Performance Optimization of Numerically Intensive Codes (Software, Environments and Tools) [Paperback]

Stefan Goedecker (Author), Adolfy Hoisie (Author)

Book Description

ISBN-10: 0898714842 | ISBN-13: 978-0898714845 | Publication Date: March 19, 2001

Performance Optimization of Numerically Intensive Codes offers a comprehensive, tutorial-style, hands-on, introductory and intermediate-level treatment of all the essential ingredients for achieving high performance in numerical computations on modern computers. The authors explain computer architectures, data traffic and issues related to performance of serial and parallel code optimization exemplified by actual programs written for algorithms of wide interest. The unique hands-on style is achieved by extensive case studies using realistic computational problems. The performance gain obtained by applying the techniques described in this book can be very significant. The book bridges the gap between the literature in system architecture, the one in numerical methods and the occasional descriptions of optimization topics in computer vendors' literature. It also allows readers to better judge the suitability of certain computer architecture to their computational requirements.

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Audience

This book will be useful to scientists, engineers and students interested in computational science and high-performance programming. It can be used as a reference book by the practitioner and as basic material for various undergraduate and graduate classes in computer and computational science. Readers should be familiar with a programming language such as Fortran or C.

About the Author

Stefan Goedecker is a Staff Scientist in the department of basic condensed matter research with the Atomic Energy Commission in Grenoble, France. He is a two-time winner of the Gordon Bell Prize in parallel computing, in 1994 and jointly with Adolfy Hoisie in 1996. He is the author of numerous research articles as well as several review articles and a book in the field of physics and computational sciences.

Adolfy Hoisie is a Staff Scientist and the Leader of the Parallel Architectures and Performance Team in the Computer and Computational Sciences division at the Los Alamos National Laboratory in New Mexico. His areas of research are performance analysis and modeling, distributed computing, and parallel computer architectures, areas in which he has published extensively.

Product Details

• Paperback: 173 pages
• Publisher: Society for Industrial Mathematics (March 19, 2001)
• Language: English
• ISBN-10: 0898714842
• ISBN-13: 978-0898714845
• Product Dimensions: 10.1 x 6.8 x 0.4 inches
• Shipping Weight: 12.8 ounces (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #451,141 in Books (See Top 100 in Books)

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4 of 4 people found the following review helpful:

4.0 out of 5 stars Code Optimization 101, February 28, 2009

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Lance C. Hibbeler (Urbana, IL, USA) - See all my reviews
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This review is from: Performance Optimization of Numerically Intensive Codes (Software, Environments and Tools) (Paperback)

I think most people take Tony Hoare's optimization remark "premature optimization is the root of all evil" too seriously...especially those people that write books on programming. In this book, Goedecker and Hoise explain how to optimize code on a variety of platforms and for a variety of common scientific operations (matrix math, FFT, etc.). We, as writers of scientific software, can no longer depend on Moore's law to make our codes run faster. Similarly, optimizing compilers can't understand the "big picture" of our software, and in certain cases it is beneficial to optimize by hand.

CPUs outpace memory speed by several orders of magnitude, so the hierarchical memory paradigm (caches and the like) has been used and improved over the past few years. The main focus of the book is to show how to take advantage of the caches, but other optimization ideas are discussed as well, like various tricks with floating point numbers and library calls. All of these topics are not discussed in general programming books or parallel programming books, where the latter is a shocking revelation to me- generally if you are bothering to go to multiple processors, you should bother to optimize the hell out of the serial code along the way.

The text is easy to read, with plenty of clear examples and a chapter of case studies which go beyond the simple proof-of-concept discussion with the individual optimization methods. The examples are all in FORTRAN, and as a C guy myself, I found them easy enough to understand. The book is a few years old...it could probably stand updating. We now have several cache layers on modern processors, in addition to multiple cores on one chip. The book focuses on RISC CPUs, even though today both the RISC and CISC paradigms are migrating towards each other and borrowing ideas. Regardless of the book showing its age, it is still full of applicable knowledge for scientists and engineers that write number-crunching applications.