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Multithreading and Concurrency

My book, C++ Concurrency in Action contains a detailed description of the C++11 threading facilities, and techniques for designing concurrent code.

The just::thread implementation of the new C++11 and C++14 thread library is available for Microsoft Visual Studio 2005, 2008, 2010, 2012, 2013 and 2015, and TDM gcc 4.5.2, 4.6.1 and 4.8.1 on Windows, g++ 4.3, 4.4, 4.5, 4.6, 4.7, 4.8 and 4.9 on Linux, and MacPorts g++ 4.3, 4.4, 4.5, 4.6, 4.7 and 4.8 on MacOSX. Order your copy today.

Wrapping Callbacks with Futures

Friday, 03 February 2017

Libraries the perform time-consuming operations, or network-based operations, often provide a means of running the task asynchronously, so that your code can continue with other things while this operation is performed in the background.

Such functions often allow you to provide a callback which is invoked when the operation has completed. The result of the operation may be supplied directly to the callback, or the callback might be expected to make further calls into the library to detect the result.

Either way, though it is a useful facility, it doesn't work well with code that needs to wait for the result — for this, you need something that can be waited on. Futures are an ideal such mechanism, because they provide a common mechanism for waiting that is abstracted away from the details of this specific library. They also allow the result to be transferred directly to the waiting thread, again abstracting away the details.

So, what do you do if the library you want to use provides a callback facility, and not a future-based wait facility? You wrap the callback in a future.

Promises, promises

The key to wrapping a callback in a future is the promise. A promise is the producer side of a future: you set the value through the promise in order to consume it via the future.

In C++, promises are provided through the std::promise class template; the template parameter specifies the type of the data being transferred, and thus is the same as the template parameter of the std::future that you will eventually be returning to the user. When you call prom.set_value() on some promise prom, then the corresponding future (retrieved by calling prom.get_future()) will become ready, holding the relevant value.

Promises and futures are not unique to C++; they are available in at least JavaScript, Python, Java and Scala, and pretty much any modern concurrency library for any language will have something that is equivalent. Adapting the examples to your favourite language is left as an exercise for the reader.

Simple callbacks

The most convenient case for us when trying to wrap a function that takes a callback is where the function we are calling takes anything that is callable as the callback. In C++ this might be represented as an instantiation of std::function. e.g.

class request_data;
class retrieved_data;

void async_retrieve_data(
    request_data param,
    std::function<void(retrieved_data)> callback);

What we need to do to wrap this is the following:

  1. Create a promise.
  2. Get the future from the promise.
  3. Construct a callable object that can hold the promise, and will set the value on the promise when called.
  4. Pass that object as the callback.
  5. Return the future that we obtained in step 2.

This is the same basic set of steps as we'll be doing in all the examples that follow; it is the details that will differ.

Note: std::function requires that the callable object it wraps is copyable (so that if the std::function object itself is copied, it can copy the wrapped callable object), so we cannot hold the std::promise itself by value, as promises are not copyable.

We can thus write this using a C++ lambda as the callable object:

std::future<retrieved_data> wrapped_retrieve_data(request_data param) {
    std::shared_ptr<std::promise<retrieved_data>> prom=
        std::make_shared<std::promise<retrieved_data>>();
    std::future<retrieved_data> res=prom->get_future();
    async_retrieve_data(
        param,
        [prom](retrieved_data result){
            prom->set_value(result);
    });
    return res;
}

Here, we're using a std::shared_ptr to provide a copyable wrapper for the promise, so that it can be copied into the lambda, and the lambda itself will be copyable. When the copy of the lambda is called, it sets the value on the promise through its copy of the std::shared_ptr, and the future that is returned from wrapped_retrieve_data will become ready.

That's all very well if the function uses something like std::function for the callback. However, in practice that's not often the case. More often you have something that takes a plain function and a parameter to pass to this function; an approach inherited from C. Indeed, many APIs that you might wish to wrap are C APIs.

Plain function callbacks with a user_data parameter

A function that takes a plain function for the callback and a user_data parameter to pass to the function often looks something like this:

void async_retrieve_data(
    request_param param,
    void (*callback)(uintptr_t user_data,retrieved_data data),
    uintptr_t user_data);

The user_data you supply to async_retrieve_data is passed as the first parameter of your callback when the data is ready.

In this case, wrapping out callback is a bit more tricky, as we cannot just pass our lambda directly. Instead, we must create an object, and pass something to identify that object via the user_data parameter. Since our user_data is uintptr_t, it is large enough to hold a pointer, so we can cast the pointer to our object to uintptr_t, and pass it as the user_data. Our callback can then cast it back before using it. This is a common approach when passing C++ objects through C APIs.

The issue is: what object should we pass a pointer to, and how will its lifetime be managed?

One option is to just allocate our std::promise on the heap, and pass the pointer to that:

void wrapped_retrieve_data_callback(uintptr_t user_data,retrieved_data data) {
    std::unique_ptr<std::promise<retrieved_data>> prom(
        reinterpret_cast<std::promise<retrieved_data>*>(user_data));
    prom->set_value(data);
}

std::future<retrieved_data> wrapped_retrieve_data(request_data param) {
    std::unique_ptr<std::promise<retrieved_data>> prom=
        std::make_unique<std::promise<retrieved_data>>();
    std::future<retrieved_data> res=prom->get_future();
    async_retrieve_data(
        param,
        wrapped_retrieve_data_callback,
        reinterpret_cast<uintptr_t>(prom->get()));
    prom.release();
    return res;
}

Here, we use std::make_unique to construct our promise, and give us a std::unique_ptr pointing to it. We then get the future as before, and call the function we're wrapping, passing in the raw pointer, cast to an integer. We then call release on our pointer, so the object isn't deleted when we return from the function.

In our callback, we then cast the user_data parameter back to a pointer, and construct a new std::unique_ptr object to take ownership of it, and ensure it gets deleted. We can then set the value on our promise as before.

This is a little bit more convoluted than the lamda version from before, but it works in more cases. Often the APIs will take a void* rather than a uintptr_t, in which case you only need a static_cast rather than the scary reinterpret_cast, but the structure is the same.

An alternative to heap-allocating the promise directly is to store it in a global container (e.g. a std::list<std::promise<T>>), provided that its address can't change. You then need to ensure that it gets destroyed at a suitable point, otherwise you'll end up with a container full of used promises.

If you've only got C++11 futures, then the advantages to wrapping the callback-based API like so is primarily about abstracting away the interface, and providing a means of waiting for the result. However, if your library provides the extended futures from the C++ Concurrency TS then you can benefit from continuations to add additional functions to call when the data is ready, without having to modify the callback.

Summary

Wrapping asynchronous functions that take callbacks with futures provides a nice abstraction boundary to separate the details of the API call from the rest of your code. It is a common pattern in all languages that provide the future/promise abstraction, especially where that abstraction allows for continuations.

If you have any thoughts on this, let me know in the comments below.

Posted by Anthony Williams
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Why do we need atomic_shared_ptr?

Friday, 21 August 2015

One of the new class templates provided in the upcoming Concurrency TS is atomic_shared_ptr, along with its counterpart atomic_weak_ptr. As you might guess, these are the std::shared_ptr and std::weak_ptr equivalents of std::atomic<T*>, but why would one need them? Doesn't std::shared_ptr already have to synchronize the reference count?

std::shared_ptr and multiple threads

std::shared_ptr works great in multiple threads, provided each thread has its own copy or copies. In this case, the changes to the reference count are indeed synchronized, and everything just works, provided of course that what you do with the shared data is correctly synchronized.

class MyClass;
void thread_func(std::shared_ptr<MyClass> sp){
    sp->do_stuff();
    std::shared_ptr<MyClass> sp2=sp;
    do_stuff_with(sp2);
}
int main(){
    std::shared_ptr<MyClass> sp(new MyClass);
    std::thread thread1(thread_func,sp);
    std::thread thread2(thread_func,sp);
    thread2.join();
    thread1.join();
}

In this example, you need to ensure that it is safe to call MyClass::do_stuff() and do_stuff_with() from multiple threads concurrently on the same instance, but the reference counts are handled OK.

Figure 1: Separate shared_ptr instances

Figure 1: Separate `shared_ptr` instances

The problems come when you try and share a single std::shared_ptr instance between threads.

Sharing a std::shared_ptr instance between threads

I could provide a trivial example of a std::shared_ptr instance shared between threads, but instead we'll look at something a little more interesting, to give a better feel for why you might need it.

Consider for a moment a simple singly-linked list, where each node holds a pointer to the next:

class MyList{
    struct Node{
        MyClass data;
        std::unique_ptr<Node> next;
    };
    std::unique_ptr<Node> head;
    // constructors etc. omitted.
};

If we're going to access this from 2 threads, then we have a choice:

  1. We could wrap the whole object with a mutex, so only one thread is accessing the list at a time, or
  2. We could try and allow concurrent accesses.

For the sake of this article, we're going to assume we're allowing concurrent accesses.

Let's start simply: we want to traverse the list. Writing a traversal function is easy:

class MyList{
void traverse(std::function<void(MyClass)> f){
    Node* p=head.get();
    while(p){
        f(p->data);
        p=p->next;
    }
};

Assuming the list is immutable, this is fine, but immutable lists are no fun! We want to remove items from the front of our list. What changes do we need to make to support that?

Removing from the front of the list

If everything was single threaded, removing an element would be easy:

class MyList{
void pop_front(){
    Node* p=head.get();
    if(p){
        head=std::move(p->next);
    }
}
};

If the list is not empty, the new head is the next pointer of the old head. However, we've got multiple threads accessing this list, so things aren't so straightforward.

Suppose we have a list of 3 elements.

A -> B -> C

If one thread is traversing the list, and another is removing the first element, there is a potential for a race.

  1. Thread X reads the head pointer for the list and gets a pointer to A.
  2. Thread Y removes A from the list and deletes it.
  3. Thread X tries to access the data for node A, but node A has been deleted, so we have a dangling pointer and undefined behaviour.

How can we fix it?

The first thing to change is to make all our std::unique_ptrs into std::shared_ptrs, and have our traversal function take a std::shared_ptr copy rather than using a raw pointer. That way, node A won't be deleted immediately, since our traversing thread still holds a reference.

class MyList{
    struct Node{
        MyClass data;
        std::shared_ptr<Node> next;
    };
    std::shared_ptr<Node> head;

    void traverse(std::function<void(MyClass)> f){
        std::shared_ptr<Node> p=head;
        while(p){
            f(p->data);
            p=p->next;
        }
    }
    void pop_front(){
        std::shared_ptr<Node> p=head;
        if(p){
            head=std::move(p->next);
        }
    }
    // constructors etc. omitted.
};

Unfortunately that only fixes that race condition. There is a second race that is just as bad.

The second race condition

The second race condition is on head itself. In order to traverse the list, thread X has to read head. In the mean time, thread Y is updating head. This is the very definition of a data race, and is thus undefined behaviour.

We therefore need to do something to fix it.

We could use a mutex to protect head. This would be more fine-grained than a whole-list mutex, since it would only be held for the brief time when the pointer was being read or changed. However, we don't need to: we can use std::experimental::atomic_shared_ptr instead.

The implementation is allowed to use a mutex internally with atomic_shared_ptr, in which case we haven't gained anything with respect to performance or concurrency, but we have gained by reducing the maintenance load on our code. We don't have to have an explicit mutex data member, and we don't have to remember to lock it and unlock it correctly around every access to head. Instead, we can defer all that to the implementation with a single line change:

class MyList{
    std::experimental::atomic_shared_ptr<Node> head;
};

Now, the read from head no longer races with a store to head: the implementation of atomic_shared_ptr takes care of ensuring that the load gets either the new value or the old one without problem, and ensuring that the reference count is correctly updated.

Unfortunately, the code is still not bug free: what if 2 threads try and remove a node at the same time.

Race 3: double removal

As it stands, pop_front assumes it is the only modifying thread, which leaves the door wide open for race conditions. If 2 threads call pop_front concurrently, we can get the following scenario:

  1. Thread X loads head and gets a pointer to node A.
  2. Thread Y loads head and gets a pointer to node A.
  3. Thread X replaces head with A->next, which is node B.
  4. Thread Y replaces head with A->next, which is node B.

So, two threads call pop_front, but only one node is removed. That's a bug.

The fix here is to use the ubiquitous compare_exchange_strong function, a staple of any programmer who's ever written code that uses atomic variables.

class MyList{
    void pop_front(){
        std::shared_ptr<Node> p=head;
        while(p &&
            !head.compare_exchange_strong(p,p->next));
    }
};

If head has changed since we loaded it, then the call to compare_exchange_strong will return false, and reload p for us. We then loop again, checking that p is still non-null.

This will ensure that two calls to pop_front removes two nodes (if there are 2 nodes) without problems either with each other, or with a traversing thread.

Experienced lock-free programmers might well be thinking "what about the ABA problem?" right about now. Thankfully, we don't have to worry!

What no ABA problem?

That's right, pop_front does not suffer from the ABA problem. Even assuming we've got a function that adds new values, we can never get a new value of head the same as the old one. This is an additional benefit of using std::shared_ptr: the old node is kept alive as long as one thread holds a pointer. So, thread X reads head and gets a pointer to node A. This node is now kept alive until thread X destroys or reassigns that pointer. That means that no new node can be allocated with the same address, so if head is equal to the value p then it really must be the same node, and not just some imposter that happens to share the same address.

Lock-freedom

I mentioned earlier that implementations may use a mutex to provide the synchronization in atomic_shared_ptr. They may also manage to make it lock-free. This can be tested using the is_lock_free() member function common to all the C++ atomic types.

The advantage of providing a lock-free atomic_shared_ptr should be obvious: by avoiding the use of a mutex, there is greater scope for concurrency of the atomic_shared_ptr operations. In particular, multiple concurrent reads can proceed unhindered.

The downside will also be apparent to anyone with any experience with lock-free code: the code is more complex, and has more work to do, so may be slower in the uncontended cases. The other big downside of lock-free code (maintenance and correctness) is passed over to the implementor!

It is my belief that the scalability benefits outweight the complexity, which is why Just::Thread v2.2 will ship with a lock-free atomic_shared_ptr implementation.

Posted by Anthony Williams
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All the world's a stage... C++ Actors from Just::Thread Pro

Wednesday, 15 July 2015

Handling shared mutable state is probably the single hardest part of writing multithreaded code. There are lots of ways to address this problem; one of the common ones is the actors metaphor. Going back to Hoare's Communicating Sequential Processes, the idea is simple - you build your program out of a set of actors that send each other messages. Each actor runs normal sequential code, occasionally pausing to receive incoming messages from other actors. This means that you can analyse the behaviour of each actor independently; you only need to consider which messages might be received at each receive point. You could treat each actor as a state machine, with the messages triggering state transitions.

This is how Erlang processes work: each process is an actor, which runs independently from the other processes, except that they can send messages to each other. Just::thread Pro: Actors Edition adds library facilities to support this to C++. In the rest of this article I will describe how to write programs that take advantage of it. Though the details will differ, the approach can be used with other libraries that provide similar facilities, or with the actor support in other languages.

Simple Actors

Actors are embodied in the jss::actor class. You pass in a function or other callable object (such as a lambda function) to the constructor, and this function is then run on a background thread. This is exactly the same as for std::thread, except that the destructor waits for the actor thread to finish, rather than calling std::terminate.

void simple_function(){
    std::cout<<"simple actor\n";
}

int main(){
    jss::actor actor1(simple_function);
    jss::actor actor2([]{
        std::cout<<"lambda actor\n";
    });
}

The waiting destructor is nice, but it's really a side issue — the main benefit of actors is the ability to communicate using messages rather than shared state.

Sending and receiving messages

To send a message to an actor you just call the send() member function on the actor object, passing in whatever message you wish to send. send() is a function template, so you can send any type of message — there are no special requirements on the message type, just that it is a MoveConstructible type. You can also use the stream-insertion operator to send a message, which allows easy chaining e.g.

actor1.send(42);
actor2.send(MyMessage("some data"));
actor2<<Message1()<<Message2();

Sending a message to an actor just adds it to the actor's message queue. If the actor never checks the message queue then the message does nothing. To check the message queue, the actor function needs to call the receive() static member function of jss::actor. This is a static member function so that it always has access to the running actor, anywhere in the code — if it were a non-static member function then you would need to ensure that the appropriate object was passed around, which would complicate interfaces, and open up the possibility of the wrong object being passed around, and lifetime management issues.

The call to jss::actor::receive() will then block the actor's thread until a message that it can handle has been received. By default, the only message type that can be handled is jss::stop_actor. If a message of this type is sent to an actor then the receive() function will throw a jss::stop_actor exception. Uncaught, this exception will stop the actor running. In the following example, the only output will be "Actor running", since the actor will block at the receive() call until the stop message is sent, and when the message arrives, receive() will throw.

void stoppable_actor(){
    std::cout<<"Actor running"<<std::endl;
    jss::actor::receive();
    std::cout<<"This line is never run"<<std::endl;
}

int main(){
    jss::actor a1(stoppable_actor);
    std::this_thread::sleep_for(std::chrono::seconds(1));
    a1.send(jss::stop_actor());
}

Sending a "stop" message is common-enough that there's a special member function for that too: stop(). "a1.stop()" is thus equivalent to "a1.send(jss::stop_actor())".

Handling a message of another type requires that you tell the receive() call what types of message you can handle, which is done by chaining one or more calls to the match() function template. You must specify the type of the message to handle, and then provide a function to call if the message is received. Any messages other than jss::stop_actor not specified in a match() call will be removed from the queue, but otherwise ignored. In the following example, only messages of type "int" and "std::string" are accepted; the output is thus:

Waiting
42
Waiting
Hello
Waiting
Done

Here's the code:

void simple_receiver(){
    while(true){
        std::cout<<"Waiting"<<std::endl;
        jss::actor::receive()
            .match<int>([](int i){std::cout<<i<<std::endl;})
            .match<std::string>([](std::string const&s){std::cout<<s<<std::endl;});
    }
}

int main(){
    {
        jss::actor a(simple_receiver);
        a.send(true);
        a.send(42);
        a.send(std::string("Hello"));
        a.send(3.141);
        a.send(jss::stop_actor());
    } // wait for actor to finish
    std::cout<<"Done"<<std::endl;
}

It is important to note that the receive() call will block until it receives one of the messages you have told it to handle, or a jss::stop_actor message, and unexpected messages will be removed from the queue and discarded. This means the actors don't accumulate a backlog of messages they haven't yet said they can handle, and you don't have to worry about out-of-order messages messing up a receive() call.

These simple examples have just had main() sending messages to the actors. For a true actor-based system we need them to be able to send messages to each other, and reply to messages. Let's take a look at how we can do that.

Referencing one actor from another

Suppose we want to write a simple time service actor, that sends the current time back to any other actor that asks it for the time. At first thought it looks rather simple: write a simple loop that handles a "time request" message, gets the time, and sends a response. It won't be that much different from our simple_receiver() function above:

struct time_request{};

void time_server(){
    while(true){
        jss::actor::receive()
            .match<time_request>([](time_request r){
                auto now=std::chrono::system_clock::now();
                ????.send(now);
        });
    }
}

The problem is, we don't know which actor to send the response to — the whole point of this time server is that it will respond to a message from any other actor. The solution is to pass the sender as part of the message. We could just pass a pointer or reference to the jss::actor instance, but that requires that the actor knows the location of its own controlling object, which makes it more complicated — none of the examples we've had so far could know that, since the controlling object is a local variable declared in a separate function. What is needed instead is a simple means of identifying an actor, which the actor code can query — an actor reference. The type of an actor reference is jss::actor_ref, which is implicitly constructible from a jss::actor. An actor can also obtain a reference to itself by calling jss::actor::self(). jss::actor_ref has a send() member function and stream insertion operator for sending messages, just like jss::actor. So, we can put the sender of our time_request message in the message itself as a jss::actor_ref data member, and use that when sending the response.

struct time_request{
    jss::actor_ref sender;
};

void time_server(){
    while(true){
        jss::actor::receive()
            .match<time_request>([](time_request r){
                auto now=std::chrono::system_clock::now();
                r.sender<<now;
            });
    }
}

void query(jss::actor_ref server){
    server<<time_request{jss::actor::self()};
    jss::actor::receive()
        .match<std::chrono::system_clock::time_point>(
            [](std::chrono::system_clock::time_point){
                std::cout<<"time received"<<std::endl;
        });
}

Dangling references

If you use jss::actor_ref then you have to be prepared for the case that the referenced actor might have stopped executing by the time you send the message. In this case, any attempts to send a message through the jss::actor_ref instance will throw an exception of type [jss::no_actor]http://www.stdthread.co.uk/prodoc/headers/actor/no_actor.html. To be robust, our time server really ought to handle that too — if an unhandled exception of any type other than jss::stop_actor escapes the actor function then the library will call std::terminate. We should therefore wrap the attempt to send the message in a try-catch block.

void time_server(){
    while(true){
        jss::actor::receive()
            .match<time_request>([](time_request r){
                auto now=std::chrono::system_clock::now();
                try{
                    r.sender<<now;
                } catch(jss::no_actor&){}
            });
    }
}

We can now set up a pair of actors that play ping-pong:

struct pingpong{
    jss::actor_ref sender;
};

void pingpong_player(std::string message){
    while(true){
        try{
            jss::actor::receive()
                .match<pingpong>([&](pingpong msg){
                    std::cout<<message<<std::endl;
                    std::this_thread::sleep_for(std::chrono::milliseconds(50));
                    msg.sender<<pingpong{jss::actor::self()};
                });
        }
        catch(jss::no_actor&){
            std::cout<<"Partner quit"<<std::endl;
            break;
        }
    }
}

int main(){
    jss::actor ping(pingpong_player,"ping");
    jss::actor pong(pingpong_player,"pong");
    ping<<pingpong{pong};
    std::this_thread::sleep_for(std::chrono::seconds(1));
    ping.stop();
    pong.stop();
}

This will give output along the lines of the following:

ping
pong
ping
pong
ping
pong
ping
pong
ping
pong
ping
pong
ping
pong
ping
pong
ping
pong
ping
pong
Partner quit

The sleep in the player's message handler is to slow everything down — if you take it out then messages will go back and forth as fast as the system can handle, and you'll get thousands of lines of output. However, even at full speed the pings and pongs will be interleaved, because sending a message synchronizes with the receive() call that receives it.

That's essentially all there is to it — the rest is just application design. As an example of how it can all be put together, let's look at an implementation of the classic sleeping barber problem.

The Lazy Barber

For those that haven't met it before, the problem goes like this: Mr Todd runs a barber shop, but he's very lazy. If there are no customers in the shop then he likes to go to sleep. When a customer comes in they have to wake him up if he's asleep, take a seat if there is one, or come back later if there are no free seats. When Mr Todd has cut someone's hair, he must move on to the next customer if there is one, otherwise he can go back to sleep.

The barber actor

Let's start with the barber. He sleeps in his chair until a customer comes in, then wakes up and cuts the customer's hair. When he's done, if there is a waiting customer he cuts that customer's hair. If there are no customers, he goes back to sleep, and finally at closing time he goes home. This is shown as a state machine in figure 1.

Figure 1: Barber State Machine

Figure 1: Barber State Machine

This translates into code as shown in listing 1.The wait loops for "sleeping" and "cutting hair" have been combined, since almost the same set of messages is being handled in each case — the only difference is that the "cutting hair" state also has the option of "no customers", which cannot be received in the "sleeping" state, and would be a no-op if it was. This allows the action associated with the "cutting hair" state to be entirely handled in the lambda associated with the customer_waiting message; splitting the wait loops would require that the code was extracted out to a separate function, which would make it harder to keep count of the haircuts. Of course, if you don't have a compiler with lambda support then you'll need to do that anyway. The logger is a global actor that receives std::strings as messages and writes them to std::cout. This avoids any synchronization issues with multiple threads trying to write out at once, but it does mean that you have to pre-format the strings, such as when logging the number of haircuts done in the day. The code for this is shown in listing 2.

The customer actor

Let's look at things from the other side: the customer. The customer goes to town, and does some shopping. Each customer periodically goes into the barber shop to try and get a hair cut. If they manage, or the shop is closed, then they go home, otherwise they do some more shopping and go back to the barber shop later. This is shown in the state machine in figure 2.

Figure 2: Customer State Machine

Figure 2: Customer State Machine

This translates into the code in listing 3. Note that the customer interacts with a "shop" actor that I haven't mentioned yet. It is often convenient to have an actor that represents shared state, since this allows access to the shared state from other actors to be serialized without needing an explicit mutex. In this case, the shop holds the number of waiting customers, which must be shared with any customers that come in, so they know whether there is a free chair or not. Rather than have the barber have to deal with messages from new customers while he is cutting hair, the shop acts as an intermediary. The customer also has to handle the case that the shop has already closed, so the shop reference might refer to an actor that has finished executing, and thus get a jss::no_actor exception when trying to send messages.

The message handlers for the shop are short, and just send out further messages to the barber or the customer, which is ideal for a simple state-manager — you don't want other actors waiting to perform simple state checks because the state manager is performing a lengthy operation; this is why we separated the shop from the barber. The shop has 2 states: open, where new customers are accepted provided there are fewer than the remaining spaces, and closed, where new customers are turned away, and the shop is just waiting for the last customer to leave. If a customer comes in, and there is a free chair then a message is sent to the barber that there is a customer waiting; if there is no space then a message is sent back to the customer to say so. When it's closing time then we switch to the "closing" state — in the code we exit the first while loop and enter the second. This is all shown in listing 4.

The messages are shown in listing 5, and the main() function that drives it all is in listing 6.

Exit stage left

There are of course other ways of writing code to deal with any particular scenario, even if you stick to using actors. This article has shown some of the issues that you need to think about when using an actor-based approach, as well as demonstrating how it all fits together with the Just::Thread Pro actors library. Though the details will be different, the larger issues will be common to any implementation of the actor model.

Get the code

If you want to download the code for a better look, or to try it out, you can download it here.

Get your copy of Just:::Thread Pro

If you like the idea of working with actors in your code, now is the ideal time to get Just::Thread Pro. Get your copy now.

This blog post is based on an article that was printed in the July 2013 issue of CVu, the Journal of the ACCU.

Posted by Anthony Williams
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New Concurrency Features in C++14

Wednesday, 08 July 2015

It might have been out for 7 months already, but the C++14 standard is still pretty fresh. The changes include a couple of enhancements to the thread library, so I thought it was about time I wrote about them here.

Shared locking

The biggest of the changes is the addition of std::shared_timed_mutex. This is a multiple-reader, single-writer mutex. This means that in addition to the single-ownership mode supported by the other standard mutexes, you can also lock it in shared ownership mode, in which case multiple threads may hold a shared ownership lock at the same time.

This is commonly used for data structures that are read frequently but modified only rarely. When the data structure is stable, all threads that want to read the data structure are free to do so concurrently, but when the data structure is being modified then only the thread doing the modification is permitted to access the data structure.

The timed part of the name is analagous to std::timed_mutex and std::recursive_timed_mutex: timeouts can be specified for any attempt to acquire a lock, whether a shared-ownership lock or a single-ownership lock. There is a proposal to add a plain std::shared_mutex to the next standard, since this can have lower overhead on some platforms.

To manage the new shared ownership mode there are a new set of member functions: lock_shared(), try_lock_shared(), unlock_shared(), try_lock_shared_for() and try_lock_shared_until(). Obtaining and releasing the single-ownership lock is performed with the same set of operations as for std::timed_mutex.

However, it is generally considered bad practise to use these functions directly in C++, since that leaves the possibility of dangling locks if a code path doesn't release the lock correctly. It is better to use std::lock_guard and std::unique_lock to perform lock management in the single-ownership case, and the shared-ownership case is no different: the C++14 standard also provides a new std::shared_lock class template for managing shared ownership locks. It works just the same as std::unique_lock; for common use cases it acquires the lock in the constructor, and releases it in the destructor, but there are member functions to allow alternative access patterns.

Typical uses will thus look like the following:

std::shared_timed_mutex m;
my_data_structure data;

void reader(){
    std::shared_lock<std::shared_timed_mutex> lk(m);
    do_something_with(data);
}

void writer(){
    std::lock_guard<std::shared_timed_mutex> lk(m);
    update(data);
}

Performance warning

The implementation of a mutex that supports shared ownership is inherently more complex than a mutex that only supports exclusive ownership, and all the shared-ownership locks still need to modify the mutex internals. This means that the mutex itself can become a point of contention, and sap performance. Whether using a std::shared_timed_mutex instead of a std::mutex provides better or worse performance overall is therefore strongly dependent on the work load and access patterns.

As ever, I therefore strongly recommend profiling your application with std::mutex and std::shared_timed_mutex in order to ascertain which performs best for your use case.

std::chrono enhancements

The other concurrency enhancements in the C++14 standard are all in the <chrono> header. Though this isn't strictly about concurrency, it is used for all the timing-related functions in the concurrency library, and is therefore important for any threaded code that has timeouts.

constexpr all round

The first change is that the library has been given a hefty dose of constexpr goodness. Instances of std::chrono::duration and std::chrono::time_point now have constexpr constructors and simple arithmetic operations are also constexpr. This means you can now create durations and time points which are compile-time constants. It also means they are literal types, which is important for the other enhancement to <chrono>: user-defined literals for durations.

User-defined literals

C++11 introduced the idea of user-defined literals, so you could provide a suffix to numeric and string literals in your code to construct a user-defined type of object, much as 3.2f creates a float rather than the default double, however there were no new types of literals provided by the standard library.

C++14 changes that. We now have user-defined literals for std::chrono::duration, so you can write 30s instead of std::chrono::seconds(30). To get this user-defined literal goodness you need to explicitly enable it in your code with a using directive — you might have other code that wants to use these suffixes for a different set of types, so the standard let's you choose.

That using directive is:

using namespace std::literals::chrono_literals;

The supported suffixes are:

  • hstd::chrono::hours
  • minstd::chrono::minutes
  • sstd::chrono::seconds
  • msstd::chrono::milliseconds
  • usstd::chrono::microseconds
  • nsstd::chrono::nanoseconds

You can therefore wait for a shared ownership lock with a 50 millisecond timeout like this:

void foo(){
    using namespace std::literals::chrono_literals;
    std::shared_lock<std::shared_timed_mutex> lk(m,50ms);
    if(lk.owns_lock()){
        do_something_with_lock_held();
    }
    else {
        do_something_without_lock_held();
    }
}

Just::Thread support

As you might expect, Just::Thread provides an implementation of all of this. std::shared_timed_mutex is available on all supported platforms, but the constexpr and user-defined literal enhancements are only available for those compilers that support the new language features: gcc 4.6 or later for constexpr and gcc 4.7 or later for user-defined literals, with the -std=c++11 or std=c++14 switch enabled in either case.

Get your copy of Just::Thread while our 10th anniversary sale is on for a 50% discount.

Posted by Anthony Williams
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Locks, Mutexes, and Semaphores: Types of Synchronization Objects

Tuesday, 21 October 2014

I recently got an email asking about locks and different types of synchronization objects, so I'm posting this entry in case it is of use to others.

Locks

A lock is an abstract concept. The basic premise is that a lock protects access to some kind of shared resource. If you own a lock then you can access the protected shared resource. If you do not own the lock then you cannot access the shared resource.

To own a lock, you first need some kind of lockable object. You then acquire the lock from that object. The precise terminology may vary. For example, if you have a lockable object XYZ you may:

  • acquire the lock on XYZ,
  • take the lock on XYZ,
  • lock XYZ,
  • take ownership of XYZ,
  • or some similar term specific to the type of XYZ

The concept of a lock also implies some kind of exclusion: sometimes you might be unable to take ownership of a lock, and the operation to do so will either fail, or block. In the former case, the operation will return some error code or exception to indicate that the attempt to take ownership failed. In the latter case, the operation will not return until it has taken ownership, which typically requires that another thread in the system does something to permit that to happen.

The most common form of exclusion is a simple numerical count: the lockable object has a maximum number of owners. If that number has been reached, then any further attempt to acquire a lock on it will be unable to succeed. This therefore requires that we have some mechanism of relinquishing ownership when we are done. This is commonly called unlocking, but again the terminology may vary. For example, you may:

  • release the lock on XYZ,
  • drop the lock on XYZ,
  • unlock XYZ,
  • relinquish ownership of XYZ,
  • or some similar term specific to the type of XYZ

When you relinquish ownership in the appropriate fashion then a blocked operation that is trying to acquire the lock may not proceed, if the required conditions have been met.

For example if a lockable object only allows 3 owners then a 4th attempt to acquire the lock will block. When one of the first 3 owners releases the lock then that 4th attempt to acquire the lock will succeed.

Ownership

What it means to "own" a lock depends on the precise type of the lockable object. For some lockable objects there is a very tight definition of ownership: this specific thread owns the lock, through the use of that specific object, within this particular scope.

In other cases, the definition is more fluid, and the ownership of the lock is more conceptual. In these cases, ownership can be relinquished by a different thread or object than the thread or object that acquired the lock.

Mutexes

Mutex is short for MUTual EXclusion. Unless the word is qualified with additional terms such as shared mutex, recursive mutex or read/write mutex then it refers to a type of lockable object that can be owned by exactly one thread at a time. Only the thread that acquired the lock can release the lock on a mutex. When the mutex is locked, any attempt to acquire the lock will fail or block, even if that attempt is done by the same thread.

Recursive Mutexes

A recursive mutex is similar to a plain mutex, but one thread may own multiple locks on it at the same time. If a lock on a recursive mutex has been acquired by thread A, then thread A can acquire further locks on the recursive mutex without releasing the locks already held. However, thread B cannot acquire any locks on the recursive mutex until all the locks held by thread A have been released.

In most cases, a recursive mutex is undesirable, since the it makes it harder to reason correctly about the code. With a plain mutex, if you ensure that the invariants on the protected resource are valid before you release ownership then you know that when you acquire ownership those invariants will be valid.

With a recursive mutex this is not the case, since being able to acquire the lock does not mean that the lock was not already held, by the current thread, and therefore does not imply that the invariants are valid.

Reader/Writer Mutexes

Sometimes called shared mutexes, multiple-reader/single-writer mutexes or just read/write mutexes, these offer two distinct types of ownership:

  • shared ownership, also called read ownership, or a read lock, and
  • exclusive ownership, also called write ownership, or a write lock.

Exclusive ownership works just like ownership of a plain mutex: only one thread may hold an exclusive lock on the mutex, only that thread can release the lock. No other thread may hold any type of lock on the mutex whilst that thread holds its lock.

Shared ownership is more lax. Any number of threads may take shared ownership of a mutex at the same time. No thread may take an exclusive lock on the mutex while any thread holds a shared lock.

These mutexes are typically used for protecting shared data that is seldom updated, but cannot be safely updated if any thread is reading it. The reading threads thus take shared ownership while they are reading the data. When the data needs to be modified, the modifying thread first takes exclusive ownership of the mutex, thus ensuring that no other thread is reading it, then releases the exclusive lock after the modification has been done.

Spinlocks

A spinlock is a special type of mutex that does not use OS synchronization functions when a lock operation has to wait. Instead, it just keeps trying to update the mutex data structure to take the lock in a loop.

If the lock is not held very often, and/or is only held for very short periods, then this can be more efficient than calling heavyweight thread synchronization functions. However, if the processor has to loop too many times then it is just wasting time doing nothing, and the system would do better if the OS scheduled another thread with active work to do instead of the thread failing to acquire the spinlock.

Semaphores

A semaphore is a very relaxed type of lockable object. A given semaphore has a predefined maximum count, and a current count. You take ownership of a semaphore with a wait operation, also referred to as decrementing the semaphore, or even just abstractly called P. You release ownership with a signal operation, also referred to as incrementing the semaphore, a post operation, or abstractly called V. The single-letter operation names are from Dijkstra's original paper on semaphores.

Every time you wait on a semaphore, you decrease the current count. If the count was greater than zero then the decrement just happens, and the wait call returns. If the count was already zero then it cannot be decremented, so the wait call will block until another thread increases the count by signalling the semaphore.

Every time you signal a semaphore, you increase the current count. If the count was zero before you called signal, and there was a thread blocked in wait then that thread will be woken. If multiple threads were waiting, only one will be woken. If the count was already at its maximum value then the signal is typically ignored, although some semaphores may report an error.

Whereas mutex ownership is tied very tightly to a thread, and only the thread that acquired the lock on a mutex can release it, semaphore ownership is far more relaxed and ephemeral. Any thread can signal a semaphore, at any time, whether or not that thread has previously waited for the semaphore.

An analogy

A semaphore is like a public lending library with no late fees. They might have 5 copies of C++ Concurrency in Action available to borrow. The first five people that come to the library looking for a copy will get one, but the sixth person will either have to wait, or go away and come back later.

The library doesn't care who returns the books, since there are no late fees, but when they do get a copy returned, then it will be given to one of the people waiting for it. If no-one is waiting, the book will go on the shelf until someone does want a copy.

Binary semaphores and Mutexes

A binary semaphore is a semaphore with a maximum count of 1. You can use a binary semaphore as a mutex by requiring that a thread only signals the semaphore (to unlock the mutex) if it was the thread that last successfully waited on it (when it locked the mutex). However, this is only a convention; the semaphore itself doesn't care, and won't complain if the "wrong" thread signals the semaphore.

Critical Sections

In synchronization terms, a critical section is that block of code during which a lock is owned. It starts at the point that the lock is acquired, and ends at the point that the lock is released.

Windows CRITICAL_SECTIONs

Windows programmers may well be familiar with CRITICAL_SECTION objects. A CRITICAL_SECTION is a specific type of mutex, not a use of the general term critical section.

Mutexes in C++

The C++14 standard has five mutex types:

The variants with "timed" in the name are the same as those without, except that the lock operations can have time-outs specified, to limit the maximum wait time. If no time-out is specified (or possible) then the lock operations will block until the lock can be acquired — potentially forever if the thread that holds the lock never releases it.

std::mutex and std::timed_mutex are just plain single-owner mutexes.

std::recursive_mutex and std::recursive_timed_mutex are recursive mutexes, so multiple locks may be held by a single thread.

std::shared_timed_mutex is a read/write mutex.

C++ lock objects

To go with the various mutex types, the C++ Standard defines a triplet of class templates for objects that hold a lock. These are:

For basic operations, they all acquire the lock in the constructor, and release it in the destructor, though they can be used in more complex ways if desired.

std::lock_guard<> is the simplest type, and just holds a lock across a critical section in a single block:

std::mutex m;
void f(){
    std::lock_guard<std::mutex> guard(m);
    // do stuff
}

std::unique_lock<> is similar, except it can be returned from a function without releasing the lock, and can have the lock released before the destructor:

std::mutex m;
std::unique_lock<std::mutex> f(){
    std::unique_lock<std::mutex> guard(m);
    // do stuff
    return std::move(guard);
}

void g(){
    std::unique_lock<std::mutex> guard(f());
    // do more stuff
    guard.unlock();
}

See my previous blog post for more about std::unique_lock<> and std::lock_guard<>.

std::shared_lock<> is almost identical to std::unique_lock<> except that it acquires a shared lock on the mutex. If you are using a std::shared_timed_mutex then you can use std::lock_guard<std::shared_timed_mutex> or std::unique_lock<std::shared_timed_mutex> for the exclusive lock, and std::shared_lock<std::shared_timed_mutex> for the shared lock.

std::shared_timed_mutex m;
void reader(){
    std::shared_lock<std::shared_timed_mutex> guard(m);
    // do read-only stuff
}
void writer(){
    std::lock_guard<std::shared_timed_mutex> guard(m);
    // update shared data
}

Semaphores in C++

The C++ standard does not define a semaphore type. You can write your own with an atomic counter, a mutex and a condition variable if you need, but most uses of semaphores are better replaced with mutexes and/or condition variables anyway.

Unfortunately, for those cases where semaphores really are what you want, using a mutex and a condition variable adds overhead, and there is nothing in the C++ standard to help. Olivier Giroux and Carter Edwards' proposal for a std::synchronic class template (N4195) might allow for an efficient implementation of a semaphore, but this is still just a proposal.

Posted by Anthony Williams
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C++ Concurrency in Action and Just::Thread Discounts

Wednesday, 25 April 2012

C++ Concurrency in Action cover

My book C++ Concurrency in Action was finally published on 29th February 2012, after 4 years of work. It's hard to believe that I can actually hold a copy in my hand; it's just been files on my computer for so long.

My book is a tutorial and reference to the thread library from the new C++11 standard. It also provides various guidelines for writing and testing multithreaded code, as well as sample implementations of thread-safe data structures and parallel algorithms

If you haven't already got a copy, you can order one direct from Manning, or from amazon.com, or amazon.co.uk. Alternatively, copies should be available at the ACCU 2012 conference in Oxford this week.

Discount on Just::Thread

If you have purchased the book then send a copy of your receipt or other proof of purchase to info@justsoftwaresolutions.co.uk for a 50% discount on a single user license of Just::Thread, our implementation of the C++11 thread library described in the book for Microsoft Visual Studio on Windows, and g++ on Windows, Linux and MacOSX.

Posted by Anthony Williams
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Thread-Safe Copy and Move Constructors

Wednesday, 17 August 2011

This is a guest post by Michael Spertus. Michael is a Distinguished Engineer at Symantec. He is also a C++ Standards Committee member and teaches the graduate C++ sequence at the University of Chicago. He can be contacted at mike_spertus@symantec.com.


This guest column discusses writing thread-safe constructors. As we will see, this is more difficult than it seems. Fortunately, we will also see that C++11 offers a very pretty solution to this problem that nicely illustrates the synergy of the new features introduced in C++11.

The problem

If you have a class that supports locking of objects to serialize access to a given object, you probably want the class' copy constructor and move constructor (if it has one) to lock the source object to get a consistent snapshot of the source object so the destination object isn't messed up if the source changes in the middle of the copy or move.

This isn't nearly as easy as it sounds. In the following class, a mutex is used to try to enforce the invariant that i_squared should always be the square of i.

class A {
public:  A(_i = 0) { set(_i); }
  set(int _i) {
    std::lock_guard<std::mutex> lock(mtx);
    i = _i;
    i_squared = i*i;
  }
  ...
private:
  std::mutex mtx;
  int i;
  int i_squared;
};

Unfortunately, the default copy constructor doesn't acquire the mutex, so in code like the following, f can copy a "half set" version of a if another thread modifies a at the same time.

void f(A &a) 
{
  A a2 = a;
  ...
}

First attempt

A naive attempt is to acquire the lock in the constructor body just like in a thread-safe method.

class A {
public:
  A(const A &a) : i(a.i), i_squared(a.i_squared) {
    std::lock_guard<std::mutex> lock(a.mtx); // Too late!
  }
  ...
};

Unfortunately, this fares no better as i and i_squared are copied before we acquire the lock.

Second attempt

One approach would be to simply not lock in the copy constructor at all and just manually lock objects you want to copy:

void f(A &a)
{
  std::lock_guard<std::mutex>
  lock(a.mtx);
  A a2 = a;
  ...
}

This approach deserves careful consideration. For classes which are not usually shared between threads or which need locking granularity at a different level than their internal operations, managing locks within the class can be an antipattern. This concern was a primary reason why C++11 does not have an equivalent to the SynchronizedCollection wrapper found in Java and C#. For example, synchronized collections make it easy to inadvertently loop through a collection believing your code is thread-safe even though the collection could change between individual operations on the collection during the loop. Of course, if we decide not to have A's copy constructor lock, then A::set() should not lock either.

Still, it remains a very common and useful pattern for classes designed for shared use to have all their internal operations acquire the lock (i.e., monitors/synchronized classes). If A is a synchronized class that locks its methods internally, it would be very confusing and prone to intermittent errors to still have to manually acquire the lock whenever an object is copied or moved. Also, generic code, which doesn't know about A::mtx is unlikely to work properly.

Third attempt

One thing we can do is dispense with member initialization lists in constructors altogether

class A {
public:
  A(const A &a) {
    std::lock_guard<std::mutex> lock(a.mtx);
    i = a.i;
    i_squared = a.i_squared;
  }
  ...
};

This solution is awkward at best if any bases or members don't have default constructors, have reference type, or are const. It also seems unfair to have to pay an efficiency penalty (for constructing and assigning separately) just because there is no place to put the lock. In practice, I also suspect intermittent errors will creep into large code bases as programmers carelessly add a base or member initializer to the constructor. Finally, it just isn't very satisfying to have to just discard core parts of constructor syntax just because your class is synchronized.

Fourth attempt

Anthony Williams has suggested implementing serialized classes using a wrapper class like:

struct PrivateBaseForA
{
     int i;
     int i_squared;
};

class A: private PrivateBaseForA
{
   mutable std::mutex mtx;
public:
   A(int _i = 0) { set(_i); }
   void set(int _i) {
     std::lock_guard<std::mutex> lock(mtx);
     i = _i;
     i_squared = _i*_i;
   }
   A(const A& other):
     PrivateBaseForA((std::lock_guard<std::mutex>(other.mtx),other))
   {}
};

Anthony makes slick use of double-parens to use the comma operator. If you wanted to avoid this, you could have PrivateBaseForA's constructor take a lock.

Again, this is not yet a very satisfying solution because writing a wrapper class for every synchronized class just to get a properly locked copy constructor is clumsy and intrusive.

Finally, the C++11 approach

Fortunately, C++11 offers a nice solution that really illustrates how beautifully and powerfully the features of C++11 work together. C++11 supports forwarding one constructor to another, which gives us an opportunity to grab the mutex before any copying or moving takes place:

class A {
private:
  A(const A &a, const std::lock_guard<std::mutex> &)
   : i(a.i), i_squared(a.i_squared) {}
public:
  A(const A &a) : A(a, std::lock_guard<std::mutex>(a.mtx)) {}
  ...
};

This solution locks the entire constructor, protects against races resulting from forgetting the manual lock, works with generic code, and doesn't require the creation of artificial wrapper classes.

While I think this is clearly the preferred solution, it is not perfect. To begin with, it is more obscure and guru-like than I would wish for such a common situation. Secondly, constructor forwarding is not yet implemented by any major C++ compiler (although the recently approved standard should change that soon). Finally, we lose the benefit of compiler generated copy constructors. If we added an i_cubed field to A that also needed to be kept consistent with i, we might forget to update the private constructor. Perhaps this will be a further opportunity for the next C++ standard (C++1y?). In the meantime, C++11 provides a powerful new solution to the everyday problem of writing thread-safe copy and move constructors.

One final note is to mention that although this article focused on copy constructors, everything applies equally to move constructors. Indeed, any constructor that needs to acquire any lock whatsoever (e.g., a database lock) for its entire duration can apply these techniques.

Posted by Anthony Williams
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Simplify Code by Encapsulating Locks

Wednesday, 15 June 2011

Over on the Future Chips blog, Aater Suleman argues that while(1) can make parallel code better. Whilst I agree that the code using while(1) is simpler than the original in terms of analysing the lock patterns, it achieves this by testing the logical condition inside the while and using break. This is additional, unnecessary, complexity.

What is wanted instead is a way of encapsulating the locking, so that the loop logic is simple, and yet the lock logic is also clear.

Here is the original code from the blog post:

lock_acquire(lock);
while(check_condition()){
  lock_release(lock);
  //do any actual work in the iteration - Thanks to Caleb for this comment
  lock_acquire(lock);
}

lock_release(lock);

The implication here is that check_condition() must be called with the lock held, but the lock need not be held for the actual iteration work. The code thus acquires and releases the mutex in two places, which is unnecessary duplication, and a potential source of errors — if the loop exits early then the lock may be released twice, for example.

Rather than moving the condition check into the loop to avoid this duplication, a better solution is to move the lock acquisition and release into the condition check:

bool atomic_check_condition()
{
  lock_acquire(lock);
  bool result=check_condition();
  lock_release(lock);
  return result;
}

while(atomic_check_condition()){
  //do any actual work in the iteration - Thanks to Caleb for this comment
}

This gives us the best of both worlds: the lock is now held only across the check_condition() call, but the logic of the while loop is still clear.

If you're programming in C++, then the C++0x library allows us to make atomic_check_condition() even simpler by using lock_guard as in the code below, but extracting the function is always an improvement.

bool atomic_check_condition()
{
  std::lock_guard<mutex_type> guard(lock);
  return check_condition();
}

Posted by Anthony Williams
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Definitions of Non-blocking, Lock-free and Wait-free

Tuesday, 07 September 2010

There have repeatedly been posts on comp.programming.threads asking for a definition of these terms. To write good multithreaded code you really need to understand what these mean, and how they affect the behaviour and performance of algorithms with these properties. I thought it would be therefore be useful to provide some definitions.

Definition of Blocking

A function is said to be blocking if it calls an operating system function that waits for an event to occur or a time period to elapse. Whilst a blocking call is waiting the operating system can often remove that thread from the scheduler, so it takes no CPU time until the event has occurred or the time has elapsed. Once the event has occurred then the thread is placed back in the scheduler and can run when allocated a time slice. A thread that is running a blocking call is said to be blocked.

Mutex lock functions such as std::mutex::lock(), and EnterCriticalSection() are blocking, as are wait functions such as std::future::wait() and std::condition_variable::wait(). However, blocking functions are not limited to synchronization facilities: the most common blocking functions are I/O facilities such as fread() or WriteFile(). Timing facilities such as Sleep(), or std::this_thread::sleep_until() are also often blocking if the delay period is long enough.

Definition of Non-blocking

Non-blocking functions are just those that aren't blocking. Non-blocking data structures are those on which all operations are non-blocking. All lock-free data structures are inherently non-blocking.

Spin-locks are an example of non-blocking synchronization: if one thread has a lock then waiting threads are not suspended, but must instead loop until the thread that holds the lock has released it. Spin locks and other algorithms with busy-wait loops are not lock-free, because if one thread (the one holding the lock) is suspended then no thread can make progress.

Defintion of lock-free

A lock-free data structure is one that doesn't use any mutex locks. The implication is that multiple threads can access the data structure concurrently without race conditions or data corruption, even though there are no locks — people would give you funny looks if you suggested that std::list was a lock-free data structure, even though it is unlikely that there are any locks used in the implementation.

Just because more than one thread can safely access a lock-free data structure concurrently doesn't mean that there are no restrictions on such accesses. For example, a lock-free queue might allow one thread to add values to the back whilst another removes them from the front, whereas multiple threads adding new values concurrently would potentially corrupt the data structure. The data structure description will identify which combinations of operations can safely be called concurrently.

For a data structure to qualify as lock-free, if any thread performing an operation on the data structure is suspended at any point during that operation then the other threads accessing the data structure must still be able to complete their tasks. This is the fundamental restriction which distinguishes it from non-blocking data structures that use spin-locks or other busy-wait mechanisms.

Just because a data structure is lock-free it doesn't mean that threads don't have to wait for each other. If an operation takes more than one step then a thread may be pre-empted by the OS part-way through an operation. When it resumes the state may have changed, and the thread may have to restart the operation.

In some cases, a the partially-completed operation would prevent other threads performing their desired operations on the data structure until the operation is complete. In order for the algorithm to be lock-free, these threads must then either abort or complete the partially-completed operation of the suspended thread. When the suspended thread is woken by the scheduler it can then either retry or accept the completion of its operation as appropriate. In lock-free algorithms, a thread may find that it has to retry its operation an unbounded number of times when there is high contention.

If you use a lock-free data structure where multiple threads modify the same pieces of data and thus cause each other to retry then high rates of access from multiple threads can seriously cripple the performance, as the threads hinder each other's progress. This is why wait-free data structures are so important: they don't suffer from the same set-backs.

Definition of wait-free

A wait-free data structure is a lock-free data structure with the additional property that every thread accessing the data structure can make complete its operation within a bounded number of steps, regardless of the behaviour of other threads. Algorithms that can involve an unbounded number of retries due to clashes with other threads are thus not wait-free.

This property means that high-priority threads accessing the data structure never have to wait for low-priority threads to complete their operations on the data structure, and every thread will always be able to make progress when it is scheduled to run by the OS. For real-time or semi-real-time systems this can be an essential property, as the indefinite wait-periods of blocking or non-wait-free lock-free data structures do not allow their use within time-limited operations.

The downside of wait-free data structures is that they are more complex than their non-wait-free counterparts. This imposes an overhead on each operation, potentially making the average time taken to perform an operation considerably longer than the same operation on an equivalent non-wait-free data structure.

Choices

When choosing a data structure for a given task you need to think about the costs and benefits of each of the options.

A lock-based data structure is probably the easiest to use, reason about and write, but has the potential for limited concurrency. They may also be the fastest in low-load scenarios.

A lock-free (but not wait-free) data structure has the potential to allow more concurrent accesses, but with the possibility of busy-waits under high loads. Lock-free data structures are considerably harder to write, and the additional concurrency can make reasoning about the program behaviour harder. They may be faster than lock-based data structures, but not necessarily.

Finally, a wait-free data structure has the maximum potential for true concurrent access, without the possibility of busy waits. However, these are very much harder to write than other lock-free data structures, and typically impose an additional performance cost on every access.

Posted by Anthony Williams
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Implementing Dekker's algorithm with Fences

Tuesday, 27 July 2010

Dekker's algorithm is one of the most basic algorithms for mutual exclusion, alongside Peterson's algorithm and Lamport's bakery algorithm. It has the nice property that it only requires load and store operations rather than exchange or test-and-set, but it still requires some level of ordering between the operations. On a weakly-ordered architecture such as PowerPC or SPARC, a correct implementation of Dekker's algorithm thus requires the use of fences or memory barriers in order to ensure correct operation.

The code

For those of you who just want the code: here it is — Dekker's algorithm in C++, with explicit fences.

std::atomic<bool> flag0(false),flag1(false);
std::atomic<int> turn(0);

void p0()
{
    flag0.store(true,std::memory_order_relaxed);
    std::atomic_thread_fence(std::memory_order_seq_cst);

    while (flag1.load(std::memory_order_relaxed))
    {
        if (turn.load(std::memory_order_relaxed) != 0)
        {
            flag0.store(false,std::memory_order_relaxed);
            while (turn.load(std::memory_order_relaxed) != 0)
            {
            }
            flag0.store(true,std::memory_order_relaxed);
            std::atomic_thread_fence(std::memory_order_seq_cst);
        }
    }
    std::atomic_thread_fence(std::memory_order_acquire);
 
    // critical section


    turn.store(1,std::memory_order_relaxed);
    std::atomic_thread_fence(std::memory_order_release);
    flag0.store(false,std::memory_order_relaxed);
}

void p1()
{
    flag1.store(true,std::memory_order_relaxed);
    std::atomic_thread_fence(std::memory_order_seq_cst);

    while (flag0.load(std::memory_order_relaxed))
    {
        if (turn.load(std::memory_order_relaxed) != 1)
        {
            flag1.store(false,std::memory_order_relaxed);
            while (turn.load(std::memory_order_relaxed) != 1)
            {
            }
            flag1.store(true,std::memory_order_relaxed);
            std::atomic_thread_fence(std::memory_order_seq_cst);
        }
    }
    std::atomic_thread_fence(std::memory_order_acquire);
 
    // critical section


    turn.store(0,std::memory_order_relaxed);
    std::atomic_thread_fence(std::memory_order_release);
    flag1.store(false,std::memory_order_relaxed);
}

The analysis

If you're like me then you'll be interested in why stuff works, rather than just taking the code. Here is my analysis of the required orderings, and how the fences guarantee those orderings.

Suppose thread 0 and thread 1 enter p0 and p1 respectively at the same time. They both set their respective flags to true, execute the fence and then read the other flag at the start of the while loop.

If both threads read false then both will enter the critical section, and the algorithm doesn't work. It is the job of the fences to ensure that this doesn't happen.

The fences are marked with memory_order_seq_cst, so either the fence in p0 is before the fence in p1 in the global ordering of memory_order_seq_cst operations, or vice-versa. Without loss of generality, we can assume that the fence in p0 comes before the fence in p1, since the code is symmetric. The store to flag0 is sequenced before the fence in p0, and the fence in p1 is sequenced before the read from flag0. Therefore the read from flag0 must see the value stored (true), so p1 will enter the while loop.

On the other side, there is no such guarantee for the read from flag1 in p0, so p0 may or may not enter the while loop. If p0 reads the value of false for flag1, it will not enter the while loop, and will instead enter the critical section, but that is OK since p1 has entered the while loop.

Though flag0 is not set to false until p0 has finished the critical section, we need to ensure that p1 does not see this until the values modified in the critical section are also visible to p1, and this is the purpose of the release fence prior to the store to flag0 and the acquire fence after the while loop. When p1 reads the value false from flag0 in order to exit the while loop, it must be reading the value store by p0 after the release fence at the end of the critical section. The acquire fence after the load guarantees that all the values written before the release fence prior to the store are visible, which is exactly what we need here.

If p0 reads true for flag1, it will enter the while loop rather than the critical section. Both threads are now looping, so we need a way to ensure that exactly one of them breaks out. This is the purpose of the turn variable. Initially, turn is 0, so p1 will enter the if and set flag1 to false, whilst p1 will not enter the if. Because p1 set flag1 to false, eventually p0 will read flag1 as false and exit the outer while loop to enter the critical section. On the other hand, p1 is now stuck in the inner while loop because turn is 0. When p0 exits the critical section it sets turn to 1. This will eventually be seen by p1, allowing it to exit the inner while loop. When the store to flag0 becomes visible p1 can then exit the outer while loop too.

If turn had been 1 initially (because p0 was the last thread to enter the critical section) then the inverse logic would apply, and p0 would enter the inner loop, allowing p1 to enter the critical section first.

Second time around

If p0 is called a second time whilst p1 is still in the inner loop then we have a similar situation to the start of the function — p1 may exit the inner loop and store true in flag1 whilst p0 stores true in flag0. We therefore need the second memory_order_seq_cst fence after the store to the flag in the inner loop. This guarantees that at least one of the threads will see the flag from the other thread set when it executes the check in the outer loop. Without this fence then both threads can read false, and both can enter the critical section.

Alternatives

You could put the ordering constraints on the loads and stores themselves rather than using fences. Indeed, the default memory ordering for atomic operations in C++ is memory_order_seq_cst, so the algorithm would "just work" with plain loads and stores to atomic variables. However, by using memory_order_relaxed on the loads and stores we can add fences to ensure we have exactly the ordering constraints required.

Posted by Anthony Williams
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