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 2:

  1. Performance measurements are needed for two main reasons. First, they are used to define targets and describe the current status; without such measurements, we cannot say whether performance is poor or excellent; neither can we judge whether the performance targets are met. Second, measurements are used to study the effects of various factors on performance, evaluate the results of code changes and other optimizations.
  2. There is no single way to measure performance for all situations because there are usually too many contributing factors and causes to analyze using a single approach and because of the sheer volume of data that is needed to characterize the performance fully.
  3. Benchmarking done by manual instrumentation of the code has the advantage that it can collect any data you want, and it is easy to put the data in context: for each line of code, you know what function or step of the algorithm it belongs to. The main limitation is in the invasive nature...