The authors analyze how the structure of a package determines its developmental complexity according to such measures as bug search times and documentation information content. The work presents arguments for why these issues impact solution cost and time more than does scalable performance. The final chapter explores the question of scalable execution and shows how scalable design relates to scalable execution. The book's focus is on program organization, which has received considerable attention in the broader software engineering community, where graphical description standards for modeling software structure and behavior have been developed by computer scientists. These discussions might be enriched by engineers who write scientific codes. This book aims to bring such scientific programmers into discussion with computer scientists. The authors do so by introducing object-oriented software design patterns in the context of scientific simulation.
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'… it's one of those books that I wish I'd read earlier in my programming career. I found many design patterns familiar simply because I'd seen them before in my own code. I'll likely turn to this book in the future whenever I suspect a program design problem might be solved already.' Computing in Science and Engineering
Book Description
This book concerns software design, focusing on scalable design. The authors analyze how the structure of a package determines its developmental complexity according to such measures as bug search times and documentation information content. The final chapter explores the question of scalable execution.
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4.0 out of 5 stars
2 reviews
1 of 1 people found the following review helpful
3.0 out of 5 stars
Good idea, poor execution
8 July 2012
By Physics Professor
- Published on Amazon.com
Format:Hardcover
I like the basic idea behind this book: the use of the methods of modern software engineering to speed up and improve the software development and V&V processes, which for scientific computing are frequently much more expensive than the cost of running the program. However, the book has a number of serious flaws.
1. The text is not very clearly written.
2. The book claims to be accessible to people from a C++ background, but it really is not. At a minimum, the book should have included an appendix on the syntax of modern Fortran (probably at the level of Fortran 95).
3. The book acknowledges that the widely used Fortran compilers are not consistent with the 2008 standard, but the authors said that they "hoped" this would be fixed by the publication date. The compiler we have, gfortran, still is not 100% compliant with Fortran 2003.
4. The programming style is abominable. For example, the authors use the class element T%t, which is meant to be the time (little t) derivative of the temperature (big T). Never mind the fact that Fortran is case insensitive! One of the big things I tell students is to use descriptive variable names, even if they are long. (Another example is "fin", which I think is either an input function, maybe an input file, or perhaps finalizes something. I always have search for the definition on some previous page when I come across "fin" in their code.)
All these faults are apparent in the first few chapters, which is as far as I got before realizing that correcting them would make the book too time- and labor-intensive to be useful as a textbook for a class I am planning to teach.
1. The text is not very clearly written.
2. The book claims to be accessible to people from a C++ background, but it really is not. At a minimum, the book should have included an appendix on the syntax of modern Fortran (probably at the level of Fortran 95).
3. The book acknowledges that the widely used Fortran compilers are not consistent with the 2008 standard, but the authors said that they "hoped" this would be fixed by the publication date. The compiler we have, gfortran, still is not 100% compliant with Fortran 2003.
4. The programming style is abominable. For example, the authors use the class element T%t, which is meant to be the time (little t) derivative of the temperature (big T). Never mind the fact that Fortran is case insensitive! One of the big things I tell students is to use descriptive variable names, even if they are long. (Another example is "fin", which I think is either an input function, maybe an input file, or perhaps finalizes something. I always have search for the definition on some previous page when I come across "fin" in their code.)
All these faults are apparent in the first few chapters, which is as far as I got before realizing that correcting them would make the book too time- and labor-intensive to be useful as a textbook for a class I am planning to teach.
5.0 out of 5 stars
Excellent guidance for moving forward
14 Oct 2012
By Dan Nagle
- Published on Amazon.com
Format:Hardcover
I published a review of this book in Scientific Computing. I
read the entire text to do so.
_Scientific Software Design_ is an excellent text aimed towards
scientific programmers. The thrust of the text is how to manage
scientific software in an environment where the code is growing
in complexity, and the team developing it has diverging areas
of expertise. Object-oriented techniques are motivated as the means
to do so.
Terminology varies, and the text compares and contrasts terms
from C++, Fortran and UML. Getting the concepts right
is the important part, which the text does. The examples
show design patterns used to solve differential equations numerically
in a variety of circumstances, including parallel processing.
This book is valuable to anyone who has a large scientific code
developed over time and who is seeking a scheme for moving
the code into the future. It's also useful for someone who
wants to see complete examples in C++ and Fortran side-by-side.
I would quibble with the coding style (but I usually do).
read the entire text to do so.
_Scientific Software Design_ is an excellent text aimed towards
scientific programmers. The thrust of the text is how to manage
scientific software in an environment where the code is growing
in complexity, and the team developing it has diverging areas
of expertise. Object-oriented techniques are motivated as the means
to do so.
Terminology varies, and the text compares and contrasts terms
from C++, Fortran and UML. Getting the concepts right
is the important part, which the text does. The examples
show design patterns used to solve differential equations numerically
in a variety of circumstances, including parallel processing.
This book is valuable to anyone who has a large scientific code
developed over time and who is seeking a scheme for moving
the code into the future. It's also useful for someone who
wants to see complete examples in C++ and Fortran side-by-side.
I would quibble with the coding style (but I usually do).
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