Book Image

The Art of Writing Efficient Programs

By : Fedor G. Pikus
3 (2)
Book Image

The Art of Writing Efficient Programs

3 (2)
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)
1
Section 1 – Performance Fundamentals
7
Section 2 – Advanced Concurrency
11
Section 3 – Designing and Coding High-Performance Programs

Summary

C++11 was the first version of the standard to acknowledge the existence of threads. It laid the foundation for documenting the behavior of C++ programs in concurrent environments and provided some useful functionality in the standard library. Out of this functionality, the basic synchronization primitives and the threads themselves are the most useful. Subsequent versions extended and completed these features with relatively minor enhancements.

C++17 brought a major advancement in the form of parallel STL. The performance is, of course, determined by the implementation. The observed performance is quite good as long as the data corpus is sufficiently large, even on hard-to-parallelize algorithms like search and partition. However, if the sequences of data are too short, parallel algorithms actually degrade the performance.

C++20 added coroutine support. You have seen how stackless coroutines work, in theory and on some basic examples. However, it is too early to talk...