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

Understanding the cost of memory synchronization

The last section was all about running multiple threads on the same machine without any interaction between these threads. If you can split the work your program does between threads in a way that makes such implementation possible, by all means, do it. You cannot beat the performance of such an embarrassingly parallel program.  

More often than not, threads must interact with each other because they are contributing work to a common result. Such interactions happen by means of threads communicating with each other through the one resource they share, the memory. We must now understand the performance implications of this.

Let us start with a trivial example. Say we want to compute a sum of many values. We have many numbers to add, but, in the end, only one result. We have so many numbers to add that we want to split the work of adding them between several threads. But there is only one result value, so the threads have...