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.

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Table of Contents (13 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
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1
A Brief Introduction to C++
A Brief Introduction to C++
Why C++?
The aim of this book
C++ compared with other languages
Class interfaces and exceptions
Libraries used in this book
Summary
2
Modern C++ Concepts
Modern C++ Concepts
Automatic type deduction with the auto keyword
The lambda function
Const propagation for pointers
Move semantics explained
Representing optional values with std::optional
Representing dynamic values with std::any
Summary
3
Measuring Performance
Measuring Performance
Asymptotic complexity and big O notation
What to measure?
Knowing your code and hot spots
Summary
4
Data Structures
Data Structures
Properties of computer memory
STL containers
Parallel arrays
Summary
5
A Deeper Look at Iterators
A Deeper Look at Iterators
The iterator concept
Summary
6
STL Algorithms and Beyond
STL Algorithms and Beyond
Using STL algorithms as building blocks
The future of STL and the ranges library
Summary
7
Memory Management
Memory Management
Computer memory
Process memory
Objects in memory
Memory ownership
Small size optimization
Custom memory management
Summary
8
Metaprogramming and Compile-Time Evaluation
Metaprogramming and Compile-Time Evaluation
Introduction to template metaprogramming
Type traits
The constexpr keyword
Heterogeneous containers
The std::variant
Real world examples of metaprogramming
Summary
9
Proxy Objects and Lazy Evaluation
Proxy Objects and Lazy Evaluation
An introduction to lazy evaluation and proxy objects
Proxy objects
Postponing an sqrt computation when comparing distances
Creative operator overloading and proxy objects
Summary
10
Concurrency
Concurrency
Understanding the basics of concurrency
What makes concurrent programming hard?
Concurrency and parallelism
Concurrent programming in C++
Lock-free programming
Performance guidelines
Summary
11
Parallel STL
Parallel STL
Importance of parallelism
Parallel algorithms
Parallel STL
Executing STL algorithms on the GPU
Boost Compute
12
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What makes concurrent programming hard?

There are a couple of reasons why concurrent programming is hard, and, if you have been writing concurrent programs before, you have most likely already encountered the ones listed here:

  1. Sharing state between multiple threads in a safe manner is hard. Whenever we have data that can be read and written to at the same time, we need some way of protecting that data from data races. We will see a lot of examples of this later on.
  1. Concurrent programs are usually more complicated to reason about because of the multiple parallel execution flows.
  2. Concurrency complicates debugging. Bugs that occur because of data races can be very hard to debug since they are dependent on how threads are being scheduled. These kinds of bugs can be hard to reproduce and in the worst case, they cease to exist when running the program using a debugger. Sometimes...
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