Buch, Englisch, 416 Seiten, Format (B × H): 188 mm x 263 mm, Gewicht: 839 g
ISBN: 978-0-691-11935-9
Verlag: Princeton University Press
What does Google's management of billions of Web pages have in common with analysis of a genome with billions of nucleotides? Both apply methods that coordinate many processors to accomplish a single task. From mining genomes to the World Wide Web, from modeling financial markets to global weather patterns, parallel computing enables computations that would otherwise be impractical if not impossible with sequential approaches alone. Its fundamental role as an enabler of simulations and data analysis continues an advance in a wide range of application areas. Scientific Parallel Computing is the first textbook to integrate all the fundamentals of parallel computing in a single volume while also providing a basis for a deeper understanding of the subject. Designed for graduate and advanced undergraduate courses in the sciences and in engineering, computer science, and mathematics, it focuses on the three key areas of algorithms, architecture, languages, and their crucial synthesis in performance. The book's computational examples, whose math prerequisites are not beyond the level of advanced calculus, derive from a breadth of topics in scientific and engineering simulation and data analysis. The programming exercises presented early in the book are designed to bring students up to speed quickly, while the book later develops projects challenging enough to guide students toward research questions in the field. The new paradigm of cluster computing is fully addressed. A supporting web site provides access to all the codes and software mentioned in the book, and offers topical information on popular parallel computing systems. Integrates all the fundamentals of parallel computing essential for today's high-performance requirements Ideal for graduate and advanced undergraduate students in the sciences and in engineering, computer science, and mathematics Extensive programming and theoretical exercises enable students to write parallel codes quickly More challenging projects later in the book introduce research questions New paradigm of cluster computing fully addressed Supporting web site provides access to all the codes and software mentioned in the book
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Logik, formale Sprachen, Automaten
- Interdisziplinäres Wissenschaften Wissenschaften Interdisziplinär Naturwissenschaften, Technik, Medizin
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Funktionale, Logische, Parallele und Visuelle Programmierung
- Mathematik | Informatik EDV | Informatik Technische Informatik Grid-Computing & Paralleles Rechnen
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik
Weitere Infos & Material
Preface ix
Notation xiii
Chapter 1. Introduction 1
1.1 Overview 1
1.2 What is parallel computing? 3
1.3 Performance 4
1.4 Why parallel? 11
1.5 Two simple examples 15
1.6 Mesh-based applications 24
1.7 Parallel perspectives 30
1.8 Exercises 33
Chapter 2. Parallel Performance 37
2.1 Summation example 37
2.2 Performance measures 38
2.3 Limits to performance 44
2.4 Scalability 48
2.5 Parallel performance analysis 56
2.6 Parallel payoff 59
2.7 Real world parallelism 64
2.8 Starting SPMD programming 66
2.9 Exercises 66
Chapter 3. Computer Architecture 71
3.1 PMS notation 71
3.2 Shared memory multiprocessor 75
3.3 Distributed memory multicomputer 79
3.4 Pipeline and vector processors 87
3.5 Comparison of parallel architectures 89
3.6 Taxonomies 92
3.7 Current trends 94
3.8 Exercises 95
Chapter 4. Dependences 99
4.1 Data dependences 100
4.2 Loop-carried data dependences 103
4.3 Dependence examples 110
4.4 Testing for loop-carried dependences 112
4.5 Loop transformations 114
4.6 Dependence examples continued 120
4.7 Exercises 123
Chapter 5. Parallel Languages 127
5.1 Critical factors 129
5.2 Command and control 134
5.3 Memory models 136
5.4 Shared memory programming 139
5.5 Message passing 143
5.6 Examples and comments 148
5.7 Parallel language developments 153
5.8 Exercises 154
Chapter 6. Collective Operations 157
6.1 The @notation 157
6.2 Tree/ring algorithms 158
6.3 Reduction operations 162
6.4 Reduction operation applications 164
6.5 Parallel prefix algorithms 168
6.6 Performance of reduction operations 169
6.7 Data movement operations 173
6.8 Exercises 174
Chapter 7. Current Programming Standards 177
7.1 Introduction to MPI 177
7.2 Collective operations in MPI 181
7.3 Introduction to POSIX threads 184
7.4 Exercises 187
Chapter 8. The Planguage Model 191
8.1 I P language details 192
8.2 Ranges and arrays 198
8.3 Reduction operations in Pfortran 200
8.4 Introduction to PC 204
8.5 Reduction operations in PC 206
8.6 Planguages versus message passing 207
8.7 Exercises 208
Chapter 9. High Performance Fortran 213
9.1 HPF data distribution directives 214
9.2 Other mechanisms for expressing concurrency 219
9.3 Compiling HPF 220
9.4 HPF comparisons and review 221
9.5 Exercises 222
Chapter 10. Loop Tiling 227
10.1 Loop tiling 227
10.2 Work vs.data decomposition 228
10.3 Tiling in OpenMP 228
10.4 Teams 232
10.5 Parallel regions 233
10.6 Exercises 234
Chapter 11. Matrix Eigen Analysis 237
11.1 The Leslie matrix model 237
11.2 The power method 242
11.3 A parallel Leslie matrix program 244
11.4 Matrix-vector product 249
11.5 Power method applications 251
11.6 Exercises 253
Chapter 12. Linea Systems 257
12.1 Gaussian elimination 257
12.2 Solving triangular systems in parallel 262
12.3 Divide-and-conquer algorithms 271
12.4 Exercises 277
12.5 Projects 281
Chapter 13. Particle Dynamics 283
13.1 Model assumptions 284
13.2 Using Newton's third law 285
13.3 Further code complications 288
13.4 Pair list generation 290
13.5 Force calculation with a pair list 296
13.6 Performance of replication algorithm 299
13.7 Case study:particle dynamics in HPF 302
13.8 Exercises 307
13.9 Projects 310
Chapter 14. Mesh Methods 315
14.1 Boundary value problems 315
14.2 Iterative methods 319
14.3 Multigrid methods 322
14.4 Multidimensional problems 327
14.5 Initial value problems 328
14.6 Exercises 333
14.7 Projects 334
Chapter 15. Sorting 335
15.1 Introduction 335
15.2 Parallel sorting 337
15.3 Spatial sorting 342
15.4 Exercises 353
15.5 Projects 355
Bibliography 357
Index 369
