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

Chapter 8:

  1. Without the standard giving some guarantees on the behavior of C++ programs in the presence of threads, it is not possible to write any portable concurrent C++ programs. Of course, in practice, we were using concurrency long before C++11, but this was made possible by the compiler writers who chose to follow an additional standard, such as POSIX. The downside of that situation was that these additional standards varied. There was no portable way to write, for example, concurrent programs for Linux and Windows without conditional compilation and OS-specific extensions for each platform. Similarly, atomic operations were implemented as CPU-specific extensions. Also, there were some subtle differences between various standards followed by different compilers, which occasionally resulted in very hard-to-find bugs.
  2. The use of parallel algorithms is very simple: any algorithm that has a parallel version can be invoked with an execution policy as the first argument. If...