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C++ High Performance

C++ High Performance

By : Björn Andrist, Sehr
4.2 (22)
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C++ High Performance

C++ High Performance

4.2 (22)
By: Björn Andrist, Sehr

Overview of this book

C++ is a highly portable language and can be used to write both large-scale applications and performance-critical code. It has evolved over the last few years to become a modern and expressive language. This book will guide you through optimizing the performance of your C++ apps by allowing them to run faster and consume fewer resources on the device they're running on without compromising the readability of your code base. The book begins by helping you measure and identify bottlenecks in a C++ code base. It then moves on by teaching you how to use modern C++ constructs and techniques. You'll see how this affects the way you write code. Next, you'll see the importance of data structure optimization and memory management, and how it can be used efficiently with respect to CPU caches. After that, you'll see how STL algorithm and composable Range V3 should be used to both achieve faster execution and more readable code, followed by how to use STL containers and how to write your own specialized iterators. Moving on, you’ll get hands-on experience in making use of modern C++ metaprogramming and reflection to reduce boilerplate code as well as in working with proxy objects to perform optimizations under the hood. After that, you’ll learn concurrent programming and understand lock-free data structures. The book ends with an overview of parallel algorithms using STL execution policies, Boost Compute, and OpenCL to utilize both the CPU and the GPU.
Table of Contents (13 chapters)
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What to measure?

Optimizations almost always add complexity to your code. High-level optimizations, such as choice of algorithm and data structures, can make the intention of the code clearer, but for the most part, optimizations will make the code harder to read and maintain. We therefore want to be absolutely sure that the optimizations we add have an actual impact on what we are trying to achieve in terms of performance. Do we really need to make the code faster? In what way? Does the code really use too much memory? To understand what optimizations are possible, we need to have a good understanding of the requirements, such as latency, throughput, and memory usage. Optimizing code is fun, but it's also very easy to get lost without any measurable winnings. We start this section by suggesting a workflow to follow when tuning your code:

  1. Define a goal: It's easier...

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