Concurrency



I’ve blogged before about the C++ unique_ptr not being unique and how true uniqueness can be implemented in an ownership-based type system. But I’ve been just scratching the surface.

The new push toward uniqueness is largely motivated by the demands of multithreaded programming. Unique objects are alias free and, in particular, cannot be accessed from more than one thread at a time. Because of that, they never require locking. They can also be safely passed between threads without the need for deep copying. In other words, they are a perfect vehicle for safe and efficient message passing. But there’s a rub…

The problem is this: How do you create and modify unique objects that have internal pointers. A classic example is a doubly linked list. Consider this Java code:

public class Node {
    public Node _next;
    public Node _prev;
}
public class LinkedList {
    private Node _head;
    public void insert(Node n) {
        n._next = _head;
        if (_head != null)
            _head._prev = n;
        _head = n;
    }
}

Suppose that you have a unique instance of an empty LinkedList and you want to insert a new link into it without compromising its uniqueness.

The first danger is that there might be external aliases to the node you are inserting–the node is not unique, it is shared. In that case, after the node is absorbed:

_head = n;

_head would be pointing to an alias-contaminated object. The list would “catch” aliases and that would break the uniqueness property.

The remedy is to require that the inserted node be unique too, and the ownership of it be transferred from the caller to the insert method. (Notice however that, in the process of being inserted, the node loses its uniqueness, since there are potentially two aliases pointing to it from inside the list–one is _head and the other is _head._prev. Objects inside the list don’t have to be unique–they may be cross-linked.)

The second danger is that the method insert might “leak” aliases. The tricky part is when we let the external node, n, store the reference to our internal _head:

n._next = _head

We know that this is safe here because the node started unique and it will be absorbed into the list, so this alias will become an internal alias, which is okay. But how do we convince the compiler to certify this code as safe and reject code that isn’t? Type system to the rescue!

Types for Uniqueness

There have been several approaches to uniqueness using the type system. To my knowledge, the most compact and comprehensive one was presented by Haller and Odersky in the paper, Capabilities for External Uniqueness, which I will discuss in this post. The authors not only presented the theory but also implemented the prototype of the system as an extension of Scala. Since not many people are fluent in Scala, I’ll translate their examples into pseudo-Java, hopefully not missing much.

Both in Scala and Java one can use annotations to extend the type system. Uniqueness introduces three such annotations, @unique, @transient, and @exposed; and two additional keywords, expose and localize.

-Objects that are @unique

In the first approximation you may think of a @unique object as a leak-proof version of C++ unique_ptr. Such object is guaranteed to be “tracked” by at most one reference–no aliases are allowed. Also no external references are allowed to point to the object’s internals and, conversely, object internals may not reference any external objects. However, and this is a very important point, the insides of the @unique object may freely alias each other. Such a closed cross-linked mess is called a cluster.

Consider, for instance, a (non-empty) @unique linked list. It’s cluster consists of cross-linked set of nodes. It’s relatively easy for the compiler to guarantee that no external aliases are created to a @unique list–the tricky part is to allow the manipulation of list internals without breaking its uniqueness (Fig 1 shows our starting point).

Fig 1. The linked list and the node form separate clusters

Look at the definition of insert. Without additional annotations we would be able to call it with a node that is shared between several external aliases. After the node is included in the list, those aliases would be pointing to the internals of the list thus breaking its uniqueness. Because of that, the uniqueness-savvy compiler will flag a call to such un-annotated insert on a @unique list as an error. So how can we annotate insert so that it guarantees the preservation of uniqueness?

-Exposing and Localizing

Here’s the modified definition of insert:

public void insert(@unique Node n) @transient {
    expose (this) { list =>
        var node = localize (n, list);
        node._next = list._head;
        if (list._head != null)
            list._head._prev = node;
        list._head = node;
    }
}

Don’t worry, most of the added code can be inferred by the compiler, but I make it explicit here for the sake of presentation. Let’s go over some of the details.

The node, n passed to insert is declared as @unique. This guarantees that it forms its own cluster and that n is the only reference to it. Also, @unique parameters to a method are consumed by that method. The caller thus loses her reference to it (the compiler invalidates it), as demonstrated in this example:

@unique LinkedList lst = new @unique LinkedList();
@unique Node nd = new @unique Node();
lst.insert(nd);
nd._next; // error: nd has been consumed!

The method itself is annotated as @transient. It means that the this object is @unique, but not consumed by the method. In general, the @transient annotation may be applied to any parameter, not only this. You might be familiar with a different name for transient–borrowed.

Inside insert, the this parameter is explicitly exposed (actually, since the method is @transient, the compiler would expose this implicitly).

expose (this) { list => ... }

The new name for the exposed this is list.

Once a cluster is exposed, some re-linking of its constituents is allowed. The trick is not to allow any re-linking that would lead to the leakage of aliases. And here’s the trick: To guarantee no leakage, the compiler assigns the exposed object a special type–its original type tagged by a unique identifier. This identifier is created for the scope of each expose statement. All members of the exposed cluster are also tagged by the same tag. Since the compiler type-checks every assignment it automatically makes sure that both sides have the same tag.

Now we need one more ingredient–bringing the @unique node into the cluster. This is done by localizing the parameter n to the same cluster as list.

var node = localize (n, list);

The localize statement does two things. It consumes n and it returns a reference to it that is tagged by the same tag as its second parameter. From that point on, node has the same tagged type as all the exposed nodes inside the list, and all assignments type-check.

Exposed list and localized node

Fig 2. The list has been exposed: all references are tagged. The node has been localized (given the same tag as the list). Re-linking is now possible without violating the type system.

Note that, in my pseudo-Java, I didn’t specify the type of node returned by localize. That’s because tagged types are never explicitly mentioned in the program. They are the realm of the compiler.

Functional Decomposition

The last example was somewhat trivial in that the code that worked on exposed objects fit neatly into one method. But a viable type system cannot impose restrictions on structuring the code. The basic requirement for any programming language is to allow functional decomposition–delegating work to separate subroutines, which can be re-used in other contexts. That’s why we have to be able to define functions that operate on exposed and/or localized objects.

Here’s an example from Haller/Odersky that uses recursion within the expose statement. append is a method of a singly-linked list:

void append(@unique SinglyLinkedList other) @transient
{
    expose(this) { list =>
        if (list._next == null)
            list._next = other; // localize and consume
        else
            list._next.append(other);
    }
}

In the first branch of the if statement, a @unique parameter, other, is (implicitly) localized and consumed. In the second branch, it is recursively passed to append. Notice an important detail, the subject of append, list._next, is not @unique–it is exposed. Its type has been tagged by a unique tag. But the append method is declared as @transient. It turns out that both unique and exposed arguments may be safely accepted as transient parameters (including the implicit this parameter).

Because of this rule, it’s perfectly safe to forgo the explicit expose inside a transient method. The append method may be thus simplified to:

void append(@unique SinglyLinkedList other) @transient
{
    // 'this' is implicitly exposed
    if (_next == null)
        _next = other; // localize and consume
    else
        _next.append(other);
}

Things get a little more interesting when you try to reuse append inside another method. Consider the implementation of insert:

void insert(@unique SingleLinkedList other) @transient
{
    var locOther = localize(other, this);
    if (other != null) 
    {
        locOther.append(_next)
        _next = locOther;
   }
}

The insert method is transient–it works on unique or exposed lists. It accepts a unique list, other, which is consumed by the localize statement. The this reference is implicitly exposed with the same tag as locOther, so the last statement _next=locOther type-checks. The only thing that doesn’t type-check is the argument to append, which is supposed to be unique, but here it’s exposed instead.

This time there is no safe conversion to help us, so if we want to be able to reuse append, we have to modify its definition. First of all, we’ll mark its parameter as @exposed. An exposed parameter is tagged by the caller. In order for append to work, the this reference must also be tagged by the caller–with the same tag. Otherwise the assignment, _next=other, inside append, wouldn’t type-check. It follows that the append method must also be marked as @exposed (when there is more than one exposed parameter, they all have to be tagged with the same tag).

Here’s the new version of append:

void append(@exposed SinglyLinkedList other) @exposed
{
    if (_next == null)
        _next = other; // both exposed under the same tag
    else
        _next.append(other); // both exposed under the same tag
}

Something interesting happened to append. Since it now operates on exposed objects, it’s the caller’s responsibility to expose and localize unique object (this is exactly what we did in insert). Interestingly, append will now also operate on non-annotated types. You may, for instance, append one non-unique list to another non-unique list and it will type-check! That’s because non-annotated types are equivalent to exposed types with a null tag–they form a global cluster of their own.

This kind of polymorphism (non-annotated/annotated) means that in many cases you don’t have to define separate classes for use with unique objects. What Haller and Odersky found out is that almost all class methods in the Scala’s collection library required only the simplest @exposed annotations without changing their implementation. That’s why they proposed to use the @exposed annotation on whole classes.

Conclusion

Every time I read a paper about Scala I’m impressed. It’s a language that has very solid theoretical foundations and yet is very practical–on a par with Java, whose runtime it uses. I like Scala’s approach towards concurrency, with strong emphasis on safe and flexible message passing. Like functional languages, Scala supports immutable messages. With the addition of uniqueness, it will also support safe mutable messages. Neither kind requires synchronization (outside of that provided by the message queue), or deep copying.

There still is a gap in the Scala’s concurrency model–it’s possible to share objects between threads without any protection. It’s up to the programmer to declare shared methods as synchronized–just like in Java; but there is no overall guarantee of data-race freedom. So far, only ownership systems were able to deliver that guarantee, but I wouldn’t be surprised if Martin Odersky had something else up his sleeve for Scala.

I’d like to thank Philip Haller for reading the draft of this post and providing valuable comments. Philip told me that a new version of the prototype is in works, which will simplify the system further, both for the programmer and the implementer.


Here’s the video from my recent talk to the Northwest C++ Users Group (NWCPP) about how to translate the data-race free type system into a system of user-defined annotations in C++. I start with the definition of a data race and discuss various ways to eliminate them. Then I describe the ownership system and give a few examples of annotated programs.

Here are the slides from the presentation. They include extensive notes.

By the way, we are looking for speakers at the NWCPP, not necessarily related to C++. We are thinking of changing the charter to include all programming languages. If you are near Seattle on Oct 21 09 (or any third Wednesday of the month), and you are ready to give a 60 min presentation, please contact me.


What is there to reference counting that is not obvious? In any language that supports deterministic destruction and the overloading of the copy constructor and the assignment operator it should be trivial. Or so I though until I decided to implement a simple ref-counted thread handle in D. Two problems popped up:

  1. How does reference counting interact with garbage collection?
  2. How to avoid data races in a multithreaded environment?

In purely garbage-collected languages, like Java, you don’t implement reference counting, period. Which is pretty bad, if you ask me. GC is great at managing memory, but not so good at managing other resources. When a program runs out of memory, it forces a collection and reclaims unused memory. When it runs out of, say, system thread handles, it doesn’t reclaim unused handles–it just dies. You can’t use GC to manage system handles. So, as far as system resources go, Java forces the programmer to use the moral equivalent of C’s malloc and free. The programmer must free the resources explicitly.

In C++ you have std::shared_ptr for all your reference-counting needs, but you don’t have garbage collection for memory–at least not yet. (There is also the Microsoft’s C++/CLI which mixes the two systems.)

D offers the best of both worlds: GC and deterministic destruction. So let’s use GC to manage memory and reference counting (or other policies, like uniqueness) to manage other limited resources.

First attempt

The key to reference counting is to have a “value” type, like the shared_ptr in C++, that can be cloned and passed between functions at will. Internally this value type must have access to a shared chunk of memory that contains the reference count. In shared_ptr this chunk is a separately allocated integral type–the counter. The important thing is that all clones of the same resource share the same counter. The counter’s value reflects how many clones there are. Copy constructors and assignment operators take care of keeping the count exact. When the count goes to zero, that is the last copy of the resource goes out of scope, the resource is automatically freed (for instance, by calling CloseHandle).

In my first attempt, I decided that the memory allocated for the counter should be garbage-collected. After all, the important thing is to release the handle–the memory will take care of itself.

struct RcHandle {
   shared Counter _counter; // GC'd shared Counter object
   HANDLE _h;
   ~this() { // destructor
      if (_counter.dec() == 0) // access the counter
         CloseHandle(_h);
   }
   // methods that keep the counter up to date
}

RcHandle is a struct, which is a value type in D. Counter is a class, which is a reference type; so _counter really hides a pointer to shared memory.

A few tests later I got a nasty surprise. My program faulted while trying to access an already deallocated counter. How did that happen? How could garbage collector deallocate my counter if I still had a reference to it?

Here’s what I did in my test: I embedded the ref-counted handle inside another garbage-collected object:

class Embedder { // GC'd class object
   RcHandle _rc;
}

When the time came to collect that object (which happened after the program ran to completion), its finalizer was called. Whenever an object contains fields that have non-trivial destructors, the compiler generates a finalizer that calls the destructors of those embedded objects–_rc in this case. The destructor of the ref-counted handle checks the reference count stored in the counter. Unfortunately the counter didn’t exist anymore. Hours of debugging later I had the answer.

What happened is that the garbage collector had two objects on its list: the embedder and the counter. It just so happened that the collector decided to collect those two objects in reverse order: the counter first, then the embedding object. So, by the time it got to the finalizer of the embedding object, the counter was gone!

What I discovered (with the help of other members of the D team who were involved in the discussion) was that there are some limitations on mixing garbage collection with deterministic destruction. There is a general rule:

An object’s destructor must not access any garbage-collected objects embedded in it.

Since the destructor of the ref-counted handle must have access to the counter, the counter must not be garbage-collectible. That means only one thing: it has to be allocated using malloc and explicitly deallocated using free. Which brings us to the second problem.

Concurrency

What can be simpler than an atomic reference count? On most processors you can atomically increment and decrement a memory location. You can even decrement and test the value in one uninterruptible operation. Problem solved! Or is it?

There is one tiny complication–the location that you are atomically modifying might disappear. I know, this is totally counter-intuitive. After all the management of the counter follows the simple rule: the last to leave the room turns off the light. If the destructor of RcHandle sees the reference count going from one to zero, it knows that no other RcHandle has access it, and it can safely free the counter. Who can argue with cold logic?

Here’s the troubling scenario: RcHandle is embedded in an object that is visible from two threads:

class Embedder {
   RcHandle _rcH;
}
shared Embedder emb;

Thread 1 tries to overwrite the handle:

RcHandle myHandle1;
emb._rcH = myHandle1;

while Thread 2 tries to copy the same handle to a local variable:

RcHandle myHandle2 = emb._rcH;

Consider the following interleaving:

  1. T2: Load the address of the _counter embedded in _rcH.
  2. T1: Swap emb._rcH with myHandle
  3. T1: Decrement the counter that was embedded in _rcH. If it’s zero (and it is, in this example), free the counter.
  4. T2: Increment the _counter. Oops! This memory location has just been freed.

The snag is that there is a window between T2 reading the pointer in (1), and incrementing the location it’s pointing to in (4). Within that window, the reference count does not match the actual number of clients having access to the pointer. If T1 happens to do its ugly deed of freeing the counter within that window, the race may turn out deadly. (This problem has been known for some time and there were various proposals to fix it, for instance using DCAS, as in this paper on Lock-Free Reference Counting.)

Should we worry? After all the C++ shared_ptr also exposes this race and nobody is crying havoc. It turns out that it all boils down to the responsibilities of the shared object (and I’m grateful to Andrei for pointing it out).

A shared object should not willy-nilly expose its implementation to clients

If the clients of Embedder want access to the handle, they should call a synchronized method. Here’s the correct, race-free implementation of the Embedder in the scenario I just described.

class Embedder {
private:
   RcHandle _rcH;
public:
   synchronized RcHandle GetHandle() const { return _rcH; }
   synchronized void SetHandle(RcHandle h) { _rcH = h; }
   ...
}

The method GetHandle copies _rcH and increments its count under the Embedder‘s lock. Another thread calling SetHandle has no chance of interleaving with that action, because it is forced to use the same lock. D2 actually enforces this kind of protection for shared objects, so my original example wouldn’t even compile.

You might be thinking right now that all this is baloney because I’m trying to fit a square peg into a round hole. I’m imposing value semantics on a non-atomic object, and you cannot atomically overwrite a non-atomic object. However, using this logic, you could convince yourself that a double cannot have value semantics (on most common architectures doubles are too large to be atomic). And yet you can safely pass doubles between threads and assign them to each other (which is the same as overwriting). It’s only when you embed a double inside a shared object, you have to protect it from concurrent access. And it’s not the double protecting itself–it’s the shared embedder that is responsible for synchronization. It’s exactly the same with RcHandle.

Conclusions

Just when you thought you knew everything about reference counting you discover something you haven’t though about. The take-home message is that mixing garbage collection with deterministic destruction is not a trivial matter, and that a race-free reference-counted object is vulnerable to races when embedded it in another shared object. Something to keep in mind when programming in D or in C++.


Is the Actor model just another name for message passing between threads? In other words, can you consider a Java Thread object with a message queue an Actor? Or is there more to the Actor model? Bartosz investigates.

I’ll start with listing various properties that define the Actor Model. I will discuss implementation options in several languages.

Concurrency

Actors are objects that execute concurrently. Well, sort of. Erlang, for instance, is not an object-oriented language, so we can’t really talk about “objects”. An actor in Erlang is represented by a thing called a Process ID (Pid). But that’s nitpicking. The second part of the statement is more interesting. Strictly speaking, an actor may execute concurrently but at times it will not. For instance, in Scala, actor code may be executed by the calling thread.

Caveats aside, it’s convenient to think of actors as objects with a thread inside.

Message Passing

Actors communicate through message passing. Actors don’t communicate using shared memory (or at least pretend not to). The only way data may be passed between actors is through messages.

Erlang has a primitive send operation denoted by the exclamation mark. To send a message Msg to the process (actor) Pid you write:

Pid ! Msg

The message is copied to the address space of the receiver, so there is no sharing.

If you were to imitate this mechanism in Java, you would create a Thread object with a mailbox (a concurrent message queue), with no public methods other than put and get for passing messages. Enforcing copy semantics in Java is impossible so, strictly speaking, mailboxes should only store built-in types. Note that passing a Java Strings is okay, since strings are immutable.

-Typed messages

Here’s the first conundrum: in Java, as in any statically typed language, messages have to be typed. If you want to process more than one type of messages, it’s not enough to have just one mailbox per actor. In Erlang, which is dynamically typed, one canonical mailbox per actor suffices. In Java, mailboxes have to be abstracted from actors. So an actor may have one mailbox for accepting strings, another for integers, etc. You build actors from those smaller blocks.

But having multiple mailboxes creates another problem: How to block, waiting for messages from more than one mailbox at a time without breaking the encapsulation? And when one of the mailboxes fires, how to retrieve the correct type of a message from the appropriate mailbox? I’ll describe a few approaches.

-Pattern matching

Scala, which is also a statically typed language, uses the power of functional programming to to solve the typed messages problem. The receive statement uses pattern matching, which can match different types. It looks like a switch statements whose case labels are patterns. A pattern may specify the type it expects. You may send a string, or an integer, or a more complex data structure to an actor. A single receive statement inside the actor code may match any of those.

receive {
    case s: String => println("string: "+ s)
    case i: Int => println("integer: "+ i)
    case m => println("unknown: "+ m)
}

In Scala the type of a variable is specified after the colon, so s:String declares the variable s of the type String. The last case is a catch-all.

This is a very elegant solution to a difficult problem of marrying object-oriented programming to functional programming–a task at which Scala exceeds.

-Casting

Of course, we always have the option of escaping the type system. A mailbox could be just a queue of Objects. When a message is received, the actor could try casting it to each of the expected types in turn or use reflection to find out the type of the message. Here’s what Martin Odersky, the creator of Scala, has to say about it:

The most direct (some would say: crudest) form of decomposition uses the type-test and type-cast instructions available in Java and many other languages.

In the paper he co-authored with Emir and Williams (Matching Objects With Patterns) he gives the following evaluation of this method:

Evaluation: Type-tests and type-casts require zero overhead for the class hierarchy. The pattern matching itself is very verbose, for both shallow and deep patterns. In particular, every match appears as both a type-test and a subsequent type-cast. The scheme raises also the issue that type-casts are potentially unsafe because they can raise ClassCastExceptions. Type-tests and type-casts completely expose representation. They have mixed characteristics with respect to extensibility. On the one hand, one can add new variants without changing the framework (because there is nothing to be done in the framework itself). On the other hand, one cannot invent new patterns over existing variants that use the same syntax as the type-tests and type-casts.

The best one could do in C++ or D is to write generic code that hides casting from the client. Such generic code could use continuations to process messages after they’ve been cast. A continuation is a function that you pass to another function to be executed after that function completes (strictly speaking, a real continuation never returns, so I’m using this word loosely). The above example could be rewritten in C++ as:

void onString(std::string const & s) {
    cout << "string: " << s << std::endl;
}
void onInt(int i) {
    cout << "integer: " << i << std::endl;
}

receive<std::string, int> (&onString, &onInt);

where receive is a variadic template (available in C++0x). It would do the dynamic casting and call the appropriate function to process the result. The syntax is awkward and less flexible than that of Scala, but it works.

The use of lambdas might make things a bit clearer. Here’s an example in D using lambdas (function literals), courtesy Sean Kelly and Jason House:

receive(
    (string s){ writefln("string: %s", s); },
    (int i){ writefln("integer: %s", i); }
);

Interestingly enough, Scala’s receive is a library function with the pattern matching block playing the role of a continuation. Scala has syntactic sugar to make lambdas look like curly-braced blocks of code. Actually, each case statement is interpreted by Scala as a partial function–a function that is not defined for all values (or types) of arguments. The pattern matching part of case becomes the isDefinedAt method of this partial function object, and the code after that becomes its apply method. Of course, partial functions could also be implemented in C++ or D, but with a lot of superfluous awkwardness–lambda notation doesn’t help when partial functions are involved.

-Isolation

Finally, there is the problem of isolation. A message-passing system must be protected from data sharing. As long as the message is a primitive type and is passed by value (or an immutable type passed by reference), there’s no problem. But when you pass a mutable Object as a message, in reality you are passing a reference (a handle) to it. Suddenly your message is shared and may be accessed by more than one thread at a time. You either need additional synchronization outside of the Actor model or risk data races. Languages that are not strictly functional, including Scala, have to deal with this problem. They usually pass this responsibility, conveniently, to the programmer.

-Kilim

Java is not a good language to implement the Actor model. You can extend Java though, and there is one such extension worth mentioning called Kilim by Sriram Srinivasan and Alan Mycroft from Cambridge, UK. Messages in Kilim are restricted to objects with no internal aliasing, which have move semantics. The pre-processor (weaver) checks the structure of messages and generates appropriate Java code for passing them around. I tried to figure out how Kilim deals with waiting on multiple mailboxes, but there isn’t enough documentation available on the Web. The authors mention using the select statement, but never provide any details or examples.

Correction: Sriram was kind enough to provide an example of the use of select:

int n = Mailbox.select(mb0, mb1, .., timeout);

The return value is the index of the mailbox, or -1 for the timeout. Composability is an important feature of the message passing model.

Dynamic Networks

Everything I described so far is common to CSP (Communicating Sequential Processes) and the Actor model. Here’s what makes actors more general:

Connections between actors are dynamic. Unlike processes in CSP, actors may establish communication channels dynamically. They may pass messages containing references to actors (or mailboxes). They can then send messages to those actors. Here’s a Scala example:

receive {
    case (name: String, actor: Actor) =>
        actor ! lookup(name)
}

The original message is a tuple combining a string and an actor object. The receiver sends the result of lookup(name) to the actor it has just learned about. Thus a new communication channel between the receiver and the unknown actor can be established at runtime. (In Kilim the same is possible by passing mailboxes via messages.)

Actors in D

The D programming language with my proposed race-free type system could dramatically improve the safety of message passing. Race-free type system distinguishes between various types of sharing and enforces synchronization when necessary. For instance, since an Actor would be shared between threads, it would have to be declared shared. All objects inside a shared actor, including the mailbox, would automatically inherit the shared property. A shared message queue inside the mailbox could only store value types, unique types with move semantics, or reference types that are either immutable or are monitors (provide their own synchronization). These are exactly the types of messages that may be safely passed between actors. Notice that this is more than is allowed in Erlang (value types only) or Kilim (unique types only), but doesn’t include “dangerous” types that even Scala accepts (not to mention Java or C++).

I will discuss message queues in the next installment.


If it weren’t for the multitude of opportunities to shoot yourself in the foot, multithreaded programming would be easy. I’m going to discuss some of these “opportunities” in relation to global variables. I’ll talk about general issues and discuss the ways compilers can detect them. In particular, I’ll show the protections provided by my proposed extensions to the type system.

Global Variables

There are so many ways the sharing of global variables between threads can go wrong, it’s scary.

Let me start with the simplest example: the declaration of a global object of class Foo (in an unspecified language with Java-like syntax).

Foo TheFoo = new Foo;

In C++ or Java, TheFoo would immediately be visible to all threads, even if Foo provided no synchronization whatsoever (strictly speaking Java doesn’t have global variables, but static data members play the same role).

If the programmer doesn’t do anything to protect shared data, the default immediately exposes her to data races.

The D programming language (version 2.0, also known as D2) makes a better choice–global variables are, by default, thread local. That takes away the danger of accidental sharing. If the programmer wants to share a global variable, she has to declare it as such:

shared Foo TheFoo = new shared Foo;

It’s still up to the designer of the class Foo to provide appropriate synchronization.

Currently, the only multithreaded guarantee for shared objects in D2 is the absence of low-level data races on multiprocessors–and even that, only in the safe subset of D. What are low level data races? Those are the races that break some lock-free algorithms, like the infamous Double-Checked Locking Pattern. If I were to explain this to a Java programmer, I’d say that all data members in a shared object are volatile. This property propagates transitively to all objects the current object has access to.

Still, the following implementation of a shared object in D would most likely be incorrect even with the absence of low-level data races:

class Foo {
    private int[] _arr;
    public void append(int i) {
       _arr ~= i; // array append
    }
}

auto TheFoo = new shared Foo;

The problem is that an array in D has two fields: the length and the pointer to a buffer. In shared Foo, each of them would be updated atomically, but the duo would not. So two threads calling TheFoo.append could interleave their updates in an unpredictable way, possibly leading to loss of data.

My race-free type system goes further–it eliminates all data races, both low- and high-level. The same code would work differently in my scheme. When an object is declared shared, all its methods are automatically synchronized. TheFoo.append would take Foo‘s lock and make the whole append operation atomic. (For the advanced programmer who wants to implement lock-free algorithms my scheme offers a special lockfree qualifier, which I’ll describe shortly.)

Now suppose that you were cautious enough to design your Java/D2 class Foo to be thread safe:

class Foo {
    private int [] _arr;
    public synchronized void append(int i) {
       _arr ~= i; // array append
    }
}

Does it mean your global variable, TheFoo, is safe to use? Not in Java. Consider this:

static Foo TheFoo; // static = global
// Thread 1
TheFoo = new Foo();
// Thread 2
while (TheFoo == null)
    continue;
TheFoo.append(1);

You won’t even know what hit you when your program fails. I will direct the reader to one of my older posts that explains the problems of publication safety on a multiprocessor machine. The bottom line is that, in order to make your program work correctly in Java, you have to declare TheFoo as volatile (or final, if you simply want to prevent such usage). Again, it looks like in Java the defaults are stacked against safe multithreading.

This is not a problem in D2, since shared implies volatile.

In my scheme, the default behavior of shared is different. It works like Java’s final. The code that tries to rebind the shared object (re-assign to the handle) would not compile. This is to prevent accidental lock-free programming. (If you haven’t noticed, the code that waits on the handle of TheFoo to switch from null to non-null is lock-free. The handle is not protected by any lock.) Unlike D2, I don’t want to make lock-free programming “easy,” because it isn’t easy. It’s almost like D2 is endorsing lock-free programming by giving the programmer a false sense of security.

So what do you do if you really want to spin on the handle? You declare your object lockfree.

lockfree Foo TheFoo;

lockfree implies shared (it doesn’t make sense otherwise), but it also makes the handle “volatile”. All accesses to it will be made sequentially consistent (on the x86, it means all stores will compile to xchg).

Note that lockfree is shallow–data members of TheFoo don’t inherit the lockfree qualification. Instead, they inherit the implied shared property of TheFoo.

It’s not only object handles that can be made lockfree. Other atomic data types like integers, Booleans, etc., can also be lockfree. A lockfree struct is also possible–it is treated as a tuple whose all elements are lockfree. There is no atomicity guarantee for the whole struct. Methods can be declared lockfree to turn off default synchronization.

Conclusion

Even the simplest case of sharing a global variable between threads is fraught with danger. My proposal inobtrusively eliminates most common traps. The defaults are carefully chosen to let the beginners avoid the pitfalls of multithreaded programming.


In my last post I talked about the proposal for the ownership scheme for multithreaded programs that provides alias control and eliminates data races. The scheme requires the addition of new type qualifiers to the (hypothetical) language. The standard concern is that new type qualifiers introduce code duplication. The classic example is the duplication of getters required by the introduction of the const modifier:

class Foo {
    private Bar _bar;
public:
    Bar get() {
        return _bar;
    }
    const Bar get() const {
        return _bar;
    }
}

Do ownership annotations lead to the same kind of duplication? Fortunately not. It’s true that, in most cases, two implementations of each public method are needed–with and without synchronization–but this is taken care by the compiler, not by the programmer. Unlike in Java, we don’t need a different class for shared Vector and thread-local ArrayList. In my scheme, when a vector is instantiated as a monitor (shared), the compiler automatically puts in the necessary synchronization code.

Need for generic code

The ownership scheme introduces an element of genericity by letting the programmer specify ownership during the instantiation of a class (just as a template parameter is specified during the instantiation of a template).

I already mentioned that most declarations can be expressed in two ways: one using type qualifiers, another using template notation–the latter exposing the generic nature of ownership annotations. For instance, these two forms are equivalent:

auto foo2 = new shared Foo;
auto foo2 = new Foo<owner::self>;

The template form emphasizes the generic nature of ownership annotations.

With the ownership system in place, regular templates parametrized by types also gain an additional degree of genericity since their parameters now include implicit ownership information. This is best seen in objects that don’t own the objects they hold. Most containers have this property (unless they are restricted to storing value types). For instance, a stack object might be thread-local while its elements are either thread-local or shared. Or the stack might be shared, with shared elements, etc. The source code to implement such stacks may be identical.

The polymorphic scheme and the examples are based on the GRFJ paper I discussed in a past post.

An example

– Stack

A parameterized stack might look like this :

class Stack<T> {
    private Node<T> _top;
public:
    void push(T value) {
        auto newNode = new Node<owner::this, T>;
        newNode.init(:=value, _top);
        _top = newNode;
    }
    T pop() {
        if (_top is null) return null;
        auto value := _top.value();
        _top = _top.next();
        return value;
    }
}

This stack is parametrized by the type T. This time, however, T includes ownership information. In particular T could be shared, unique, immutable or–the default–thread-local. The template picks up whatever qualifier is specified during instantiation, as in:

auto stack = new Stack<unique Foo>;

The _top (the head of the linked list) is of the type Node, which is parametrized by the type T. What is implicit in this declaration is that _top is owned by this–the default assignment of ownership for subobjects. If you want, you can make this more explicit:

private Node<owner::this, T> _top;

Notice that, when constructing a new Node, I had to specify the owner explicitly as this. The default would be thread-local and could leak thread-local aliases in the constructor. It is technically possible that the owner::this could, at least in this case, be inferred by the compiler through simple flow analysis.

Let’s have a closer look at the method push, where some interesting things are happening. First push creates a new node, which is later assigned to _top. The compiler cannot be sure that the Node constructor or the init method don’t leak aliases. That looks bad at first glance because, if an alias to newNode were leaked, that would lead to the leakage of _top as well (after a pop).

And here’s the clincher: Because newNode was declared with the correct owner–the stack itself–it can’t leak an alias that has a different owner. So anybody who tries to access the (correctly typed) leaked alias would have to hold the lock on the stack. Which means that, if the stack is shared, unsynchronized access to any of the nodes and their aliases is impossible. Which means no races on Nodes.

I also used the move operator := to move the values stored on the stack. That will make the stack usable with unique types. (For non-unique types, the move operator turns into regular assignment.)

I can now instantiate various stacks with different combinations of ownerships. The simplest one is:

auto localStack = new Stack<Foo>;

which is thread-local and stores thread-local objects of class Foo. There are no restrictions on Foo.

A more interesting combination is:

auto localStackOfMonitors = new Stack<shared Foo>;

This is a thread-local stack which stores monitor objects (the opposite is illegal though, as I’ll explain in a moment).

There is also a primitive multithreaded message queue:

auto msgQueue = new shared Stack<shared Foo>;

Notice that code that would try to push a thread-local object on the localStackOfMonitors or the msgQueue would not compile. We need the rich type system to be able to express such subtleties.

Other interesting combinations are:

auto stackOfImmutable = new shared Stack<immutable Foo>;
auto stackOfUnique = new shared Stack<unique Foo>;

The latter is possible because I used move operators in the body of Stack.

– Node

Now I’ll show you the fully parameterized definition of Node. I made all ownership annotations explicit for explanatory purposes. Later I’ll argue later that all of them could be elided.

class Node<T> {
private:
    T _value;
    Node<owner::of_this, T> _next;
public:
    void init(T v, Node<owner::of_this, T> next)
    {
        _value := v;
        _next = next;
    }
    T value() {
        return :=_value;
    }
    Node<owner::of_this, T> next() {
        return _next;
    }
}

Notice the declaration of _next: I specified that it must be owned by the owner of the current object, owner::of_this. In our case, the current object is a node and its owner is an instance of the Stack (let’s assume it’s the self-owned msgQueue).

This is the most logical assignment of ownership: all nodes are owned by the same stack object. That means no ownership conversions need be done, for instance, in the implementation of pop. In this assignment:

_top = _top.next();

the owner of _top is msgQueue, and so is the owner of its _next object. The types match exactly. I drew the ownership tree below. The fat arrows point at owners.
Ownership hierarchy using owner::of_this

But that’s not the only possibility. The default–that is _next being owned by the current node–would work too. The corresponding ownership tree is shown below.

Ownership hierarchy using owner::this

The left-hand side of the assignment

_top = _top.next();

is still owned by msgQueue. But the _next object inside the _top is not. It is owned by the _top node itself. These are two different owners so, during the assignment, the compiler has to do an implicit ownership conversion. Such conversion is only safe if both owners belong to the same ownership tree (sometimes called a “region”). Indeed, ownership is only needed for correct locking, and the locks always reside at the top of the tree (msgQueue in this case). So, after all, we don’t need to annotate _next with the ownership qualifier.

The two other annotations can be inferred by the compiler (there are some elements of type inference even in C++0x and D). The argument next to the method init must be either owned by this or be convertible to owner::this because of the assignment

_next = next;

Similarly, the return from the method next is implicitly owned by this (the node). When it’s used in Stack.pop:

_top = _top.next();

the owner conversion is performed.

With ownership inference, the definition of Node simplifies to the following:

class Node<T> {
private:
    T _value;
    Node<T> _next; // by default owned by this
public:
    void init(T v, Node<T> next)
    {
        _value := v;
        _next = next; // inference: owner of next must be this
    }
    T value() {
        return :=_value;
    }
    Node<T> next() {
        return _next; // inference: returned node is owned by this
    }
}

which has no ownership annotations.

Let me stress again a very important point: If init wanted to leak the alias to next, it would have to assign it to a variable of the type Node<owner::this, T>, where this is the current node. The compiler would make sure that such a variable is accessed only by code that locks the root of the ownership tree, msgQueue. This arrangement ensures the absence of races for the nodes of the list.

Another important point is that Node contains _value of type T as a subobject. The compiler will refuse instantiations where Node‘s ownership tree is shared (its root is is self-owned), and T is thread-local. Indeed, such instantiation would lead to races if an alias to _value escaped from Node. Such an alias, being thread-local, would be accessible without locking.

Comment on notation

In general, a template parameter list might contain a mixture of types, type qualifiers, values, (and, in D, aliases). Because of this mixture, I’m using special syntax for ownership qualifiers, owner::x to distinguish them from other kinds of parameters.

As you have seen, a naked ownership qualifier may be specified during instantiation. If it’s the first template argument, it becomes the owner of the object. Class templates don’t specify this parameter, but they have access to it as owner::of_this.

Other uses of qualifier polymorphism

Once qualifier polymorphism is in the language, there is no reason not to allow other qualifiers to take part in polymorphism. For instance, the old problem of having to write separate const versions of accessors can be easily solved:

class Foo {
    private Bar _bar;
    public mut_q Bar get<mutability::mut_q>() mut_q
    {
        return _bar;
    }
}

Here method get is parametrized by the mutability qualifier mut_q. The values it can take are: mutable (the default), const, or immutable. For instance, in

auto immFoo = new immutable Foo;
immutable Bar b = immFoo.get();

the immutable version of get is called. Similarly, in

void f(const Foo foo) {
    const Bar b = foo.get();
}

the const version is called (notice that f may also be called with an immutable object–it will work just fine).

Class methods in Java or D are by default virtual. This is why, in general, non-final class methods cannot be templatized (an infinite number of possible versions of a method would have to be included in the vtable). Type qualifiers are an exception, because there is a finite number of them. It would be okay for the vtable to have three entries for the method get, one for each possible value of the mutability parameter. In this case, however, all three are identical, so the compiler will generate just one entry.

Conclusion

The hard part–explaining the theory and the details of the ownership scheme–is over. I will now switch to a tutorial-style presentation that is much more programmer friendly. You’ll see how simple the scheme really is in practice.


Since ownership plays a major role in race-free programming, it will be the first topic in my proposal for a race-free system. I presented the bird’s eye view of the system and provided a few teasers in my previous post. The design is based on published papers (see bibliography at the end). My contribution was to integrate several ideas into one package.

When I showed this proposal to my friends they either didn’t believe it could work or considered it too complex, depending which end they were looking at. From users’ perspective, the system looks relatively simple, so the natural reaction is: That can’t work. If you get into the details of why it works, and how the compiler knows you are in danger of a data race, you need some theory, and that is complex. So I decided to deal with some theory first, to show that the things work. If you’re not into theory, just look at the examples. They are usually simple to understand.

Owners

The ownership relationship is necessary to establish a tree-like structure among objects. This is needed by the compiler to decide which lock, if any, is responsible for the protection of each object, and take it when necessary. Simply speaking, the lock at the root of each tree protects the rest of the tree. If you think that your multithreaded programs don’t follow a tree structure, look at them closely. If they don’t, you either already have data races or are likely to develop them in the future.

-Every object has an owner

The owner may be another object–usually the embedding object. In the example below:

class Foo {
    void doWork() { _bar.doWork(); }
    private Bar _bar;
}

auto foo = new Foo;

the embedded object _bar is owned, at runtime, by the object foo (I repeat, the concrete object, not the class Foo). This is the default ownership relationship for embedded objects, so no special notation is needed to establish it (I’ll show later how to override this default).

There are also special symbolic “owners” that are used for the roots of ownership trees:

  • thread,
  • self,
  • unique, and
  • immutable.

unique and immutable are included in this list for convenience. I’ll discuss them later.

-Trees

Every object has just one owner for life, a condition necessary to create ownership trees that can be checked at compile time. Every tree has a single root and a lock is attached to that root, if needed.

The ownership information is embedded in the type of the object. Using this information, the compiler is able to deduce which lock must be held while accessing that object, and what kind of aliasing is allowed. All races (access to mutable shared variables without locking) are detected at compile time. I’ll sketch a proof later.

-What may be shared

Only immutable objects or objects rooted with a self-owned object may be shared between threads.

Additionally, objects whose direct owner is self (such objects are called monitors) may have multiple aliases while being shared. Monitors may own (and protect) other objects that are not monitors.

-Locking

The compiler will make sure that access to an object can only happen when the root of its ownership tree is locked (symbolic owners other than self are considered locked at all times). Since an object may only have one lock associated with it (at the top of its ownership tree), this condition is enough to ensure freedom from races.

Proof: I have to show that when a (mutable) object is seen by more than one thread, each access to it (read or write) is always protected by the same lock. Indeed, for an object to be shared between threads, the root of its ownership tree must be self, hence the top object must be a monitor. This monitor’s lock is always, automatically or explicitly, taken before accessing any member of the tree. The compiler knows which lock to take because the ownership information is encoded in the type of the object.

Introducing ownership annotations

Ownership is specified at the instance level (although it may be restricted at the class level). The previous example, which relied on default assignment of owners, is equivalent to the more explicit instance-level specification (that you will never see in actual programs):

Foo<owner::thread> foo = new Foo<owner::thread>;

This declares and constructs foo as being owned by the symbolic owner, thread. The embedded object _bar‘s owner is foo.

-Creating a monitor

A self-owned object is a monitor (I will alternate between the notation using shared type modifier or explicit owner annotation, <owner::self>). It contains a hidden lock and its methods are, by default, synchronized. Continuing with my example:

auto fooMon = new shared Foo;
// The same as:
// auto fooMon = new Foo<owner::self>;
fooMon.doWork();

The variable fooMon is a monitor and the doWork method is implicitly synchronized. The object _bar is now owned by fooMon. Its type can be expressed (this is rarely needed, however see the example of external ownership) as:

Bar<owner::fooMon>

Types parameterized by runtime entities (fooMon is a runtime handle) are known in programming language theory as dependent types.

Notice that I’m using the same class to create thread-local and shared instances. This is usually possible unless there is a specific restriction at the class level.

Note to D programmers: The current semantics of D “shared” is slightly different from my proposal. For instance, it forces all embedded objects to be monitors (their methods must be synchronized by their own lock), requires explicit use of the synchronized keyword, and forces all access in non-synchronized methods to be sequentially consistent. (And it doesn’t guarantee freedom from races.)

Thread-local objects

The special thread owner, which is the owner of all thread-local objects, is conceptually always locked, so thread-local objects don’t require any synchronization. Also, thread is the default owner so, in the absence of any ownership annotations, all objects are thread-local. That’s one of the defaults that makes single-threaded programs work as-is.

Here’s an interesting twist–global and static objects are by default thread-local. This part has been implemented in D, uncovering a number of threading bugs in the process.

Monitors

The special self owner (or the shared type modifier) is used to create monitor objects. A monitor has a built-in lock and all its public methods are by default synchronized.

As always with defaults, the language must provide a (preferably safe) way to bypass them. To prevent locking, a method may be explicitly marked as lockfree. The compiler is obliged to check if the lockfree method doesn’t access the object’s members in a non-safe way (although it can’t prevent high-level races on lockfree variables). That restricts the lockfree constructs to those that don’t require whole-program analysis to prove their safety.

The lockfree annotation is essential for, among others, the double-checked locking pattern (DCLP). I showed its implementation as a teaser in my previous post.

Subobjects

As I explained earlier, data members of an object are by default owned by that object. This way they inherit the root owner from their parent. This is another default that makes single-threaded programs run without additional qualifiers.

Notice that there are two important aspects of ownership, the direct owner and the root owner, which might be different. The direct owner is used in type-checking, the root owner in deciding which synchronization method to use. Both are known or inferred during compilation.

As usual, the defaults may be overridden. For instance, you may embed a monitor in a thread-local object by qualifying it as self-owned/shared:

class Holder {
    private Mon<owner::self> _mon;
}

or, in common notation, as shared:

class Holder {
    private shared Mon _mon;
}

Here, _mon is not owned by Holder (the default has been overridden) so it doesn’t inherit its root owner. Its methods are synchronized by its own lock. As you can see, ownership tree not always reflects embedding. An embedded monitor starts a new tree.

Well, the situation is a bit subtler. Objects in Java or D have reference semantics, so there is a hidden pointer, or handle, in the code above. Accessing the handle is not the same as accessing the object proper. Consider this example:

class Holder {
    private shared Mon _mon;
    public setMon(shared Mon newMon) {
        _mon = newMon;
    }
}

Let’s instantiate a self-owned Holder and a self-owned Mon:

auto holder = new shared Holder;
auto newMon = new shared Mon;
holder.setMon(newMon);

Since holder is itself a monitor, the setMon method is automatically synchronized by its lock (it must be!). Therefore, strictly speaking, the handle part of _mon is owned by holderMon, whereas the object-proper part is self-owned.

You cannot embed a thread-owned object inside a monitor–the compiler would flag it as an error. This is part of alias control–a thread-local object might possibly have thread-local aliases that may be accessed without locking. Being part of a monitor, it could then migrate to another thread and cause a race.

What if a subobject is accessed directly (not through a method)? This may happen when the subobject is declared public:

class Foo {
    public Bar _bar;
}

In that case not all uses of _bar are allowed. Consider this:

auto foo = new shared Foo;
foo._bar.m(); // error

Access to _bar must happen only when foo is locked. The compiler knows it because the full type of _bar is:

Bar<owner::foo>

Here’s the corrected code:

synchronized(foo) {
    foo._bar.m();
}

An even better solution is to make _bar private and provide appropriate methods to access it. Those methods would be automatically synchronized for a shared foo.

unique and immutable

I discussed unique objects in one of my previous posts. Although not strictly required in the ownership scheme, uniqueness allows for very efficient and safe transmission of large objects between threads. It makes sense to include unique as another symbolic root owner, since its multithreaded semantics is different from other types and it doesn’t require locking.

Some languages, including D, define immutable objects, which cannot be modified after creation. Such objects may be freely shared and passed by reference between threads. Again, immutable may be used as a root owner.

Example

With the preliminaries out of the way, I can now explain in more detail the workings of the teaser from my previous post. Here’s the definition of the class MVar:

class MVar<T> {
private:
    T    _msg;
    bool _full;
public:
    void put(T msg) {
        _msg := msg; // move
        _full = true;
        notify();
    }
    T take() {
        while (!_full)
            wait();
        _full = false;
        return := _msg;
    }
}

First, let’s instantiate MVar as a shared (self-owned) monitor that is used to pass unique objects of class Foo as messages:

auto chanUnique = new shared MVar<unique Foo>;

The type of _msg in this instantiation is unique Foo, which is the same as Foo<owner::unique>. The method put takes unique Foo, so the following code is type-correct:

auto foo = new unique Foo;
chanUnique.put(:= foo); // move foo

Notice that unique objects cannot be assigned or passed by value–they have to be moved, hence the use of the move operator, :=. Internally, the method put also uses the move operator (good thinking on the part of the designer–otherwise MVar couldn’t be instantiated with unique). What’s interesting about this example is that messages are not deep-copied between threads. They are safely passed by reference.

Since chanUnique is self-owned (shared), both put and get are automatically synchronized.

Now let’s access chanUnique from another thread:

// another thread
unique Foo f2 = chanUnique.get(); // implicit move of rvalue

The return type of get is unique Foo, so the types check. I could have used the move operator, but since the right hand side is an rvalue, the compiler lets me use the assignment.

Now for the tricky case: What’s wrong with this code?

auto mVar = new shared MVar<Foo>;
auto myFoo = new Foo;
mVar.put(myFoo);
myFoo.unsyncMethod(); // ouch!

Since myFoo is created as thread-local (that’s the default), its methods are not synchronized. If I were able to pass it to shared MVar, another thread could obtain it through get. It could then call the unsynchronized method unsyncMethod at the moment when I was calling it. A data race would be possible! Or would it?

Guess what–the compiler won’t let you shoot yourself in the foot. It will notice that it would have to instantiate a shared object mVar with a thread-local member _msg. That’s against the rules! (A shared object cannot own a thread-local object.)

External ownership

In the original GRFJ paper the authors showed an example where one object was owned by another object without the former being embedded in the latter. They made an observation that, for the purpose of locking, the ownership relationship must be unchangeable: You can’t switch the owner on the fly. Therefore external ownership is allowed only if the owner is declared final.

final shared Lock lockObj = new shared Lock;
auto foo = new Foo<owner::lockObj>;
auto bar = new Bar<owner::lockObj>;

In this case, the compiler will only allow access to foo under the lock of lockObj, as in:

synchronized(lockObj) {
    foo.method();
    bar.method();
}

This construct is useful in situations where the locking discipline is not easily convertible to object hierarchy.

Conclusion

You might have noticed my use of dual notation. Most user code would be written with type qualifiers such as shared, unique, or immutable. However, in some cases I used an alternative notation that looked more like the specification of template parameters: <owner::self>, <owner::unique>, <owner::immutable>, or even <owner::thread> (in D they would be surrounded by !( and )). This was not meant to further confuse the reader, but as a gentle introduction to qualifier polymorphism, which I will describe in the next installment. I will show how classes and methods may be parameterized with different types of ownership, cutting down code duplication.

I’d like to thank Andrei Alexandrescu, Walter Bright, Sean Kelly and Jason House for very helpful comments. I’m also indebted to the D community for discussing my previous posts.

Bibliography

  1. Boyapati, Rinard, A Parameterized Type System for Race-Free Java Programs
  2. C. Flanagan, M. Abadi, Object Types against Races.

Making the C language race-free is quite a challenge. I discussed one approach in my previous post. Here’s another one, based on lightweight annotations and whole-program analysis.

SharC is a sharing checker and program transformer for C. It is described in the paper SharC: Checking Data Sharing Strategies for Multithreaded C.

Sharing modes

The goal of SharC is to detect (and possibly eliminate) data races from legacy C programs. This is an incremental process in which the programmer annotates the program with type qualifiers, runs the checker, listens to its complaints, ads more annotations, etc., until SharC is totally satisfied and produces an instrumented version of the program with additional built-in runtime checks.

Some of the annotations that specify sharing modes are similar to those of Cyclone, but there are some new ones.

  • private: Thread-local
  • readonly: Does not change (immutable). Interestingly, it may be cast to another sharing mode that is writable.
  • locked(lock): Shared under specific lock. The lock must be readonly.
  • racy: Intentionally racy. No compile- or runtime checks.
  • dynamic: May change sharing mode at runtime, at which point it’s checked for readonly or single-thread access.

Note that SharC provides ways to move data between various sharing modes. The key to these operations is a very clever reference counting scheme built into the memory allocator. The paper provides the details of that scheme, but I’ll skip them here.

The overall policy of SharC is to consider almost all sharing (even under locks) illegal unless explicitly declared by the user.

Safe cast

Dynamic types can be cast between sharing modes. Here’s an example:

int readonly * y;
...
x = SCAST(int private *, y);

A pointer to a readonly int (which may be shared) is cast to a pointer to private (thread-only) int. Normally such cast would be unsafe. The int might be referenced by multiple readonly pointers (potentially shared between threads). The clients of those pointers don’t expect the value to ever change and may access it without locking. That’s why SCAST not only nulls the original pointer y, but also checks if the data reference count is zero (using the clever reference counting scheme I mentioned before). If it’s not, it reports an error.

Example

The paper offers a more elaborate example, which shows the use of dynamic data types and checked casting. The example is based on a pattern typical for multimedia processing and games.

A data buffer is processed in stages and passed from thread to thread.

We start with a linked list of stages. Each stage is run by a separate thread. The first stage is fed some data in a buffer (for instance a frame of a movie). The thread responsible for that stage starts its own processing. When it’s done, it hands the buffer over to the next stage, and so on. The buffer is like a car on an assembly line.

When the assembly line is created, each stage is assigned a different processing function through the function pointer fun (see code below).

You may also think of a stage as a one-element synchronous channel, with the buffer being the message.

What is important in this example is that we don’t want to copy a potentially very large buffer. We want to pass it between threads by reference without risking data races.

This is a stage structure with all the necessary SharC annotations.

typedef struct stage(q) {
    struct stage dynamic * q next;
    cond racy * q cv;
    mutex racy * readonly mut;
    char locked(mut) * locked(mut) sdata;
    void (*q fun)(char private * private fdata);
} stage_t;

Don’t be put off by the sheer number of annotations. Most of them are defaults or can be easily inferred by SharC.

This is what the programmer must annotate:

  • The buffer sdata is shared and protected by mut.
  • The function pointed to by fun takes a private (thread-local) buffer, fdata.

Notice that, without casting, fun cannot be called with sdata, because sdata is shared.

What SharC infers is that:

  • Structure stage is parametrized by some sharing policy, q (which stands for “qualifier”). This qualifier will be provided when the variable of type stage is defined. (This is called qualifier polymorphism.)
  • All pointers inside stage, except for mut and sdata inherit the qualifier q.
  • The field next, which is a pointer to the next stage, has two qualifiers. The pointer itself inherits the qualifier q from its parent. The data it points to is marked dynamic, which means that it will be checked during runtime to be either accessed by a single thread or immutable.
  • The pointers cv and fun also inherit the parent qualifier, q. Condition variable cv is declared racy to turn off compiler checks for sharing. cv is shared between threads but does not require locking.
  • The mutex object is also racy. The pointer to it though is readonly (immutable). This is an important detail, since it is unsafe to change the mutex during the lifetime of data it protects (which is sdata in this case).
  • sdata is a pointer to a char buffer. Both the pointer and the buffer it points to are protected by the mutex, mut.
  • Finally, the pointer fun inherits its qualifier from the parent. The function parameter, fdata, is a private pointer to a private buffer.

This is the beginning of the thread function, thrFunc:

void dynamic * private thrFunc(void dynamic * private d) {
    stage_t dynamic * private S = d;
    stage_t dynamic * private nextS = S->next;
    char private * private ldata;
    ...

The function takes and returns a private void pointer to dynamically shared data. In reality this pointer points to a stage structure–this is reflected in the definition of S. Notice that in C a void pointer can be converted to a typed pointer. In SharC, the compiler makes sure that the qualifiers match.

The next field of S inherits its qualifier private from S. The data it points to (the next stage) is dynamic, as per the definition of stage.

Finally there is a private pointer to a private buffer, which will be used to point to the shared buffer. Since in general private and shared don’t mix, a sharing cast will be performed in the body of thrFunc (see below).

Here’s the rest of thrFunc:

    ...
    while (notDone) {
        mutexLock(S->mut);
        while (S->sdata == NULL)
            condWait(S->cv,S->mut);
        ldata = SCAST(char private *, S->sdata);
        condSignal(S->cv);
        mutexUnlock(S->mut);
        S->fun(ldata);
        if (nextS) {
            mutexLock(nextS->mut);
            while (nextS->sdata)
                condWait(nextS->cv,nextS->mut);
            nextS->sdata =
                SCAST(char locked(nextS->mut) *, ldata);
            condSignal(nextS->cv);
            mutexUnlock(nextS->mut);
        }
    }
    return NULL;
}

The stage S that was passed to the thread function contains shared pointer sdata. To access this pointer we need to take the correct lock, S->mut. This is the lock that was specified in the declaration of sdata inside stage.

First we loop waiting for S->sdata to become non-null. Another thread is supposed to initialize that pointer. As long as the pointer is null, we block on the condition variable S->cv.

while (S->sdata == NULL)
    condWait(S->cv,S->mut);

Notice that condWait takes the mutex as its second argument. It has to unlock it while waiting for the signal, otherwise no other thread would have a chance to modify S->sdata (and we’d have a livelock). (In the channel analogy, we are performing a synchronous receive.)

Now we are ready to call the function S->fun to process the buffer. However, that function is declared to take a private buffer, not a shared buffer. It could, for instance, be a library function that has no provisions for sharing. It could be strlen or a Fast Fourier Transform.

The programmer knows that it is safe to call this function, because the buffer has been handed over between threads and shared access is no longer in the picture. What remains is to convince the compiler that that’s okay, and to add some code that does necessary checks at runtime. All this is done by SCAST.

The line

ldata = SCAST(char private *, S->sdata);

casts S->sdata to a pointer to a private (thread-local) buffer and stores it in the private variable ldata (local data). It also nulls the source and makes sure there are no more references to it (a very clever reference counting scheme, remember?). If this looks to you like move semantics, you get the right idea.

Still under lock, we signal the previous thread that the shared buffer has been nulled so it can re-fill it.

We are now ready to release the lock and do some local processing. We call the function S->fun with the (now private) buffer.

If there is a next stage (pointed to by S->next), we want to pass our processed buffer to it. Before we do that, we have to wait until the buffer in the next stage becomes null. (In the channel analogy, we are now doing a synchronous send.)

while (nextS->sdata)
    condWait(nextS->cv,nextS->mut);

We have to do it under the next stage lock.

We assign our current buffer to the next stage and signal the next thread. Here, again, we have to perform a cast: our buffer is private, and next->sdata is shared and locked under next->mut. The statement:

nextS->sdata = SCAST(char locked(nextS->mut) *, ldata);

casts ldata (local data) to a pointer to char locked(nextS->mut). This is exactly the opposite of what we did earlier (casting shared data to local data). As before, the runtime has to null the source and check if there are no other references to it. This way thread-local unique data may be moved to another thread. If you followed my previous posts, you notice the pattern: unique data, move semantics.

Conclusion

What is unique in SharC approach is the presence of dynamically checked casts that are used to change sharing modes at runtime. This is probably the best that can be done on top of C.

When designing a new language, we could use different mechanisms to achieve the same goals. For instance, the buffer could be declared unique. The move semantics would then guarantee the absence of dangerous aliasing thus eliminating the need for behind-the-scenes reference counting.

The problem of passing a unique object to an unsuspecting library function (without nulling the source) can be solved by declaring the function parameter as lent (which should be the default).

If you read this space on a semi-regular basis, you might have noticed that recently it turned into a series of “Cliff Notes for CS grad students.” That was because I tried to prepare myself for the task of designing a multithreading system and memory model for the D programming language. My future posts will (hopefully) track progress in that direction.


When I read Dan Grossman’s paper, Type-Safe Multithreading in Cyclone, I imagine Dan on a raft in the middle of a tropical cyclone, his threads soaked by the rain, frantically racing to build a shelter. Maybe a type system can’t protect you from the rain, but it sure can protect you from data races–even if the final product looks a little like a Rube Goldberg contraption.

Cyclone, as it turns out, is a safe dialect of C. Dan’s goal was to extended Cyclone’s type system to make it safe in a multithreaded environment. This is not an easy task in a language that uses pointers and manual memory management. The ideas are similar to those in the dialects of Java in my previous posts, but the type-theoretical concepts are pretty advanced.

-Locks

A Cyclone lock is a special type parameterized by name. Defining such entities requires a major type-system tour de force. Little known notions like “existential types,” “dependent types,” and “effect systems” come into play. The major point of all this magic is that you want to use lock names without knowing what they are.

You create a Cyclon lock using the following pseudo-syntax:

let lk<λ> = newlock();

I’ll use Greek letters for symbolic lock names (except for the special name, loc), since the author didn’t propose any concrete syntax and, admittedly, never finished the implementation.

What’s happening behind the scenes is that newlock returns an existential type, which is unpacked into variable lk of type lock_t<λ>. That variable may, for instance, be passed to a (generic) function taking lock_t<λ>.

The beauty of an existential type is that it won’t let the client “guess” the name of the lock. The name is totally opaque–there is not even a type for it. It can only be generated by the system in the call to newlock.

To synchronize a (possibly compound) statement, you precede it with sync(lk), where lk is a lock variable (or an expression that evaluates to such). Continuing the previous example, we may write:

sync(lk) ++*p;

which increments the value pointed to by p under the lock lk.

-Ownership

In an OO language, ownership is the key to a race-free type system. The owner of a given object has to be locked before the object may be accessed.

In a non-OO language, like Cyclone, instead of specifying the owner of data, you specify the lock that protects it. Since you might not have access to the lock variable when you are declaring data, the symbolic lock name is used instead.

Also, since all references in Cyclon are expressed through pointers, lock annotations are associated with pointers. (This is again parallel to annotating object references with their owner objects.)

We can now complete the example above with the annotated definition of p:

int *λ p = new 0;

The pointer p can only be access when the lock whose name is λ is held.

-Local and Shared data

In Cyclon, thread-local data appear as a special case of shared data. A special lock name loc and a (global) variable nonlock are used to turn off the locking of individual instances. (Compare this to the dummy owner thisThread in the OO approach.)

Lock names are also used to parameterize functions and structs. In that case it might be necessary to be able to encode the “sharability” of the type. If the lock type has the kind LS, it actually protects Shared data. The kind LU, on the other hand, may be Unsharable, i.e., it could be instantiated with the thread-local loc. LS is a sub-kind of LU.

The main purpose of “kinding” is to ensure that thread-local data can never be shared between threads.

-Example

All this sounds very dry without an actual (however contrived) example. Let’s start with a simple function inc:

void inc<λ:LU>(int *λ p; {λ}) 
{
    *p = *p + 1;
}

The function is parameterized by a lock name parameter λ of the LU kind. It means that it can be instantiated both in the sharing and thread-local context. Lock-name variable λ first appears in the declaration of the pointer parameter p. Data pointed to by p is protected by the lock named λ. That’s not telling a lot–every pointer in Cyclon is protected by some lock (or nonlock).

What makes the difference is that the same name λ appears in the effects part of the declaration. The runtime system keeps track of what locks are being held at any point in the program, and passes this information to every function. Here, {λ} is an effect that contains one lock name, λ. In other words, the lock that protects p must be taken before calling inc. (Of course, other locks might be held too.)

Here’s an example of how inc may be called from another function, inc2.

void inc2<λ:LU>(lock_t<λ> plk, int *λ p ;{}) 
{
    sync(plk) inc(p);
}

The effects part of the declaration of inc2 is empty. No locks need be held before calling it. But when inc is called, the current effects include the name of the lock specified in the declaration of plk (the sync section adds λ to the effects). Since the same lock name appears in the declaration of p, the precondition for calling inc is fulfilled.

A struct containing a pointer must also be an existential type. The actual value of λ is assigned to LkInt when the type is “packed” (created from its constituents).

struct LkInt 
{<λ:LS> 
    lock_t<λ> plk;
    int*λ p; 
};

Here, LkInt (locked integer) only makes sense in a sharing context, so λ is of the strictly sharing kind, LS. That allows LkInts to be passed between threads.

Here’s an example of using a pointer to LkInt:

void g<λ:LU>(struct LkInt *λ s ;{λ}) 
{
    let LkInt{<λ'> .plk=lk, .p=ptr} = *s;
    inc2(lk, ptr);
}

Function g is parameterized by some lock name of the LU kind (so it may be loc). This lock is used by the callee to lock the LkInt argument s (λ is present in the callee effects).

Now s itself contains a pointer and a lock (of the LS kind). We get at them by unpacking an existential type–splitting it into constituents and giving them new names, lk and ptr. The name of lk is λ’. A shallow copy of s is performed in the process. We can now call inc2, which doesn’t require any effects, and uses lk to lock and increment ptr.

Finally, all these things come together in the example below:

void f(;{}) {
    let lk<λ> = newlock();
    int *λ p1 = new 0;
    int *loc p2 = new 0;
    struct LkInt *loc s = new LkInt{.plk=lk, .p=p1};
    spawn(g, s, sizeof(struct LkInt));
    inc2(lk, p1);
    inc2(nonlock, p2);
}

Here’s the play-by-play:

  • Function f requires no effects.
  • The programmer creates a lock lk with the name λ (unpacks an existential type returned by newlock).
  • She creates a pointer p1 to an integer protected by the lock named λ.
  • Then she creates an unprotected thread-local pointer p2 (using dummy lock name, loc)
  • She combines the lock lk and the pointer p1 into a struct LkInt. This is an example of “packing” an existential type. Note that both components must share the same lock name. (The pointer to LkInt, s, is thread-local–lock name: loc.)
  • The programmer spawns a thread that executes the function g with the argument s. The argument is passed by a shallow copy, but deep contents (the lock and the pointer) are shared. In fact they must be shared with the kinding LS; otherwise spawn would not compile. This mechanism protects any thread-local data from being shared with a new thread.
  • While the call to g(s) executes in parallel, the programmer increments p1 under the lock lk. Notice that these are the same variables that the other thread operates on. The shared lock enforces mutual exclusion.
  • The increment of thread-local p2 requires no lock, that’s why dummy nonlock is used.

-Limitations

The biggest limitation of the Cyclon system is that it’s static. The association between data and locks is rigid. That makes several useful idioms impossible to implement. There is no way to “move” data between threads. You can either pass a pointer together with its lock (essentially creating a monitor), or pass data by value.

The other serious problem is the overall complexity of the system–especially the “effects” part. When effects have to be included and manipulated in generic code things get a little hairy.

Granted, a lot of annotations can be elided–the defaults “do the right thing” in most cases. Still, what remains is asking a lot from an average programmer.

-Conclusion

I would like to thank Dan Grossman for correcting some of my mistakes and explaining a few subtle points.

In my next installment I’ll describe a more recent race-free extension of C, SharC, which supports a mix of static and dynamic checking.


A type system that prevents data races must not eliminate useful concurrency patterns or force the programmer to maintain multiple copies of almost identical data structures.

In my previous post about Guava, a dialect of Java, I talked about a type system that enforces the separation of thread-local and shared objects at the class level. Unfortunately, such rigid system forces the programmer (especially a library writer) to provide dual implementations of many generic classes like vectors, queues, etc. Often the only difference between implementations is the use of synchronized (or, in case of Guava, the special base class called Monitor).

To solve this problem, Boyapati and Rinard proposed a system where the same generic class may be used in different sharing contexts. For instance, the same parameterized vector class, may be used to instantiate a thread-local instance that requires no locking, as well as a shared instance that has a built-in lock.

The paper precedes Generic Java, but employs similar notation. For instance, it lets you define a generic vector class like this:

  class vector<thisOwner> { ... }

and then instantiate it as thread-local (no locking requirements):

  vector<thisThread> localV= new vector<thisThread>;

or as a shared monitor:

  vector<self> sharedV= new vector<self>;

The first template parameter is always interpreted as “the owner” (more about it later). Objects owned by thisThread are thread-local, objects owned by self are monitors.

Even though the notation used in GRFJ (the acronym the authors use for their Java dialect) is different from that of Guava, there are many similarities in the two approaches, since both have to deal with similar issues: explicit sharing, ownership, passing objects between threads, etc.

-Explicit sharing

You might remember that, in Guava, only those classes that inherit from Monitor may be shared. In GRFJ, sharing is defined at the instance level (the instantiation of a template). Every instance declaration must specify the owner of the object. If the owner is not thisThread, the object may be shared between threads. The equivalent of the Guava Monitor is a self-owned object–its owner is declared as self.

-Ownership

Ownership plays an essential role in preventing data races. Every object has an owner. In GRFJ there are three basic types of owners:

  1. thisThread–the object owned by thisThread is never shared.
  2. self–the object is the root of an ownership tree.
  3. Another object–the sharing is defined by the root of the ownership tree

Types of ownership translate naturally into protection patterns. If the owner is thisThread there is no need for locking. If the owner is self, all methods must be synchronized by the object’s lock. In the third case, the object’s owner is responsible for locking. More precisely, the root of the ownership tree to which the object belongs has to be locked, if it’s not declared thread-local.

There are some interesting combinations of ownership. For instance, you can declare a thread-local vector that stores self-owned (shared) items. Or you can declare a shared Stack that contains (owns) a Vector object. All access to Vector will be protected by the Stack’s lock.

For this level of expressiveness, we need classes that are parameterized by owners. Notice that, if the owner of the object x is another object, that object must exist when the declaration of x is reached.

A template might be parameterized by multiple owners. The first one on the list is always the owner of this. In the example below, Stack is parameterized by two owners–the first owns the Stack, the second owns the Values. Note that, in this case, all Values will always share the same owner.

class Stack<thisOwner, valueOwner> {
    Node<thisOwner, valueOwner> head = null;

    void push(Value<valueOwner> value) requires (this) {
        Node<thisOwner, valueOwner> newNode = 
            new Node<thisOwner, valueOwner>;
        newNode.init(value, head);
        head = newNode;
    }
    Value<valueOwner> pop() requires (this) {
        if (head == null) return null;
        Value<valueOwner> value = head.value();
        head = head.next();
        return value;
    }
}

The requires clause specifies whose lock must be held when calling a particular method. In this case, the lock on this must be held. But if this is owned by another object, the locking responsibility automatically moves up a level, until it reaches the ownership root.

Here’s the definition of Node:

class Node<thisOwner, valueOwner> {
    Value<valueOwner> value;
    Node<thisOwner, valueOwner> next;

    void init(Value<valueOwner> v, Node<thisOwner, valueOwner> n)
        requires (this) {
        this.value = v;
        this.next = n;
    }
    Value<valueOwner> value() requires (this) {
        return value;
    }
    Node<thisOwner, valueOwner> next() requires (this) {
       return next;
    }
}

And the definition of Value:

class Value<thisOwner> { int x = 0; }

Using the declarations above we are now ready to declare different values and stacks:

Value<thisThread> v1 = new Value<thisThread>;
Value<self> v2 = new Value<self>;

We have created two values from the same template–v1 is thread-local, v2 is a monitor (access to x is protected by its lock).

Stack<thisThread, thisThread> s1 = new Stack<thisThread, thisThread>;
Stack<thisThread, self> s2 =  new Stack<thisThread, self>;
Stack<self, self> s3 = new Stack<self, self>;

Stack s1 is thread-local and can store only thread-local values. No locks or locking code will be created by the compiler. Stack s2 is also thread-local, but it stores shareable values. A thread-local stack will never be visible from other threads. But self-owned values it stores might be accessed from multiple threads. Finally, s3 is a shared stack containing shared values. Both, the stack s3 and the values it stores have their own independent locks.

s1.push(v1);
s2.push(v2);

We may push a thread-local value v1 on the stack s1, but if we tried to push v2 on s1, the compiler would consider it an error. Pushing v2 on s2, on the other hand, is okay.

Since GRFJ is based on Concurrent Java, the locking and threading look a little odd. To illustrate the sharing of s3, we fork a thread, passing it s3 and v2 (both are sharing-ready) and executing the code s3.push(v2) under the lock of s3.

fork (s3,v2) {synchronized (s3) in {s3.push(v2);}}

Notice that, according to the declaration of s3, it would be an error to push v1 onto it. Indeed, that could result in illegal sharing of a thread-local object. The type system protects us from a hard-to-detect error.

This is hardly a free lunch, though. Annotating every class and every variable might be just too much for a programmer. Fortunately, most owners can be inferred by the compiler by analyzing assignments. Because of that, single threaded programs in GRFJ require virtually no annotations.

-Foreign owners

In most cases, ownership tree follows the containment tree. The owner contains the ownee. Although desirable from the architectural point of view, this arrangement is not strictly necessary. An object might declare another separate object as its owner. This is safe under one condition–the owner object may not be overwritten. Hence the requirement that the owner be final. Here’s the relevant example:

final Foo<self> owner = new Foo<self>;
Bar<owner> ownee = new Bar<owner>;

This becomes important when building new locking protocols from pre-existing parts.

-Object migration

The passing of objects between threads requires either deep copying (like Guava’s Values), or move semantics. In GRFJ, move semantics is implemented by specifying another special type of owner–unique. Unique objects cannot be copied, they have to be moved. The “move” operator is the postfix decrement, just like in Guava. It moves the reference and nulls the source.

Value<unique> v3 = new Value<unique>
Value<unique> v4 = v3--;

Our Stack class is not prepared to store unique objects. This prohibition may be included in its definition (compare it with C++ “concepts”):

class Stack<thisOwner, valueOwner> where (valueOwner != unique)

Conversely, we might want to redefine Stack to store unique objects. A few code changes would be necessary though. For instance, in push, the value must be moved:

newNode.init(value--, head);

In pop, the return value has to be moved:

return value--;

The class Node requires similar changes.

The authors note that, despite appearances, the moving of objects is multiprocessor safe. Even though the assignment to a new reference is not guaranteed to be immediately visible to other threads, a unique object is always published to another thread via a monitor (for instance, a shared stack). The new thread can only get hold of the object by first acquiring the monitor’s lock, which forces previous stores to commit.

-Alias control

Move semantics requires control over aliasing–a moved object may not leave any aliases, nor may it carry along any references to thread-local objects. GRFJ provides additional annotations to mark non-escaping method parameters. The syntax is !e appended to the parameter type. Here’s an example:

void display(Value<unique>!e val);
Value<unique> v5 = new Value<unique>;
display(v5);

A unique object here is passed by reference only because display guarantees that its argument won’t escape.

-Immutable objects

Immutable objects may be passed between threads by reference. In GRFJ, immutable objects are marked by another special owner type, readonly. Interestingly, the problem of the construction of immutable objects is cleverly avoided. You first create a unique object and then move it to a readonly reference.

Value<unique> v6 = new Value<unique>;
v6.x = 1;
Value<readonly> v7 = v6--;

This is a perfectly safe operation, since there is a guarantee that no writable aliases to the unique object may stay behind. The move to readonly freezes the object forever.

Immutable objects can only be passed to methods that promise not to modify their arguments. This is done by appending !w to the type of the parameter. The two suffixes may be combined to form !ew (I am not kidding you!), a parameter that doesn’t escape and is not modified by the method.

-Limitations

– Some concurrent programs use multi-stage access patterns that are not expressible in GRFJ. For instance, a shared array is divided into disjoint sections and each thread operates exclusively on its section without any locking. After all threads synchronize on a barrier, they pick up different sections and continue. The ownership of sections changes with time. (In D this pattern might be implementable using array slices.)

– Dynamic downcasting, which used to be the workhorse of Java before generics, can’t verify the ownership part of the cast, because this information is not available at runtime.

– Static variables may be accessed only when the client holds the class lock. Each class with static variables must therefore have a static lock.

– The authors mention the idea of parameterizing methods that accept poly-owned arguments. This is not so easy as it sounds, since virtual functions cannot be parameterized by a potentially infinite set of types. My guess is that this is possible because detailed ownership information is only needed during type checking. Still, the compiler might have to produce additional implementations of a method depending on whether the parameter is thread-local or not (imagine the parameterized method creating a local variable of the same ownership type–sometimes this variable must contain a lock, sometimes not). Still, this is a finite set of possibilities, so vtable slots may be preallocated for all of them.

– The template syntax for ownership won’t work in languages where templates already accept value parameters, and the compiler isn’t always able to distinguish between types and values.

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