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Mastering C++ Multithreading
Mastering C++ Multithreading

Mastering C++ Multithreading: Write robust, concurrent, and parallel applications

By Maya Posch
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Book Jul 2017 244 pages 1st Edition
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Table of content icon View table of contents Preview book icon Preview Book

Mastering C++ Multithreading

Chapter 1. Revisiting Multithreading

Chances are that if you're reading this book, you have already done some multithreaded programming in C++, or, possibly, other languages. This chapter is meant to recap the topic purely from a C++ point of view, going through a basic multithreaded application, while also covering the tools we'll be using throughout the book. At the end of this chapter, you will have all the knowledge and information needed to proceed with the further chapters.

Topics covered in this chapter include the following:

  • Basic multithreading in C++ using the native API
  • Writing basic makefiles and usage of GCC/MinGW
  • Compiling a program using make and executing it on the command-line

Getting started


During the course of this book, we'll be assuming the use of a GCC-based toolchain (GCC or MinGW on Windows). If you wish to use alternative toolchains (clang, MSVC, ICC, and so on), please consult the documentation provided with these for compatible commands.

To compile the examples provided in this book, makefiles will be used. For those unfamiliar with makefiles, they are a simple but powerful text-based format used with the make tool for automating build tasks including compiling source code and adjusting the build environment. First released in 1977, make remains among the most popular build automation tools today.

Familiarity with the command line (Bash or equivalent) is assumed, with MSYS2 (Bash on Windows) recommended for those using Windows.

The multithreaded application


In its most basic form, a multithreaded application consists of a singular process with two or more threads. These threads can be used in a variety of ways; for example, to allow the process to respond to events in an asynchronous manner by using one thread per incoming event or type of event, or to speed up the processing of data by splitting the work across multiple threads.

Examples of asynchronous responses to events include the processing of the graphical user interface (GUI) and network events on separate threads so that neither type of event has to wait on the other, or can block events from being responded to in time. Generally, a single thread performs a single task, such as the processing of GUI or network events, or the processing of data.

For this basic example, the application will start with a singular thread, which will then launch a number of threads, and wait for them to finish. Each of these new threads will perform its own task before finishing.

Let's start with the includes and global variables for our application:

#include <iostream>
#include <thread>
#include <mutex>
#include <vector>
#include <random>

using namespace std;

// --- Globals
mutex values_mtx;
mutex cout_mtx;
vector<int> values;

Both the I/O stream and vector headers should be familiar to anyone who has ever used C++: the former is here used for the standard output (cout), and the vector for storing a sequence of values.

The random header is new in c++11, and as the name suggests, it offers classes and methods for generating random sequences. We use it here to make our threads do something interesting.

Finally, the thread and mutex includes are the core of our multithreaded application; they provide the basic means for creating threads, and allow for thread-safe interactions between them.

Moving on, we create two mutexes: one for the global vector and one for cout, since the latter is not thread-safe.

Next we create the main function as follows:

int main() {
    values.push_back(42);

We push a fixed value onto the vector instance; this one will be used by the threads we create in a moment:

    thread tr1(threadFnc, 1);
    thread tr2(threadFnc, 2);
    thread tr3(threadFnc, 3);
    thread tr4(threadFnc, 4);

We create new threads, and provide them with the name of the method to use, passing along any parameters--in this case, just a single integer:

    tr1.join();
    tr2.join();
    tr3.join();
    tr4.join();

Next, we wait for each thread to finish before we continue by calling join() on each thread instance:

    cout << "Input: " << values[0] << ", Result 1: " << values[1] << ", Result 2: " << values[2] << ", Result 3: " << values[3] << ", Result 4: " << values[4] << "\n";


    return 1;
}

At this point, we expect that each thread has done whatever it's supposed to do, and added the result to the vector, which we then read out and show the user.

Of course, this shows almost nothing of what really happens in the application, mostly just the essential simplicity of using threads. Next, let's see what happens inside this method that we pass to each thread instance:

void threadFnc(int tid) {
    cout_mtx.lock();
    cout << "Starting thread " << tid << ".\n";
    cout_mtx.unlock();

In the preceding code, we can see that the integer parameter being passed to the thread method is a thread identifier. To indicate that the thread is starting, a message containing the thread identifier is output. Since we're using a non-thread-safe method for this, we use the cout_mtx mutex instance to do this safely, ensuring that just one thread can write to cout at any time:

    values_mtx.lock();
    int val = values[0];
    values_mtx.unlock();

When we obtain the initial value set in the vector, we copy it to a local variable so that we can immediately release the mutex for the vector to enable other threads to use the vector:

    int rval = randGen(0, 10);
    val += rval;

These last two lines contain the essence of what the threads created do: they take the initial value, and add a randomly generated value to it. The randGen() method takes two parameters, defining the range of the returned value:

    cout_mtx.lock();
    cout << "Thread " << tid << " adding " << rval << ". New value: " << val << ".\n";
    cout_mtx.unlock();

    values_mtx.lock();
    values.push_back(val);
    values_mtx.unlock();
}

Finally, we (safely) log a message informing the user of the result of this action before adding the new value to the vector. In both cases, we use the respective mutex to ensure that there can be no overlap when accessing the resource with any of the other threads.

Once the method reaches this point, the thread containing it will terminate, and the main thread will have one less thread to wait for to rejoin. The joining of a thread basically means that it stops existing, usually with a return value passed to the thread which created the thread. This can happen explicitly, with the main thread waiting for the child thread to finish, or in the background.

Lastly, we'll take a look at the randGen() method. Here we can see some multithreaded specific additions as well:

int randGen(const int& min, const int& max) {
    static thread_local mt19937 generator(hash<thread::id>()(this_thread::get_id()));
    uniform_int_distribution<int> distribution(min, max);
    return distribution(generator)
}

This preceding method takes a minimum and maximum value as explained earlier, which limits the range of the random numbers this method can return. At its core, it uses a mt19937-based generator, which employs a 32-bit Mersenne Twister algorithm with a state size of 19937 bits. This is a common and appropriate choice for most applications.

Of note here is the use of the thread_local keyword. What this means is that even though it is defined as a static variable, its scope will be limited to the thread using it. Every thread will thus create its own generator instance, which is important when using the random number API in the STL.

A hash of the internal thread identifier is used as a seed for the generator. This ensures that each thread gets a fairly unique seed for its generator instance, allowing for better random number sequences.

Finally, we create a new uniform_int_distribution instance using the provided minimum and maximum limits, and use it together with the generator instance to generate the random number which we return.

Makefile

In order to compile the code described earlier, one could use an IDE, or type the command on the command line. As mentioned in the beginning of this chapter, we'll be using makefiles for the examples in this book. The big advantages of this are that one does not have to repeatedly type in the same extensive command, and it is portable to any system which supports make.

Further advantages include being able to have previous generated artifacts removed automatically and to only compile those source files which have changed, along with a detailed control over build steps.

The makefile for this example is rather basic:

GCC := g++

OUTPUT := ch01_mt_example
SOURCES := $(wildcard *.cpp)
CCFLAGS := -std=c++11 -pthread

all: $(OUTPUT)

$(OUTPUT):
    $(GCC) -o $(OUTPUT) $(CCFLAGS) $(SOURCES)

clean:
    rm $(OUTPUT)

.PHONY: all

From the top down, we first define the compiler that we'll use (g++), set the name of the output binary (the .exe extension on Windows will be post-fixed automatically), followed by the gathering of the sources and any important compiler flags.

The wildcard feature allows one to collect the names of all files matching the string following it in one go without having to define the name of each source file in the folder individually.

For the compiler flags, we're only really interested in enabling the c++11 features, for which GCC still requires one to supply this compiler flag.

For the all method, we just tell make to run g++ with the supplied information. Next we define a simple clean method which just removes the produced binary, and finally, we tell make to not interpret any folder or file named all in the folder, but to use the internal method with the .PHONY section.

When we run this makefile, we see the following command-line output:

$ make
g++ -o ch01_mt_example -std=c++11 ch01_mt_example.cpp

Afterwards, we find an executable file called ch01_mt_example (with the .exe extension attached on Windows) in the same folder. Executing this binary will result in a command-line output akin to the following:

$ ./ch01_mt_example.exe

Starting thread 1.

Thread 1 adding 8. New value: 50.

Starting thread 2.

Thread 2 adding 2. New value: 44.

Starting thread 3.

Starting thread 4.

Thread 3 adding 0. New value: 42.

Thread 4 adding 8. New value: 50.

Input: 42, Result 1: 50, Result 2: 44, Result 3: 42, Result 4: 50

What one can see here already is the somewhat asynchronous nature of threads and their output. While threads 1 and 2 appear to run synchronously, starting and quitting seemingly in order, threads 3 and 4 clearly run asynchronously as both start simultaneously before logging their action. For this reason, and especially in longer-running threads, it's virtually impossible to say in which order the log output and results will be returned.

While we use a simple vector to collect the results of the threads, there is no saying whether Result 1 truly originates from the thread which we assigned ID 1 in the beginning. If we need this information, we need to extend the data we return by using an information structure with details on the processing thread or similar.

One could, for example, use struct like this:

struct result {
    int tid;
    int result;
};

The vector would then be changed to contain result instances rather than integer instances. One could pass the initial integer value directly to the thread as part of its parameters, or pass it via some other way.

Other applications


The example in this chapter is primarily useful for applications where data or tasks have to be handled in parallel. For the earlier mentioned use case of a GUI-based application with business logic and network-related features, the basic setup of a main application, which launches the required threads, would remain the same. However, instead of having each thread to be the same, each would be a completely different method.

For this type of application, the thread layout would look like this:

As the graphic shows, the main thread would launch the GUI, network, and business logic thread, with the latter communicating with the network thread to send and receive data. The business logic thread would also receive user input from the GUI thread, and send updates back to be displayed on the GUI.

Summary


In this chapter, we went over the basics of a multithreaded application in C++ using the native threading API. We looked at how to have multiple threads perform a task in parallel, and also explored how to properly use the random number API in the STL within a multithreaded application.

In the next chapter, we'll discuss how multithreading is implemented both in hardware and in operating systems. We'll see how this implementation differs per processor architecture and operating system, and how this affects our multithreaded application.

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Key benefits

  • Delve into the fundamentals of multithreading and concurrency and find out how to implement them
  • Explore atomic operations to optimize code performance
  • Apply concurrency to both distributed computing and GPGPU processing

Description

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++.

What you will learn

[*] Deep dive into the details of the how various operating systems currently implement multithreading [*] Choose the best multithreading APIs when designing a new application [*] Explore the use of mutexes, spin-locks, and other synchronization concepts and see how to safely pass data between threads [*] Understand the level of API support provided by various C++ toolchains [*] Resolve common issues in multithreaded code and recognize common pitfalls using tools such as Memcheck, CacheGrind, DRD, Helgrind, and more [*] Discover the nature of atomic operations and understand how they can be useful in optimizing code [*] Implement a multithreaded application in a distributed computing environment [*] Design a C++-based GPGPU application that employs multithreading

Product Details

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Publication date : Jul 28, 2017
Length 244 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781787121706
Category :
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Publication date : Jul 28, 2017
Length 244 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781787121706
Category :
Concepts :

Table of Contents

17 Chapters
Title Page Chevron down icon Chevron up icon
Credits Chevron down icon Chevron up icon
About the Author Chevron down icon Chevron up icon
About the Reviewer Chevron down icon Chevron up icon
www.PacktPub.com Chevron down icon Chevron up icon
Customer Feedback Chevron down icon Chevron up icon
Preface Chevron down icon Chevron up icon
1. Revisiting Multithreading Chevron down icon Chevron up icon
2. Multithreading Implementation on the Processor and OS Chevron down icon Chevron up icon
3. C++ Multithreading APIs Chevron down icon Chevron up icon
4. Thread Synchronization and Communication Chevron down icon Chevron up icon
5. Native C++ Threads and Primitives Chevron down icon Chevron up icon
6. Debugging Multithreaded Code Chevron down icon Chevron up icon
7. Best Practices Chevron down icon Chevron up icon
8. Atomic Operations - Working with the Hardware Chevron down icon Chevron up icon
9. Multithreading with Distributed Computing Chevron down icon Chevron up icon
10. Multithreading with GPGPU Chevron down icon Chevron up icon

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