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The Art of Writing Efficient Programs

The Art of Writing Efficient Programs

By : Fedor G. Pikus
4.3 (24)
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The Art of Writing Efficient Programs

The Art of Writing Efficient Programs

4.3 (24)
By: Fedor G. Pikus

Overview of this book

The great free lunch of "performance taking care of itself" is over. Until recently, programs got faster by themselves as CPUs were upgraded, but that doesn't happen anymore. The clock frequency of new processors has almost peaked, and while new architectures provide small improvements to existing programs, this only helps slightly. To write efficient software, you now have to know how to program by making good use of the available computing resources, and this book will teach you how to do that. The Art of Efficient Programming covers all the major aspects of writing efficient programs, such as using CPU resources and memory efficiently, avoiding unnecessary computations, measuring performance, and how to put concurrency and multithreading to good use. You'll also learn about compiler optimizations and how to use the programming language (C++) more efficiently. Finally, you'll understand how design decisions impact performance. By the end of this book, you'll not only have enough knowledge of processors and compilers to write efficient programs, but you'll also be able to understand which techniques to use and what to measure while improving performance. At its core, this book is about learning how to learn.
Table of Contents (18 chapters)
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1
Section 1 – Performance Fundamentals
7
Section 2 – Advanced Concurrency
11
Section 3 – Designing and Coding High-Performance Programs

Chapter 2: Performance Measurements

Whether writing a new high-performance program or optimizing an existing one, one of the first tasks set before you will be to define the performance of the code in its current state. Your success will be measured by how much you can improve its performance. Both of these statements imply the existence of a performance metric, something that can be measured and quantified. One of the more interesting outcomes of the last chapter was the discovery that there isn't even a single definition of performance that fits every need: what you measure when you want to quantify performance depends on the nature of the problem you're working on.

But there is much more to the measurements than simply defining the goals and confirming success. Every step of your performance optimization, whether of existing code or new code you're just writing, should be guided and informed by measurements.

The first rule of performance is Never guess about...

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