconnection Networks = 30
      • 2.4.2 Evaluating Static Interconnection Networks = 37
      • 2.5 Embedding Other Networks into a Hypercube = 39
      • 2.5.1 Embedding a Linear Array into a Hypercube = 39
      • 2.5.2 Embedding a Mesh into a Hypercube = 41
      • 2.5.3 Embedding a Binary Tree into a Hypercube = 42
      • 2.6 Routing Mechanisms for Static Networks = 42
      • 2.7 Communication Costs in Static Interconnection Networks = 45
      • 2.7.1 Store-and-Forward Routing = 45
      • 2.7.2 Cut-Through Routing = 46
      • 2.8 Cost-Performance Tradeoffs = 48
      • 2.9 Architectural Models for Parallel Algorithm Design = 49
      • 2.10 Bibliographic Remarks = 51
      • Problems = 54
      • References = 60
      • CHAPTER 3 Basic Communication Operations = 65
      • 3.1 Simple Message Transfer between Two Processors = 66
      • 3.2 One-to-All Broadcast = 66
      • 3.2.1 Store-and-Forward Routing = 67
      • 3.2.2 cut-Through Routing = 75
      • 3.3. All-to-all Broadcast, Reduction, and Prefix Sums = 77
      • 3.3.1 Store-and-Forward Routing = 78
      • 3.3.2 Cut-Through Routing = 86
      • 3.4 One-to-All Personalized Communication = 88
      • 3.5 All-to-All Personalized Communication = 90
      • 3.5.1 Store-and-Forward Routing = 90
      • 3.5.2 Cut-Through Routing = 95
      • 3.6 Circular Shift = 98
      • 3.6.1 Store-and-Forward Routing = 98
      • 3.6.2 Cut-Through Routing = 101
      • 3.7 Faster Methods for Some Communication Operations = 101
      • 3.7.1 Routing Messages in Parts = 102
      • 3.7.2 All-Port Communication = 104
      • 3.7.3 Special Hardware for Global Operations = 105
      • 3.8 Summary = 106
      • 3.9 Bibliographic Remarks = 107
      • Problems = 108
      • References = 114
      • CHAPTER 4 Performance and Scalability of Parallel Systems = 117
      • 4.1 Performance Metrics for Parallel Systems = 117
      • 4.1.1 Run Time = 117
      • 4.1.2 Speedup = 118
      • 4.1.3 Efficiency = 120
      • 4.1.4 Cost = 120
      • 4.2 The Effect of Granularity and Data Mapping on Performance = 121
      • 4.3 The Scalability of Parallel Systems = 126
      • 4.4 The Isoefficiency Metric of Scalability = 128
      • 4.4.1 Problem Size = 129
      • 4.4.2 The Overhead Function = 129
      • 4.4.3 The Isoefficiency Function = 130
      • 4.4.4 Cost-Optimality and the Isoefficiency Function = 133
      • 4.4.5 A Lower Bound on the Isoefficiency Function = 134
      • 4.4.6 The Degree of Concurrency and the Isoefficiency Function = 134
      • 4.5 Sources of Parallel Overhead = 135
      • 4.5.1 Interprocessor Communication = 135
      • 4.5.2 Load Imbalance = 135
      • 4.5.3 Extra Computation = 136
      • 4.6 Minimum Execution Time and Minimum Cost-Optional Execution Time = 136
      • 4.7 Other Scalability Metrics and Bibliographic Remarks = 139
      • Problems = 141
      • References = 146
      • CHAPTER 5 Dense Matrix Algorithms = 151
      • 5.1 Mapping Matrices onto Processors = 151
      • 5.1.1 Striped Partitioning = 151
      • 5.1.2 Checkerboard Partitioning = 152
      • 5.2 Matrix Transposition = 153
      • 5.2.1 Checkerboard Partitioning = 154
      • 5.2.2 Striped Partitioning = 159
      • 5.3 Matrix-Vector Multiplication = 160
      • 5.3.1 Rowwise Striping = 160
      • 5.3.2 Checkerboard Partitioning = 163
      • 5.4. Matrix Multiplication = 168
      • 5.4.1 A Simple Parallel Algorithm = 169
      • 5.4.2 Cannon's Algorithm = 171
      • 5.4.3 Fox's Algorithm = 173
      • 5.4.4 The DNS Algorithm = 174
      • 5.5 Solving a System of Linear Equations = 178
      • 5.5.1 A Simple Gaussian Elimination Algorithm = 179
      • 5.5.2 Gaussian Elimination with Partial Pivoting = 192
      • 5.5.3 Solving a Triangular System :Back-Substitution = 195
      • 5.5.4 Numerical Considerations in Solving Systems of Linear Equations = 196
      • 5.6 Bibliographic Remarks = 197
      • Problems = 198
      • References = 204
      • CHAPTER 6 Sorting = 209
      • 6.1 Issues in Sorting on Parallel Computers = 210
      • 6.1.1 Where the Input and Output Sequences Are Stored = 210
      • 6.1.2 How Comparisons Are Performed = 210
      • 6.2 Sorting Networks = 212
      • 6.2.1 Bitonic Sort = 214
      • 6.2.2 Mapping bitonic Sort onto a Hypercube and a Mesh = 216
      • 6.3 Bubble Sort and Its Variants = 224
      • 6.3.1 Odd-Even Transposition = 225
      • 6.3.2 Shellsort = 227
      • 6.4 Quicksort = 229
      • 6.4.1 Parallelizing Quicksort = 231
      • 6.4.2 A Comparison of Quicksort Formulations = 243
      • 6.5 Other Sorting Algorithms = 243
      • 6.5.1 Enumeration Sort = 243
      • 6.5.2 Bucket Sort = 244
      • 6.5.3 Sample Sort = 245
      • 6.5.4 Radix Sort = 247
      • 6.6 Bibliographic Remarks = 247
      • Problems = 250
      • References = 254
      • CHAPTER 7 Graph Algorithms = 257
      • 7.1 Definitions and Representation = 257
      • 7.2 Minimum Spanning tree :Prim's Algorithm = 260
      • 7.3 Single-Source Shortest Paths :Dijkstra's Algorithm = 265
      • 7.4 All-Pairs Shortest Paths = 266
      • 7.4.1 Matrix-Multiplication Based Algorithm = 266
      • 7.4.2 Dijkstra's Algorithm = 268
      • 7.4.3 floyd's algorithm = 271
      • 7.4.4 Performance Comparisons = 276
      • 7.5 Transitive Closure = 276
      • 7.6 Connected Components = 278
      • 7.6.1 A Depth-First Search Based Algorithm = 278
      • 7.7 Algorithms for Sparse Graphs = 281
      • 7.7.1 Single-Source Shortest Paths = 284
      • 7.8 Bibliographic Remarks = 290
      • Problems = 293
      • References = 295
      • CHAPTER 8 Search Algorithms for Discrete Optimization Problems = 299
      • 8.1 Definitions and Examples = 299
      • 8.2 Sequential Search Algorithms = 304
      • 8.2.1 Depth-First Search Algorithms = 304
      • 8.2.2 Best-First Search Algorithms = 308
      • 8.3 Search Overhead Factor = 308
      • 8.4 Parallel Depth-First Search = 310
      • 8.4.1 Important Parameters of Parallel DFS = 313
      • 8.4.2 A General Framework for Analysis of Parallel DFS = 315
      • 8.4.3 Analysis of Load-Balancing Schemes for Hypercubes = 318
      • 8.4.4 Analysis of Load-Balancing Schemes for a Network of Workstations = 320
      • 8.4.5 Termination Detection = 321
      • 8.4.6 Experimental Results = 325
      • 8.4.7 Parallel Formulations of Depth-First Branch-and-Bound Search = 328
      • 8.4.8 Parallel Formulations of IDA* = 328
      • 8.4.9 Parallel DFS on SIMD Computers = 329
      • 8.5 Parallel Best-First Search = 332
      • 8.6 Speedup Anomalies in Parallel Search Algorithms = 336
      • 8.6.1 Analysis of Average Speedup in Parallel DFS = 338
      • 8.7 Bibliographic Remarks = 340
      • Problems = 343
      • References = 348
      • CHAPTER 9 Dynamic Programming = 355
      • 9.1 Serial Monadic DP Formulations = 357
      • 9.1.1 The Shortest-Path Problem = 358
      • 9.1.2 The 0/1 Knapsack Problem = 360
      • 9.2 Nonserial Monadic DP Formulations = 362
      • 9.2.1 The Longest-Common-Subsequence Problem = 363
      • 9.3 Serial Polyadic DP Formulations = 365
      • 9.3.1 Floyd's All-Pairs Shortest-Paths Algorithm = 365
      • 9.4 Nonserial Polyadic DP Formulations = 366
      • 9.4.1 The Optimal Matrix-Parenthesization Problem = 366
      • 9.5 Summary and Discussion = 369
      • 9.6 Bibliographic Remarks = 370
      • Problems = 371
      • References = 375
      • CHAPTER 10 Fast Fourier Transform = 377
      • 10.1 The Serial Algorithm = 377
      • 10.2 The Binary-Exchange Algorithm = 382
      • 10.2.1 Hypercube = 383
      • 10.2.2 Mesh = 388
      • 10.2.3 Extra Computations in Parallel FFT = 390
      • 10.3 The Transpose algorithm = 393
      • 10.3.1 Two-Dimensional Transpose Algorithm = 393
      • 10.3.2 The Generalized Transpose Algorithm = 396
      • 10.4 Cost-Effectiveness of Meshes and Hypercubes for FFT = 400
      • 10.5 Bibliographic Remarks = 403
      • Problems = 404
      • References = 405
      • CHAPTER 11 Solving Sparse Systems of Linear Equations = 407
      • 11.1 Basic Operations = 409
      • 11.1.1 Storage Schemes for Sparse Matrices = 409
      • 11.1.2 Vector Inner Product = 412
      • 11.1.3 Sparse Matrix-Vector Multiplication = 413
      • 11.2 Iterative Methods for Sparse Linear Systems = 426
      • 11.2.1 Jacobi Iterative Method = 427
      • 11.2.2 Gauss-Seidel and SOR Methods = 429
      • 11.2.3 The Conjugate Gradient Method = 433
      • 11.3 Finite Element Method = 446
      • 11.4 Direct Methods for Sparse Linear systems = 454
      • 11.4.1 Ordering = 455
      • 11.4.2 Symbolic Factorization = 458
      • 11.4.3 Numerical Factorization = 458
      • 11.4.4 Solving a Triangular system = 468
      • 11.5 Multigrid Methods = 468
      • 11.6 Bibliographic Remarks = 474
      • Problems = 477
      • References = 482
      • CHAPTER 12 Systolic Algorithms and their Mapping onto Parallel Computers = 491
      • 12.1 Examples of Systolic Systems = 493
      • 12.1.1 Convolution = 493
      • 12.1.2 Banded Matrix-Vector Multiplication = 496
      • 12.1.3 Matrix Multiplication = 499
      • 12.1.4 Optimal Matrix Parenthesization = 501
      • 12.2 General Issues in Mapping Systolic Systems onto Parallel Computers = 505
      • 12.2.1 Architectural Differences between Systolic Arrays and Parallel Computers = 505
      • 12.2.2 Absolute Efficiency of Systolic Systems = 506
      • 12.3 Mapping One-Dimensional Systolic Arrays = 507
      • 12.3.1 Virtual Processors = 508
      • 12.3.2 Block-Striped Mapping = 508
      • 12.3.3 Other One-Dimensional Mappings = 511
      • 12.4 Mapping Two-Dimensional Systolic Arrays = 513
      • 12.4.1 Block-Checkerboard Mapping = 514
      • 12.4.2 Cyclic-Checkerboard Mapping = 516
      • 12.4.3 Summary of Two-Dimensional Mappings = 518
      • 12.5 Bibliographic Remarks = 518
      • Problems = 520
      • References = 521
      • CHAPTER13 Parallel Programming = 525
      • 13.1 Parallel Programming Paradigms = 525
      • 13.1.1 Explicit versus Implicit Parallel Programming = 525
      • 13.1.2 Shared-Address-Space versus Message-Passing = 526
      • 13.1.3 Data Parallelism versus Control Parallelism = 527
      • 13.2 Primitives for the Message-Passing Programming Paradigm = 529
      • 13.2.1 Basic Extensions = 529
      • 13.2.2 nCUBE2 = 531
      • 13.2.3 iPSC860 = 532
      • 13.2.4 CM-5 = 533
      • 13.2.5 Example Program = 535
      • 13.3 Data-Parallel Languages = 538
      • 13.3.1 Data Partitioning and Virtual Processors = 539
      • 13.3.2 C* = 540
      • 13.3.3 CM Fortran = 549
      • 13.4 Primitives for the Shared-Address-Space Programming Paradigm = 551
      • 13.4.1 Primitives to Allocate Shared Variables = 552
      • 13.4.2 Primitives for Mutual Exclusion and Synchronization = 552
      • 13.4.3 Primitives for Creating Processes = 553
      • 13.4.4 Sequent Symmetry = 553
      • 13.4.5 Example Programs = 554
      • 13.5 Fortran D = 558
      • 13.5.1 Problem Mapping = 559
      • 13.5.2 Machine Mapping = 561
      • 13.5.3 Example Program = 562
      • 13.6 Bibliographic Remarks = 564
      • References = 566
      • APPENDIX A Complexity of Functions and Order Analysis = 571
      • Author Index = 575
      • Subject Index = 583
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