Writing Scientific Software

A Guide to Good Style

By: Suely Oliveira, David E. Stewart

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Paperback | 30 October 2006

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316 Pages

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The core of scientific computing is designing, writing, testing, debugging and modifying numerical software for application to a vast range of areas: from graphics, meteorology and chemistry to engineering, biology and finance. Scientists, engineers and computer scientists need to write good code, for speed, clarity, flexibility and ease of re-use. Oliveira and Stewart's style guide for numerical software points out good practices to follow, and pitfalls to avoid. By following their advice, readers will learn how to write efficient software, and how to test it for bugs, accuracy and performance. Techniques are explained with a variety of programming languages, and illustrated with two extensive design examples, one in Fortran 90 and one in C++: other examples in C, C++, Fortran 90 and Java are scattered throughout the book. This manual of scientific computing style will be an essential addition to the bookshelf and lab of everyone who writes numerical software.

Industry Reviews

'Writing Scientific Software bridges the chasm that too often separates numerical analysis from real scientific computing. It is full of tips, tricks, and just plain interesting information. The reader will learn how to write code that takes advantage of many hidden features in modern computer architectures, programming languages, and compilers. This is a book that is full of good sense.' William H. Press, author of Numerical Recipes 'The book is a useful tool working in the filed of scientific computations...' Zentralblatt MATH

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You Can Find This Book In

Non-Fiction Language & Linguistics Reference, Dictionaries & Guides Writing & Editing Guides Technical Writing Computing & I.T.Computer Programming & Software Development Software Engineering Computer Science Computer Architecture & Logic Design Compilers & Interpreters Programming & Scripting Languages

Mathematics Calculus & Mathematical Analysis Numerical Analysis

Preface	p. ix
Numerical Software	p. 1
Why numerical software?	p. 3
Efficient kernels	p. 4
Rapid change	p. 5
Large-scale problems	p. 6
Scientific computation and numerical analysis	p. 8
The trouble with real numbers	p. 8
Fixed-point arithmetic	p. 18
Algorithm stability vs. problem stability	p. 19
Numerical accuracy and reliability	p. 23
Priorities	p. 30
Correctness	p. 30
Numerical stability	p. 32
Accurate discretization	p. 32
Flexibility	p. 33
Efficiency: time and memory	p. 35
Famous disasters	p. 36
Patriot missiles	p. 36
Ariane 5	p. 37
Sleipner A oil rig collapse	p. 38
Exercises	p. 39
Developing Software	p. 43
Basics of computer organization	p. 45
Under the hood: what a CPU does	p. 45
Calling routines: stacks and registers	p. 47
Allocating variables	p. 51
Compilers, linkers, and loaders	p. 53
Software design	p. 57
Software engineering	p. 57
Software life-cycle	p. 57
Programming in the large	p. 59
Programming in the small	p. 61
Programming in the middle	p. 67
Interface design	p. 70
Top-down and bottom-up development	p. 75
Don't hard-wire it unnecessarily!	p. 77
Comments	p. 78
Documentation	p. 80
Cross-language development	p. 82
Modularity and all that	p. 87
Data structures	p. 90
Package your data!	p. 90
Avoid global variables!	p. 91
Multidimensional arrays	p. 92
Functional representation vs. data structures	p. 96
Functions and the "environment problem"	p. 97
Some comments on object-oriented scientific software	p. 106
Design for testing and debugging	p. 118
Incremental testing	p. 118
Localizing bugs	p. 120
The mighty "print" statement	p. 120
Get the computer to help	p. 122
Using debuggers	p. 129
Debugging functional representations	p. 130
Error and exception handling	p. 132
Compare and contrast	p. 135
Tracking bugs	p. 136
Stress testing and performance testing	p. 137
Random test data	p. 141
Exercises	p. 143
Efficiency in Time, Efficiency in Memory	p. 147
Be algorithm aware	p. 149
Numerical algorithms	p. 149
Discrete algorithms	p. 151
Numerical algorithm design techniques	p. 153
Computer architecture and efficiency	p. 156
Caches and memory hierarchies	p. 156
A tour of the Pentium 4[superscript T M] architecture	p. 158
Virtual memory and paging	p. 164
Thrashing	p. 164
Designing for memory hierarchies	p. 165
Dynamic data structures and memory hierarchies	p. 168
Pipelining and loop unrolling	p. 168
Basic Linear Algebra Software (BLAS)	p. 170
LAPACK	p. 178
Cache-oblivious algorithms and data structures	p. 184
Indexing vs. pointers for dynamic data structures	p. 185
Global vs. local optimization	p. 187
Picking algorithms vs. keyhole optimization	p. 187
What optimizing compilers do	p. 188
Helping the compiler along	p. 191
Practicalities and asymptotic complexity	p. 192
Grabbing memory when you need it	p. 195
Dynamic memory allocation	p. 195
Giving it back	p. 197
Garbage collection	p. 198
Life with garbage collection	p. 199
Conservative garbage collection	p. 202
Doing it yourself	p. 203
Memory tips	p. 205
Memory bugs and leaks	p. 208
Beware: unallocated memory!	p. 208
Beware: overwriting memory!	p. 208
Beware: dangling pointers!	p. 210
Beware: memory leaks!	p. 214
Debugging tools	p. 215
Tools	p. 217
Sources of scientific software	p. 219
Netlib	p. 220
BLAS	p. 220
LAPACK	p. 221
GAMS	p. 221
Other sources	p. 221
Unix tools	p. 223
Automated builds: make	p. 223
Revision control: RCS, CVS, Subversion and Bitkeeper	p. 226
Profiling: prof and gprof	p. 228
Text manipulation: grep, sed, awk, etc.	p. 230
Other tools	p. 232
What about Microsoft Windows?	p. 233
Design Examples	p. 237
Cubic spline function library	p. 239
Creation and destruction	p. 242
Output	p. 244
Evaluation	p. 244
Spline construction	p. 247
Periodic splines	p. 257
Performance testing	p. 260
Multigrid algorithms	p. 262
Discretizing partial differential equations	p. 262
Outline of multigrid methods	p. 264
Implementation of framework	p. 265
Common choices for the framework	p. 272
A first test	p. 273
The operator interface and its uses	p. 276
Dealing with sparse matrices	p. 279
A second test	p. 282
Review of vectors and matrices	p. 287
Identities and inverses	p. 288
Norms and errors	p. 289
Errors in solving linear systems	p. 291
Trademarks	p. 292
References	p. 293
Index	p. 299
Table of Contents provided by Ingram. All Rights Reserved.

Writing Scientific Software

A Guide to Good Style

At a Glance

Paperback

Industry Reviews

Shipping

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Defective items

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