Parallel Computing (eBook)

Preface 6
List of Contributors 8
Contents 11
About the Editors 20
Chapter 1 Overview – Parallel Computing: Numerics, Applications, and Trends 22
1.1 Introduction 22
1.2 Book Chapters 34
1.3 Conclusions 53
References 57
Chapter 2 Introduction to Parallel Computation 64
2.1 Introduction 65
2.2 Parallel Versus Sequential Computation 66
2.3 Parallel Computational Models 67
2.4 Parallel Algorithm Design Methods 73
2.5 Theoretical Underpinnings 76
2.6 Parallel Algorithms for Conventional Computations 81
2.7 Parallel Algorithms for Unconventional Computations 87
2.8 Non-Universality in Computation 98
2.9 Conclusion 100
References 101
Chapter 3 Tools for Parallel and Distributed Computing 102
3.1 Introduction 103
3.2 Related Work 104
3.4 SCALEA 109
3.5 ZENTURIO 112
3.6 AKSUM 114
3.7 Grid-Prophet 119
3.8 Experiments 124
3.9 Conclusions 132
References 132
Chapter 4 Grid Computing 137
4.1 Introduction 137
4.2 Challenges in Grid Computing 143
4.3 Tools and Applications 149
4.4 Conclusions and Future Trends 159
References 160
Chapter 5 Parallel Structured Adaptive Mesh Refinement 166
5.1 Introduction 167
5.2 An Introduction to SAMR 169
5.3 Details of SAMR 174
5.4 Computer Science Aspects of SAMR 177
5.5 Some Results 183
5.6 Conclusions and FutureWork 188
References 189
Chapter 6 Applications and Parallel Implementation of QMC Integration 193
6.1 Introduction 194
6.2 Monte Carlo and Quasi Monte Carlo Methods in Numerical Integration Over [0,1)s 195
6.3 QMC Methods for Integrals over Rs with aWeight Function 200
6.4 QMC Integration on Parallel Systems 206
6.5 Numerical Experiments 209
6.6 Application of the Diaphony in Parallel Computation 226
6.7 Conclusion 229
References 231
Chapter 7 Parallel Evolutionary Computation Framework for Single- and Multiobjective Optimization 234
7.1 Introduction 235
7.2 Optimization Problems 236
7.3 Evolutionary Algorithms 239
7.4 Parallel Single- and Multiobjective Evolutionary Algorithms 241
7.5 Casting Process Optimization Task 246
7.6 Parallel Evolutionary Computation Framework 247
7.7 Empirical Evaluation 250
7.8 Conclusion 256
References 257
Chapter 8 WaLBerla: Exploiting Massively Parallel Systems for Lattice Boltzmann Simulations 258
8.1 Motivation 259
8.2 Introduction to the Lattice Boltzmann Method 260
8.3 Domain Partitioning Using Patches 262
8.4 Communication Concept 264
8.5 Performance Studies 266
8.6 Conclusion 275
References 276
Chapter 9 Parallel Pseudo-Spectral Methods for the Time-Dependent Schrödinger Equation 278
9.1 Introduction 278
9.2 Time Stepping and Split Operator Technique 281
9.3 Variable Transformations and Spectral Bases 282
9.4 Parallelizing Many Dimensional FFTs 284
9.5 Creating a Framework for Combining Discretization Methods 288
9.6 A Numerical Example 292
9.7 Conclusion 295
References 295
Chapter 10 Parallel Approaches in Molecular Dynamics Simulations 297
10.1 Split Integration Symplectic Method 298
10.2 Parallel Computers 306
10.3 Parallel Molecular Dynamics Computer Simulations 310
10.4 Parallelization of SISM 315
10.5 Conclusions 317
References 318
Chapter 11 Parallel Computer Simulations of Heat Transfer in Biological Tissues 322
11.1 Introduction 323
11.2 Principal Steps in Computer Simulation 326
11.3 Numerical Solution of Partial Differential Equations 329
11.4 Diffusion Equation 336
11.5 Bio-Heat Equation 345
11.6 Geometric Modeling of a Knee 348
11.7 Simulation Methods and Parameters 351
11.8 Variation of Simulation Parameters 353
11.9 Simulation Results 355
11.10 Validation of Results 360
11.11 Parallel Implementation 362
11.12 Conclusions 369
References 371
Chapter 12 Parallel SVD Computing in the Latent Semantic Indexing Applications for Data Retrieval 374
12.1 Introduction 374
12.2 Two Updating Problems in LSI 376
12.3 Two Downdating Problems in LSI 378
12.4 Kogbetliantz Method for Triangular Matrices 381
12.5 Parallel Two-sided Block-Jacobi SVD Algorithm with Dynamic Ordering 392
12.6 LSI Implemented on a Grid 398
12.7 LSI Implemented on a Distributed System 403
12.8 Conclusions 409
References 410
Chapter 13 Short-Vector SIMD Parallelization in Signal Processing 411
13.1 Introduction 411
13.2 General Vectorization Approaches 415
13.3 Convolution Type Algorithms 418
13.4 Recursive Algorithms 433
13.5 Block Algorithms 437
13.6 Mixed Algorithms 442
13.7 Conclusion 445
References 446
Chapter 14 Financial Applications: Parallel Portfolio Optimization 448
14.1 Introduction 449
14.2 Asset and Liability Management by Stochastic Programming 450
14.3 Parallel Solution Approaches: Decomposition 459
14.4 Parallel Solution Approaches: Interior Point Based Algorithms 461
14.5 Parallel Solution Approaches: Heuristics and Evolutionary Algorithms 471
14.6 Other Approaches 471
14.7 Detailed Comparison of Parallel Interior Point Approaches 472
14.8 Conclusions 478
References 479
Chapter 15 The Future of Parallel Computation 483
15.1 Introduction 484
15.2 Quantum Computing 487
15.3 Parallelism in Quantum Computing 497
15.4 Examples 500
15.5 Looking Ahead 519
References 520
Index 523