Book Image

C++ High Performance - Second Edition

By : Björn Andrist, Viktor Sehr
5 (2)
Book Image

C++ High Performance - Second Edition

5 (2)
By: Björn Andrist, Viktor Sehr

Overview of this book

C++ High Performance, Second Edition guides you through optimizing the performance of your C++ apps. This allows them to run faster and consume fewer resources on the device they're running on without compromising the readability of your codebase. The book begins by introducing the C++ language and some of its modern concepts in brief. Once you are familiar with the fundamentals, you will be ready to measure, identify, and eradicate bottlenecks in your C++ codebase. By following this process, you will gradually improve your style of writing code. The book then explores data structure optimization, memory management, and how it can be used efficiently concerning CPU caches. After laying the foundation, the book trains you to leverage algorithms, ranges, and containers from the standard library to achieve faster execution, write readable code, and use customized iterators. It provides hands-on examples of C++ metaprogramming, coroutines, reflection to reduce boilerplate code, proxy objects to perform optimizations under the hood, concurrent programming, and lock-free data structures. The book concludes with an overview of parallel algorithms. By the end of this book, you will have the ability to use every tool as needed to boost the efficiency of your C++ projects.
Table of Contents (17 chapters)
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Parallel arrays

We will finish this chapter by talking about iterating over elements and exploring ways to improve performance when iterating over array-like data structures. I have already mentioned two important factors for performance when accessing data: spatial locality and temporal locality. When iterating over elements stored contiguously in memory, we will increase the probability that the data we need is already cached if we manage to keep our objects small, thanks to spatial locality. Obviously, this will have a great impact on performance.

Recall the cache-thrashing example, shown at the beginning of this chapter, where we iterated over a matrix. It demonstrated that we sometimes need to think about the way we access data, even if we have a fairly compact representation of the data.

Next, we will compare how long it takes to iterate over objects of different sizes. We will start by defining two structs, SmallObject and BigObject:

struct SmallObject {