Book Image

Modern C++ Programming Cookbook - Third Edition

By : Marius Bancila
Book Image

Modern C++ Programming Cookbook - Third Edition

By: Marius Bancila

Overview of this book

The updated third edition of Modern C++ Programming Cookbook addresses the latest features of C++23, such as the stack library, the expected and mdspan types, span buffers, formatting library improvements, and updates to the ranges library. It also gets into more C++20 topics not previously covered, such as sync output streams and source_location. The book is organized in the form of practical recipes covering a wide range of real-world problems. It gets into the details of all the core concepts of modern C++ programming, such as functions and classes, iterators and algorithms, streams and the file system, threading and concurrency, smart pointers and move semantics, and many others. You will cover the performance aspects of programming in depth, and learning to write fast and lean code with the help of best practices. You will explore useful patterns and the implementation of many idioms, including pimpl, named parameter, attorney-client, and the factory pattern. A chapter dedicated to unit testing introduces you to three of the most widely used libraries for C++: Boost.Test, Google Test, and Catch2. By the end of this modern C++ programming book, you will be able to effectively leverage the features and techniques of C++11/14/17/20/23 programming to enhance the performance, scalability, and efficiency of your applications.
Table of Contents (15 chapters)
13
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14
Index

Implementing parallel map and fold with threads

In Chapter 3, Exploring Functions, we discussed two higher-order functions: map, which applies a function to the elements of a range by either transforming the range or producing a new range, and fold (also referred to as reduce), which combines the elements of a range into a single value. The various implementations we did were sequential. However, in the context of concurrency, threads, and asynchronous tasks, we can leverage the hardware and run parallel versions of these functions to speed up their execution for large ranges, or when the transformation and aggregation are time-consuming. In this recipe, we will see a possible solution for implementing map and fold using threads.

Getting ready

You need to be familiar with the concepts of the map and fold functions. It is recommended that you read the Implementing higher-order functions map and fold recipe from Chapter 3, Exploring Functions. In this recipe, we will use the...