Book Image

C++ High Performance

By : Björn Andrist, Viktor Sehr
5 (1)
Book Image

C++ High Performance

5 (1)
By: Björn Andrist, Viktor 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)

Understanding the basics of concurrency

A concurrent program can execute multiple tasks at the same time. Concurrent programming is, in general, a lot harder than sequential programming, but there are several reasons why a program may benefit from being concurrent:

  • Efficiency: Smartphones and desktop computers of today have multiple CPU cores that can execute multiple tasks in parallel if your program allows it to. If you manage to split a big task into subtasks that can be run in parallel, it is theoretically possible to divide the running time of the big task by the number of CPU cores. For programs that run on machines with one single core, there can still be a gain in performance if a task is I/O bound. While one subtask is waiting for I/O, other subtasks can still perform useful work on the CPU.
  • Responsiveness and low latency contexts: For applications with a graphical user...