Models for Parallel and Distributed Computation

Theory, Algorithmic Techniques and Applications

By: R. Correa (Editor), Ines de Castro Dutra (Editor), Mario Fiallos (Editor)

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Hardcover | 30 June 2002

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348 Pages

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24.13 x 16.51 x 2.54

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Parallel and distributed computation has been gaining a great lot of attention in the last decades. During this period, the advances attained in computing and communication technologies, and the reduction in the costs of those technolo gies, played a central role in the rapid growth of the interest in the use of parallel and distributed computation in a number of areas of engineering and sciences. Many actual applications have been successfully implemented in various plat forms varying from pure shared-memory to totally distributed models, passing through hybrid approaches such as distributed-shared memory architectures. Parallel and distributed computation differs from dassical sequential compu tation in some of the following major aspects: the number of processing units, independent local dock for each unit, the number of memory units, and the programming model. For representing this diversity, and depending on what level we are looking at the problem, researchers have proposed some models to abstract the main characteristics or parameters (physical components or logical mechanisms) of parallel computers. The problem of establishing a suitable model is to find a reasonable trade-off among simplicity, power of expression and universality. Then, be able to study and analyze more precisely the behavior of parallel applications.

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Non-Fiction Computing & I.T.Computer Science Computer Architecture & Logic Design Parallel Processing Medicine Medicine in General Computer Programming & Software Development Systems Analysis & Design

Contributing Authors	p. xi
Models of Parallel Computation
Introduction to the Complexity of Parallel Algorithms	p. 3
Introduction	p. 3
Sequential complexity	p. 4
Turing machines	p. 4
Random access machines	p. 6
Relations between TM and RAM	p. 6
Thesis	p. 6
Relations between DSPACE and NSPACE	p. 7
Central problem	p. 8
PRAM model	p. 8
Concurrent or exclusive accesses	p. 9
Computing with PRAM	p. 10
PRAM separation	p. 11
PRAM simulation	p. 12
Boolean and arithmetic circuits	p. 12
Parallel computation thesis	p. 15
NC class	p. 15
Central problem of parallel complexity	p. 16
Distributed memory PRAM	p. 17
Performances	p. 19
Evaluation of arithmetic expressions	p. 22
Solving linear systems	p. 23
Perspectives and conclusion	p. 24
The Combinatorics of Resource Sharing	p. 27
Introduction	p. 27
Resource-sharing computations	p. 29
Deadlock models	p. 31
Graph structures for deadlock detection	p. 35
Partially ordered sets and deadlock prevention	p. 38
Ordering the processes	p. 39
Ordering the resources	p. 41
The graph abacus	p. 43
Graph coloring and concurrency	p. 46
Concluding remarks	p. 50
On Solving the Static Task Scheduling Problem for Real Machines	p. 53
Introduction	p. 53
Designing parallel algorithms	p. 54
Scheduling tasks	p. 55
Models of communication	p. 57
Task scheduling under the LogP model	p. 59
Scheduling strategies	p. 61
List scheduling	p. 61
Clustering algorithms	p. 62
Task recomputation	p. 63
Message bundling	p. 64
Terminology	p. 64
Design issues for clustering algorithms	p. 65
A new scheduling algorithm	p. 69
Simplifying LogP scheduling	p. 70
The first stage	p. 71
The second stage	p. 74
Algorithm performance	p. 74
Conclusions	p. 77
Summary	p. 78
Predictable Parallel Performance: The BSP Model	p. 85
Applying parallel computing	p. 85
Introduction to BSP	p. 87
Implementation of BSP	p. 89
BSP algorithm design	p. 91
A BSP design strategy	p. 93
BSP optimisation	p. 97
Message packing	p. 97
Destination scheduling	p. 99
Pacing	p. 99
Barrier implementation	p. 100
Optimisation in clusters	p. 101
Hole filling error recovery	p. 102
Reducing acknowledgement traffic	p. 102
Just in time acknowledgements	p. 103
Managing buffers more effectively	p. 103
Software development	p. 103
Applications of BSP	p. 106
Parallel data mining	p. 106
Bagging	p. 107
Exchanging information earlier	p. 108
Summary	p. 109
Discrete computing with CGM	p. 117
Introduction	p. 117
Parallel models	p. 119
Basic algorithms in CGM	p. 120
Prefix sums	p. 120
CGM sorting	p. 123
Deterministically selecting the splitters	p. 124
CGM randomized sorting	p. 125
Connected components	p. 127
Connected components for graphs such that n [less than or equal] m/p	p. 128
Interval graphs	p. 131
Convex hull computation	p. 133
Outline of the algorithm	p. 133
Merging convex hulls in parallel	p. 134
Experimental results	p. 138
Conclusion	p. 139
Parallel Applications
Parallel Graph Algorithms for Coarse-Grained Multicomputers	p. 147
Introduction	p. 147
The coarse grained multicomputer (CGM)	p. 148
List ranking	p. 149
A randomized parallel algorithm for list ranking	p. 150
A simple algorithm using a single random sample	p. 151
Improving the maximum sublist size	p. 152
Simulation and experimental results	p. 155
Applications	p. 157
Conclusion	p. 157
A deterministic algorithm for list ranking	p. 157
The basic idea	p. 157
Connected components and spanning forest	p. 160
Parallel range minima	p. 163
Sequential algorithms	p. 164
Algorithm of Gabow et al.	p. 164
Sequential algorithm of Alon and Schieber	p. 165
The CGM algorithm for the range minima problem	p. 166
Query processing	p. 169
Experimental results	p. 170
Conclusion	p. 172
Lowest common ancestor	p. 173
Concluding remarks	p. 175
Parallel metaheuristics for combinatorial optimization	p. 179
Introduction	p. 180
Parallel variable neighborhood descent	p. 182
Parallel genetic algorithms	p. 183
Parallel simulated annealing	p. 187
Parallel tabu search	p. 189
Parallel GRASP	p. 192
Parallel computing environments	p. 194
Concluding remarks	p. 197
Parallelism in Logic Programming and Scheduling Issues	p. 207
Introduction	p. 207
Logic programming	p. 208
Parallelism in logic programming	p. 212
OR-Parallelism	p. 214
Independent AND-parallelism (IAP)	p. 219
Dependent AND-parallelism	p. 221
AND-OR Parallelism	p. 222
Tools for performance evaluation	p. 224
Scheduling	p. 225
Scheduling OR-parallel work	p. 226
Scheduling AND-parallel work	p. 227
Scheduling AND/OR-parallel work	p. 228
Compile-time analysis	p. 229
Discussion	p. 230
Conclusion	p. 231
Parallel Asynchronous Team Algorithms	p. 243
Introduction	p. 243
Framework for convergence analysis of Team Algorithms	p. 244
Block Team Algorithm	p. 246
Fully Overlapped Team Algorithm	p. 249
Generalized Team Algorithm	p. 252
The load flow problem	p. 259
Computational results	p. 263
Numerical results using a Block TA	p. 265
Numerical results using a TA with Partial Overlapping	p. 266
A-Teams with Genetic Algorithms	p. 267
Hydroelectric generation optimization	p. 268
Topological optimization of reliable networks	p. 271
Conclusions	p. 273
Parallel Numerical Methods for Differential Equations	p. 279
Introduction	p. 279
Initial remarks	p. 279
Final remarks	p. 280
Domain decomposition--DD	p. 281
General facts	p. 282
Parallelization	p. 285
Additive x multiplicative Schwarz methods	p. 287
A question by Chan; words from Keyes; final remarks	p. 288
The multigrid method	p. 288
Introductory facts	p. 289
A simple example--the main idea	p. 290
Parallel environment--a super convergent algorithm	p. 292
Scattered remarks	p. 294
Comparisons	p. 295
The finite element method	p. 295
Element-by-element method	p. 295
Applications	p. 298
Tear-and-interconnect method	p. 298
Mesh decomposition	p. 298
Evolutionary equations	p. 299
Parallelism in time scale?	p. 299
Frequency domain--spectral method	p. 300
Binary bisection method	p. 300
The independent time-step method	p. 302
Windowed iterative methods	p. 303
The parallel time-stepping method	p. 304
BBN butterfly implementation: time-step overlap	p. 305
Explicit schemes and high accuracy	p. 305
Piecewise parabolic method	p. 307
Index	p. 315
Table of Contents provided by Syndetics. All Rights Reserved.

Models for Parallel and Distributed Computation

Theory, Algorithmic Techniques and Applications

At a Glance

Hardcover

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