Tracing the demo application

In the demonstration code of Chapter 1, Revisiting Multithreading, we looked at a simple c++11 application which used four threads to perform some processing. In this section, we will look at the same application, but from a hardware and OS perspective.

When we look at the start of the code in the main function, we see that we create a data structure containing a single (integer) value:

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

After the OS creates a new task and associated stack structure, an instance of a vector data structure (customized for integer types) is allocated on the stack. The size of this was specified in the binary file's global data section (BSS for ELF).

When the application's execution is started using its entry function (main() by default), the data structure is modified to contain the new integer value.

Next, we create four threads, providing each with some initial data:

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

For the OS, this means creating new data structures, and allocating a stack for each new thread. For the hardware, this initially does not change anything if no hardware-based task switching is used.

At this point, the OS's scheduler and the CPU can combine to execute this set of tasks (threads) as efficiently and quickly as possible, employing features of the hardware including SMP, SMT, and so on.

After this, the main thread waits until the other threads stop executing:

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

These are blocking calls, which mark the main thread as being blocked until these four threads (tasks) finish executing. At this point, the OS's scheduler will resume execution of the main thread.

In each newly created thread, we first output a string on the standard output, making sure that we lock the mutex to ensure synchronous access:

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

A mutex, in essence, is a singular value being stored on the stack of heap, which then is accessed using an atomic operation. This means that some form of hardware support is required. Using this, a task can check whether it is allowed to proceed yet, or has to wait and try again.

In this last particular piece of code, this mutex lock allows us to output on the standard C++ output stream without other threads interfering.

After this, we copy the initial value in the vector to a local variable, again ensuring that it's done synchronously:

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

The same thing happens here, except now the mutex lock allows us to read the first value in the vector without risking another thread accessing or even changing it while we use it.

This is followed by the generating of a random number as follows:

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

This uses the randGen() method, which is as follows:

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 method is interesting due to its use of a thread-local variable. Thread-local storage is a section of a thread's memory which is specific to it, and used for global variables, which, nevertheless, have to remain limited to that specific thread.

This is very useful for a static variable like the one used here. That the generator instance is static is because we do not want to reinitialize it every single time we use this method, yet we do not want to share this instance across all threads. By using a thread-local, static instance, we can accomplish both goals. A static instance is created and used, but separately for each thread.

The Thread function then ends with the same series of mutexes being locked, and the new value being copied to the array.

    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();
}

Here we see the same synchronous access to the standard output stream, followed by synchronous access to the values data structure.