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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

Design for optimal data access

We discussed the impact of data organization on performance in detail in Chapter 4, Memory Architecture and Performance. There, we observed that whenever you have no "hot code," you will usually find "hot data." In other words, if the runtime is spread over a large part of the code and nothing stands out as a good optimization opportunity, it is likely that there is some data (one or more data structures) that is being accessed throughout the program, and it is these accesses that limit the overall performance.

This can be a very unpleasant situation to find oneself in: the profiler shows no low-hanging fruit for optimization, you may find some sub-optimal code, but the measurements show that you can save at most a percent or two of total runtime from each of these places. Unless you know what to look for, it is very hard to find ways to improve the performance of such code.

Now that you know that you need to look for &quot...

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