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E-raamat: High Performance Computing and the Art of Parallel Programming: An Introduction for Geographers, Social Scientists and Engineers [Taylor & Francis e-raamat]

(University of Leeds, England, UK),
  • Formaat: 304 pages, 29 Tables, black and white
  • Ilmumisaeg: 25-Nov-1999
  • Kirjastus: Routledge
  • ISBN-13: 9780203981436
  • Taylor & Francis e-raamat
  • Hind: 212,34 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 303,35 €
  • Säästad 30%
  • Formaat: 304 pages, 29 Tables, black and white
  • Ilmumisaeg: 25-Nov-1999
  • Kirjastus: Routledge
  • ISBN-13: 9780203981436
To help social scientists actually make use of the glut of data now flooding developed countries, geographers (U. of Leeds) Openshaw and Turton offer a non-technical introduction to high-performance computing applications and advice on how beginners can start to write parallel programs, especially those to be used in geographical information systems. They present case studies from geography to demonstrate the key principles and clarify the logic and thought processes that lie behind parallel programming. Their concern is with the art rather than the science of parallel programming, which is usually embedded in computer jargon. Annotation c. Book News, Inc., Portland, OR (booknews.com)

This book provides a non-technical introduction to High Performance Computing applications together with advice about how beginners can start to write parallel programs. The authors show what HPC can offer geographers and social scientists and how it can be used in GIS. They provide examples of where it has already been used and suggestions for other areas of application in geography and the social sciences. Case studies drawn from geography explain the key principles and help to understand the logic and thought processes that lie behind the parallel programming.
List of figures
ix
List of tables
x
List of appendices
xii
Dedication and acknowledgements xiii
High-performance computing: why bother with it?
1(11)
An HPC point of view
1(1)
HPC stimulates new research and creates new research opportunities
2(1)
Why parallel processing is important
3(2)
Parallel computing is the future of HPC
5(2)
Aims and objectives
7(1)
Fostering a computational culture
8(2)
Plan of the book
10(2)
High-performance computing applications in geography and GIS
12(35)
Introduction
12(3)
Parallel programming
15(2)
Geocomputation
17(2)
Raising HPC awareness
19(7)
HPC applications in geography and GIS
26(3)
Some examples of HPC applications in geography
29(13)
Parallel GIS applications
42(3)
Overcoming access barriers
45(2)
Parallel and high-performance computing: concepts, principles and theory
47(27)
What is parallel computing?
47(5)
Why parallel processing is important
52(5)
Highly and massively parallel processing
57(5)
Examples of thinking in parallel
62(2)
Can paralle machines ever be used efficiently?
64(5)
Building a wall in a parallel way
69(2)
A brief history of parallel computing
71(2)
Conclusions
73(1)
Types of parallel-processing hardware and programming paradigms
74(20)
Automatic parallelisation software
74(2)
Computer architectures
76(6)
The three principal types of HPC hardware
82(6)
Levels of parallelism and identifying them
88(2)
Programming models
90(1)
Examples of each type of parallelism
91(1)
Conclusions
92(2)
Programming vector supercomputers
94(45)
Introduction
94(1)
The secrets of vector processing
95(4)
Vectorising your code
99(1)
Optimisation of performance
100(4)
A case study in vector processing using the Mark 1 geographical analysis machine as an example
104(15)
Case study 2: orgin-constrained spatial interaction model
119(3)
Conclusions
122(17)
Shared-loop and data parallel programming
139(39)
Introduction
139(1)
Multi-tasking on shared-memory MIMD machines
140(7)
Parallelisation strategies
147(4)
Data parallel programming
151(4)
Conclusions
155(23)
Parallel programming using simple message passing
178(33)
Introduction
178(2)
Message passing?
180(2)
Message-passing software
182(2)
How to use MPI for SPMD
184(2)
Example 1: probably the world's simplest MPI program that could be useful
186(2)
Example 2: sum M numbers
188(8)
Example 3: a data parallel spatial interaction in MPI
196(7)
Conclusions
203(8)
Parallelising the geographical analysis machine using MPI
211(24)
A data parallel GAM using MPI
211(2)
Where is the parallelism in the GAM?
213(2)
Loop 4 message passing
215(2)
Some alternative message-passing schemes
217(2)
Doing even better by task farming
219(2)
A task-farming GAM
221(1)
Improvements?
222(2)
Loop 0 complications
224(1)
Multiple task farms
225(1)
More advanced MPI routines
225(1)
Conclusions
225(10)
Optimising performance and debugging hints
235(16)
Introduction
235(1)
First optimise your algorithm rather than fiddling with code
236(2)
Now start to fiddle
238(1)
Scaleable performance
238(2)
Exploit Amdahl's law
240(1)
Some MPI optimisation secrets
241(2)
Debugging parallel code is harder than serial code
243(1)
Debugging message passing
244(3)
Defensive coding
247(1)
Shared-memory debugging
248(1)
Message-passing debugging
248(2)
Conclusions
250(1)
Putting it all together
251(17)
Background
251(1)
Introduction to benchmarking
251(2)
The spatial interaction model as a benchmark code
253(3)
The high-performance Fortran version
256(2)
The message-passing code using MPI
258(1)
The bulk synchronous parallel model
259(1)
Measuring performance using MPI and serial code
259(2)
A comparison of HPF and MPI codes
261(4)
Conclusions
265(3)
Epilogue for geographers and social scientists
268(9)
The global challenge
268(4)
What has HPC got to do with any of this?
272(1)
Revising the definition of geocomputation
272(2)
A GIS--HPC research agenda
274(2)
HPC futures in geography, etc.
276(1)
References and further reading 277(6)
Index 283
Stan Openshaw, Ian Turton