Book Image

High Performance Scientific Computing with C [Video]

By : Benjamin Walter Keller
Book Image

High Performance Scientific Computing with C [Video]

By: Benjamin Walter Keller

Overview of this book

<p>In this course, you’ll learn to develop scientific and numerical programs that solve problems. It’s ideal for scientists, engineers, and programmers who need to model mathematical or physical systems. You’ll get a core toolkit of algorithms that can be used in a wide variety of applications, using the low-level capabilities of the C programming language.</p> <p>The close-to-the-metal approach means you’ll learn to optimize our programs to get the absolute best performance that our hardware can provide. You’ll see how the design of algorithms affects their performance and accuracy, the tools that can be used to optimize your code and develop your intuition about numerical problems. Finally, you’ll examine the growing array of parallel solutions that enable you to take advantage of multi-core CPUs, distributed compute clusters and GPU accelerators.</p> <p>By the end of this course, you’ll know how to write fast, accurate code that can run on many different platforms and solve many different scientific problems.</p> <p>The code bundle for this video course is available at- https://github.com/PacktPublishing/High-Performance-Scientific-Computing-With-C</p> <h1>Style and Approach</h1> <p>A hands-on step-by-step approach that will take you through the important concepts of scientific and numerical computing with C.</p>
Table of Contents (4 chapters)
Chapter 2
Optimizing Scientific Code for Performance and Accuracy
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Section 1
Floating-Point Numbers and IEEE-754
How are real numbers stored and manipulated? How does this affect our program’s performance? - Learn how the IEEE-754 standard defines floating-point storage - Learn the range of floating-point numbers - See how we can go beyond this with subnormalnumbers, underflows, and overflows