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The Art of Writing Efficient Programs

The Art of Writing Efficient Programs

By : Fedor G. Pikus
4.3 (24)
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The Art of Writing Efficient Programs

The Art of Writing Efficient Programs

4.3 (24)
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)
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1
Section 1 – Performance Fundamentals
7
Section 2 – Advanced Concurrency
11
Section 3 – Designing and Coding High-Performance Programs

The performance begins with the CPU

As we have observed in the earlier chapters, an efficient program is one that makes full use of the available hardware resources and does not waste them for tasks that are not needed. A high-performing program cannot be described so simply because performance can be defined only with respect to specific targets. Nonetheless, in this book, and in particular, in this chapter, we are largely concerned with the computational performance or throughput: how fast can we solve a given problem with the hardware resources we have? This type of performance is closely related to efficiency: our program will deliver the result faster if every computation it executes brings us closer to the result, and, at every moment, we do as much computing as possible.

This brings us to the next question: just how much computing can be done, say, in one second? The answer, of course, will depend on what hardware you have, how much of it, and how efficiently your program...

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