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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

Micro-benchmarking

By the end of the previous section, we figured out where our program spends most of its execution time. We were also surprised when our "obvious" and "foolproof" optimization backfired and made the program run slower, not faster. It is clear now that we have to investigate the performance-critical function in more detail.

We already have the tools for that: the overall program is exercising this code, and we have ways to measure its performance. But we're not really interested in the rest of the program anymore, at least not until we solve the performance issues we already identified.

Working with a large program to optimize just a few lines of code has the following two major drawbacks:

First of all, even though the few lines are identified as performance-critical, it doesn't mean the rest of the program takes no time at all (in our demo example, it does, but recall that this example is supposed to represent the entire large...

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