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

In this chapter, we have learned about the computing capabilities of the main processor and how to use them effectively. The key to high performance is to make maximum use of all available computing resources: a program that computes two results at the same time is faster than the one that computes the second result later (assuming the computing power is available). As we have learned, the CPU has a lot of computing units for various types of computations, most of which are idle at any given moment unless the program is very highly optimized.

We have seen that the main restriction on efficient use of the CPU's instruction-level parallelism is usually the data dependencies: there simply isn't enough work that can be done in parallel to keep the CPU busy. The hardware solution to this problem is pipelining: the CPU doesn't just execute the code at the current point in the program but takes some computations from the future that have no unsatisfied data dependencies...