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)
15
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Index

Parallel algorithms

As mentioned in Chapter 11, Concurrency, the terms concurrency and parallelism can be a little hard to distinguish from each other. As a reminder, a program is said to run concurrently if it has multiple individual control flows running during overlapping time periods. On the other hand, a parallel program executes multiple tasks or subtasks simultaneously (at the exact same time), which requires hardware with multiple cores. We use parallel algorithms to optimize latency or throughput. It makes no sense to parallelize algorithms if we don't have hardware that can execute multiple tasks simultaneously to achieve better performance. A few simple formulas will now follow to help you understand what factors need to be considered when evaluating parallel algorithms.

Evaluating parallel algorithms

In this chapter, speedup is defined as the ratio between a sequential and a parallel version of an algorithm, as follows:

T1 is the time it takes to...