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Mastering C++ Multithreading

By : Maya Posch

3.1 (12)

Buy this Book

Mastering C++ Multithreading

3.1 (12)

By: Maya Posch

Buy this Book

Overview of this book

Multithreaded applications execute multiple threads in a single processor environment, allowing developers achieve concurrency. This book will teach you the finer points of multithreading and concurrency concepts and how to apply them efficiently in C++. Divided into three modules, we start with a brief introduction to the fundamentals of multithreading and concurrency concepts. We then take an in-depth look at how these concepts work at the hardware-level as well as how both operating systems and frameworks use these low-level functions. In the next module, you will learn about the native multithreading and concurrency support available in C++ since the 2011 revision, synchronization and communication between threads, debugging concurrent C++ applications, and the best programming practices in C++. In the final module, you will learn about atomic operations before moving on to apply concurrency to distributed and GPGPU-based processing. The comprehensive coverage of essential multithreading concepts means you will be able to efficiently apply multithreading concepts while coding in C++.

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Free Chapter

Revisiting Multithreading

Getting started

The multithreaded application

Other applications

Summary

Multithreading Implementation on the Processor and OS

Defining processes and threads

The stack

Defining multithreading

Schedulers

Tracing the demo application

Mutual exclusion implementations

Summary

C++ Multithreading APIs

API overview

POSIX threads

Windows threads

Boost

POCO

C++ threads

Putting it together

Summary

Thread Synchronization and Communication

Safety first

The scheduler

Sharing data

Summary

Native C++ Threads and Primitives

The STL threading API

The 2011 standard

C++14

C++17

STL organization

Thread class

Mutex

Shared mutex

Condition variable

Future

Atomics

Summary

Debugging Multithreaded Code

When to start debugging

The humble debugger

Dynamic analysis tools

Summary

Best Practices

Proper multithreading

Wrongful expectations - deadlocks

Being careless - data races

Mutexes aren't magic

Locks are fancy mutexes

Threads versus the future

Static order of initialization

Summary

Atomic Operations - Working with the Hardware

Atomic operations

Summary

Multithreading with Distributed Computing

Distributed computing, in a nutshell

Installing Open MPI

Distributing jobs across nodes

MPI communication

MPI versus threads

Potential issues

Summary

Multithreading with GPGPU

The GPGPU processing model

Setting up a development environment

A basic OpenCL application

GPU memory management

GPGPU and multithreading

Potential issues

Debugging GPGPU applications

Summary

Customer Reviews

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Debugging GPGPU applications

The biggest challenge with GPGPU applications is that of debugging a kernel. CUDA comes with a simulator for this reason, which allows one to run and debug a kernel on a CPU. OpenCL allows one to run a kernel on a CPU without modification, although this may not get the exact same behavior (and bugs) as when run on a specific GPU device.

A slightly more advanced method involves the use of a dedicated debugger such as Nvidia's Nsight, which comes in versions both for Visual Studio (https://developer.nvidia.com/nvidia-nsight-visual-studio-edition) and Eclipse (https://developer.nvidia.com/nsight-eclipse-edition).

According to the marketing blurb on the Nsight website:

NVIDIA Nsight Visual Studio Edition brings GPU computing into Microsoft Visual Studio (including multiple instances of VS2017). This application development environment for GPUs allows...