Programming



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.


“I’ve been doing some template metaprogramming lately,” he said nonchallantly.

Why is it funny? Because template metaprogramming is considered really hard. I mean, über-guru-level hard. I’m lucky to be friends with two such gurus, Andrei Alexandrescu who wrote the seminal “Modern C++ Programming,” and Eric Niebler, who implemented the Xpressive library for Boost; so I know the horrors.

But why is template metaprogramming so hard? Big part of it is that C++ templates are rather ill suited for metaprogramming, to put it mildly. They are fine for simple tasks like parameterized containers and some generic algorithms, but not for operations on types or lists of types. To make things worse, C++ doesn’t provide a lot in terms of reflection, so even such simple tasks like deciding whether a given type is a pointer are hard (see the example later). Granted, C++0x offers some improvements, like template parameter packs; but guru-level creds are still required.

So, I’ve been doing some template metaprogramming lately… in D. The D programming language doesn’t have the baggage of compatibility with previous botched attempts, so it makes many things considerably easier on programmers. But before I get to it, I’d like to talk a little about the connection between generic programming and functional programming, give a short intro to functional programming; and then show some examples in C++ and D that involve pattern matching and type lists.

It’s not your father’s language

The key to understanding metaprogramming is to realize that it’s done in a different language than the rest of your program. Both in C++ and D you use a form of functional language for that purpose. First of all, no mutation! If you pass a list of types to a template, it won’t be able to append another type to it. It will have to create a completely new list using the old list and the new type as raw materials.

Frankly, I don’t know why mutation should be disallowed at compile time (all template calculations are done at compile time). In fact, for templates that are used in D mixins, I proposed not to invent a new language but to use a subset of D that included mutation. It worked just fine and made mixins much easier to use (for an example, see my DrDobbs article).

Once you disallow mutation, you’re pretty much stuck with functional paradigm. For instance, you can’t have loops, which require a mutable loop counter or some other mutable state, so you have to use recursion.

You’d think functional programmers would love template metaprogramming; except that they flip over horrendous syntax of C++ templates. The one thing going for functional programming is that it’s easy to define and implement. You can describe typeless lambda calculus with just a few formulas in operational semantics.

One thing is important though: meta-language can’t be strongly typed, because a strongly typed language requires another language to implement generic algorithms on top of it. So to terminate the succession of meta-meta-meta… languages there’s a need for either a typeless, or at least dynamically-typed, top-level meta-language. My suspicion is that C++0x concepts failed so miserably because they dragged the metalanguage in the direction of strong typing. The nails in the coffin for C++ concepts were concept maps, the moral equivalent of implicit conversions in strongly-typed languages.

Templates are still not totally typeless. They distinguish between type arguments (introduced by typename or class in C++), template template arguments, and typed template arguments. Here’s an example that shows all three kinds:

template<class T, template<class X> class F, int n>

Functional Programming in a Nutshell

“Functions operating on functions”–that’s the gist of functional programming. The rest is syntactic sugar. Some of this sugar is very important. For instance, you want to have built-in integers and lists for data types, and pattern matching for dispatching.

-Functions

Here’s a very simple compile-time function in the C++ template language:

template<class T> 
struct IsPtr {
    static const bool apply = false;
}

If it doesn’t look much like a function to you, here it is in more normal ad-hoc notation:

IsPtr(T) {
    return false;
}

You can “execute” or “call” this meta-function by instantiating the template IsPtr with a type argument and accessing its member apply:

IsPtr<int>::apply;

There is nothing magical about “apply”, you can call it anything (“result” or “value” are other popular identifiers). This particular meta-function returns a Boolean, but any compile-time constant may be returned. What’s more important, any type or a template may be returned. But let’s not get ahead of ourselves.

-Pattern matching

You might be wondering what the use is for a function (I’ll be dropping the “meta-” prefix in what follows) that always returns false and is called IsPtr. Enter the next weapon in the arsenal of functional programmers: pattern matching. What we need here is to be able to match function arguments to different patterns and execute different code depending on the match. In particular, we’d like to return a different value, true, for T matching the pattern T*. In the C++ metalanguage this is done by partial template specialization. It’s enough to define another template of the same name that matches a more specialized pattern, T*:

template<class T>
struct IsPtr<T*> {
    static const bool apply = true;
}

When faced with the call,

IsPtr<int*>::apply

the compiler will first look for specializations of the template IsPtr, starting with the most specialized one. In our case, the argument int* matches the pattern T* so the version returning true will be instantiated. Accessing the apply member of this instantiation will result in the Boolean value true, which is exactly what we wanted. Let me rewrite this example using less obfuscated syntax.

IsPtr(T*) {
    return true;
}
IsPtr(T) { // default case
    return false;
}

D template syntax is slightly less complex than that of C++. The above example will read:

template IsPtr(T) {
    static if (is (T dummy: U*, U))
        enum IsPtr = true;
    else
        enum IsPtr = false;
}
// Compile-time tests
static assert( IsPtr!(int*) );
static assert( !IsPtr!(int) );

As you can see, D offers compile-time if statements and more general pattern matching. The syntax of pattern matching is not as clear as it could be (what’s with the dummy?), but it’s more flexible. Compile-time constants are declared as enums.

There is one little trick (a hack?) that makes the syntax of “function call” a little cleaner. If, inside the template, you define a member of the same name as the template itself (I call it the “eponymous” member) than you don’t have to use the “apply” syntax. The “call” looks more like a call, except for the exclamation mark before the argument list (a D tradeoff for not using angle brackets). You’ll see later how the eponymous trick fails for more complex cases.

-Lists

The fundamental data structure in all functional languages is a list. Lists are very easy to operate upon using recursive algorithms and, as it turns out, they can be used to define arbitrarily complex data structures. No wonder C++0x felt obliged to introduce a compile-time type list as a primitive. It’s called a template parameter pack and the new syntax is:

template<class... T>Foo

You can instantiate such a template with zero arguments,

Foo<>

one argument,

Foo<int>

or more arguments,

Foo<int, char*, void*>

How do you iterate over a type list? Well, there is no iteration in the metalanguge so the best you can do is to use recursion. To do that, you have to be able to separate the head of the list from its tail. Then you perform the action on the head and call yourself recursively with the tail. The head/tail separation is done using pattern matching.

Let me demonstrate a simple example from the paper Variadic Templates by Garry Powell et al. It calculates the length of a pack using recursion. First, the basic case–length-zero list:

template<>
struct count <> {
    static const int value = 0;
}

That is the full specialization of a template, so it will be tried first. Here’s the general case:

template<typename Head, typename... Tail>
struct count<Head, Tail...> {
    static const int value = 1 + count<Tail...>::value;
}

Let’s see what it would look like in “normal” notation:

count() {
    return 0;
}
count(head, tail) {
    return 1 + count(tail);
}

And here’s the D version:

template count(T...) {
    static if (T.length == 0)
        enum count = 0;
    else
        enum count = 1 + count!(T[1..$]);
}
// tests
static assert( count!() == 0);
static assert( count!(int, char*, char[]) == 3);

T… denotes a type tuple, which supports array-like access. To get to the tail of the list, D uses array slicing, where T[1..$] denotes the slice of the array starting from index 1 up to the length of the array (denoted by the dollar sign). I’ll explain the important differences between C++ pack and D tuple (including pack expansion) in the next installment.

Conclusion

When looked upon from the functional perspective, template metaprogramming doesn’t look as intimidating as it it seems at first. Knowing this interpretation makes you wonder if there isn’t a better syntax or even a better paradigm for metaprogramming.

I’ll discuss more interesting parts of template metaprogramming in the next installment (this one is getting too big already). In particular, I’ll show examples of higher order meta-functions like Filter or Not and some interesting tricks with type lists.


Spawning a thread in non-functional languages is considered a very low-level primitive. Often spawn or CreateThread takes a function pointer and an untyped (void) pointer to “data”. The newly created thread will execute the function, passing it the untyped pointer, and it’s up to the function to cast the data into something more palatable. This is indeed the lowest of the lowest. It’s the stinky gutters of programming.

Isn’t it much nicer to create a Thread or a Runnable object and let the ugly casting be done under the covers? But, as I argued before, the Thread object doesn’t really buy you much in terms of the most important safety issue: the avoidance of data races. So we can have a Thread object instead of a void pointer, and a run method that understands the format of the Thread object (or Runnable, take your pick). But because the Thread /Runnable object has reference semantics, we still end up inadvertently sharing data between threads. Unless the programmer consciously avoids or synchronizes shared access, he or she is left exposed to the most vile concurrency bugs–by default!

As they say, Cooks cover their mistakes with sauces; doctors, with six feet of dirt; language designers, with objects.

Requirements

But enough ranting! I have the opportunity to design the spawn function for D and I don’t want to do any more cover-ups beyond hiding the ugly systems’ APIs. Here are my design requirements:

  • spawn should take an arbitrary function as the main argument. It should refuse (at compile time) delegates or closures, which would introduce back-door sharing. (This might be relaxed later as we gain experience in controlling the sharing.)
  • It should take a variable number of arguments of the types compatible with those of the function parameters. It should detect type mismatches at compile time.
  • It should refuse the types of arguments that are prone to introducing data races. For now, I’ll allow only value types, immutable types, and explicitly shared types (shared is a type modifier in D).

I wish I could use the more precise race-free type system that I’ve been describing in my previous posts, but since I can’t get it into D2, there’s still a little bit of “programmer beware” in this implementation.

These requirement seem like a tall order for any language other than D. I wouldn’t say it’s a piece of cake in D, but it’s well within the reach of a moderately experienced programmer.

Unit Tests

Let me start by writing a little use case for my design (Oh, the joys of extreme programming!):

S s = { 3.14 };
Tid tid = spawn(&thrFun, 2, s, "hello");
tid.join;

Here’s the definition of the function, thrFun:

void thrFun(int i, S s, string str) {
    writeln("thread function called with: ", i, ", ", s.fl, " and ", str);
}

Its parameter types fulfill the restrictions I listed above. The int is a value and so is S (structs are value types in D, unless they contain references):

struct S { 
    float fl; 
}

Interestingly, the string is okay too, because its reference part is immutable. In D, a string is defined as an array of immutable characters, immutable (char)[].

Besides positive tests, the even more important cases are negative. For instance, I don’t want spawn to accept a function that takes an Object as argument. Objects are reference types and (if not declared shared) can sneak in unprotected sharing.

How do you build unit tests whose compilation should fail? Well, D has a trick for that (ignore the ugly syntax):

void fo(Object o) {}
assert (!__traits(compiles, 
    (Object o) { return spawn(&fo, o); }));

This code asserts that the function literal (a lambda),

(Object o){ return spawn(&fo, o); }

does not compile with the thread function fo. Now that’s one useful construct worth remembering!

Implementation

Without further ado, I present you with the implementation of spawn that passes all the above tests (and more):

Tid spawn(T...)(void function(T) fp, T args)
    if (isLocalMsgTypes!(T))
{
    return core.thread.spawn( (){ fp(args); });
}

This attractively terse code uses quite a handful of D features, so let me first read it out loud for kicks:

  • spawn is a function template returning the Tid (Thread ID) structure. Tid is a reference-counted handle, see my previous blog.
  • It is parameterized by a type tuple T….
  • It takes the following parameters:
    • a pointer to a function, fp, taking arguments of the types specified by the tuple T…
    • a variable number of parameters, args, of types T….
  • The type tuple T… must obey the predicate isLocalMsgTypes, which is defined elsewhere.
  • The implementation of spawn calls the (in general, unsafe) function core.thread.spawn (defined in the module core.thread) with the following closure (nested function):
        (){ fp(args); }

    which captures local variables, args.

As you may guess, the newly spawned thread runs the closure, so it has access to captured args from the original thread. In general, that’s a recipe for a data race. What saves the day is the predicate isLocalMsgTypes, which defines what types are safe to pass as inter-thread messages.

Note the important point: there should be no difference between the constraints imposed on the types of parameters passed to spawn and the types of messages that can be sent to a thread. You can think of spawn parameters as initial messages sent to a nascent thread. As I said before, message types include value types, immutable types and shared types (no support for unique types yet).

Useful D features

Let me explain some of D novelties I used in the definition of spawn.

A function with two sets of parenthesized parameters is automatically a template–the first set are template parameters, the second, runtime parameters.

-Tuples

Type tuples, like T…, represent arbitrary lists of types. Similar constructs have also been introduced in C++0x, presumably under pressure from Boost, to replace the unmanageably complex type lists.

What are the things that you can do with a type-tuple in D? You can retrieve its length (T.length), access its elements by index, or slice it; all at compile time. You can also define a variable-argument-list function, like spawn and use one symbol for a whole list of arguments, as in T args:

Tid spawn(T...)(void function(T) fp, T args)

Now let’s go back to my test:

Tid tid = spawn(&f, 2, s, "hello");

I spawn a thread to execute a function of three arguments, void f(int i, S s, string str). The spawn template is instantiated with a type tuple (int, S, string). At compile time, this tuple is successfully tested by the predicate isLocalMsgTypes. The actual arguments to spawn, besides the pointer to function, are (2, s, “hello”), which indeed are of correct types. They appear inside spawn under the collective name, args. They are then used as a collective argument to fp inside the closure, (){ fp(args); }.

-Closures

The closure captures the arguments to spawn. It is then passed to the internal function (not a template anymore),

core.thread.spawn(void delegate() dlg)

When the new thread is created, it calls the closure dlg, which calls fp with the captured arguments. At that point, the value arguments, i and s are copied, along with the shallow part of the string, str. The deep part of the string, the buffer, is not copied–and for a good reason too– it is immutable, so it can safely be read concurrently. At that point, the thread function is free to use those arguments without worrying about races.

-Restricted Templates

The if statement before the body of a template is D’s response to C++0x DOA concepts (yes, after years of design discussions, concepts were finally killed with extreme prejudice).

if (isLocalMsgTypes!(T))

The if is used to create “restricted templates”. It contains a logical compile-time expression that is checked before the template is instantiated. If the expression is false, the template doesn’t match and you get a compile error. Notice that template restrictions not only produce better error messages, but can also impose restrictions that are otherwise impossible or very hard to enforce. Without the restriction, spawn could be called with an unsuitable type, e.g. an Object not declared as shared and the compiler wouldn’t even blink.

(I will talk about template restrictions and templates in general in a future blog.)

–Message Types

Besides values, we may also pass to spawn objects that are declared as immutable or shared (in fact, we may pass them inside values as well). In D, shared objects are supposed to provide their own synchronization–their methods must either be synchronized or lock free. An example of a shared object that you’d want to pass to spawn is a message queue–to be shared between the parent thread and the spawned thread.

You might remember that my race-free type system proposal included unique types, which would be great for message passing, and consequently as arguments to spawn (there is a uniqueness proposal for Scala, and there’s the Kilim message-passing system for Java based on unique types). Unfortunately, unique types won’t be available in D2. Instead some kind of specialized Unique library classes might be defined for that purpose.

Conclusion

The D programming language has two faces. On the one hand, it’s easy to use even for a beginner. On the other hand, it provides enough expressive power to allow for the creation of sophisticated and safe libraries. What I tried to accomplish in this post is to give a peek at D from the perspective of a library writer. In particular I described mechanisms that help make the concurrency library safer to use.

This is still work in progress, so don’t expect to see it in the current releases of D2.


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.


I started writing a post about implementing actors in D when I realized that there was something wrong with the way thread spawning interacts with data sharing. Currently D’s Thread class closely mimics its Java counterpart, so I started wondering if this may be a more general problem–a problem with mixing object-oriented paradigm with multithreading.

In functional languages like Erlang or Concurrent ML, you start a thread by calling a function–spawn or create, respectively. The argument to this function is another function–the one to be executed in a new thread. If you think of a new thread as a semi-separate application, the thread function is its “main”. You may also pass arguments to it–the equivalent of argc, argv, only more general. Those arguments are, of course, passed by value (we’re talking functional programming after all).

In non-functional languages it’s possible and often desirable to share data between threads. It’s very important to know which variables are shared and which aren’t, because shared variables require special handling–they need synchronization. You may share global variables with a thread, or you may pass shared variables to it during thread creation. You may also provide access to more shared variables during the thread’s lifetime by attaching and detaching them from the original shared variables–that’s how message queues may be described in this language.

In the object-oriented world everything is an object so, predictably, a (capital letter) Thread is an object too. An object combines data with code. There is a piece of data associated with every thread–the thread ID or a handle–and there are things you can do to a thread, like pause it, wait for its termination, etc.

But there’s more: A Thread in Java has a thread function. It’s a method called run. The user defines his or her own thread by inheriting from Thread and overriding run. Since run takes no arguments, data has to be passed to the new thread by making it part of the derived thread object. In general a thread object contains two types of data: shared and non-shared. The non-shared data are the value arguments passed by the creator to the thread function, possibly some return values, plus state used internally by the thread function. Thread data may form some logical abstraction or, as it often happens, have the structure of a kitchen sink after a dinner party.

Because of the presence of shared state, a Thread object must be considered shared, thus requiring synchronization. As I described in my previous posts, public methods of a shared object must either be synchronized or lock free. But what about the run method? It cannot be synchronized because that would make the whole thread object inaccessible to other threads, including its creator (which may be okay for a daemon thread, but wold be a fatal flaw in general).

It makes perfect sense for run to be private, since nobody should call it from the outside (or, for that matter, from the inside). But the reason for private methods not requiring synchronization is that they are only called from public methods, which must be synchronized. This is not true for runrun is not called from under any lock! So essentially run has unfettered access to all (potentially shared) data stored in this. Unless the programmer is very disciplined, the potential for data races is high. And that’s where Java stands right now (the Runnable interface has the same problems).

If you’ve been following my blog, you know that I’m working on a type system that eliminates races. If I can convince Walter and Andrei, this system might be implemented in the D programming language. As I mentioned, D’s treatment of threads comes directly from Java and therefore is inherently unsafe. Like in Java, D’s Thread object has a run method.

So how could D, with the race-free type system, improve on Java? One possibility is to make the run method lockfree. A lockfree method (public or private) is not synchronized but its access to this is severely restricted. It can only operate on lockfree data members (if any), unless it takes the object’s lock or calls a synchronized method (all public methods of a shared object are, by default, synchronized). Let me give you a small example:

class Counter: Thread {
public:
    // public methods are implicitly synchronized
    void inc() { ++_cnt; }
    void dec() { --_cnt; }
private:
    override void run() lockfree {
        inc(); // ok: calling a synchronized method
        wait(10000);
        synchronized(this) { // ok: explicit synchronization on this
            _cnt = _cnt * _cnt;
        }
        // _cnt /= 2; // error: not synchronized!
    }
    int _cnt;
}
// usage:
auto cnt = new shared Counter;
cnt.start;
cnt.inc;
cnt.join;

This approach would work and guarantee freedom from races, but I don’t think it fits D. Unlike Java, which is so OO that even main is a method of an object, D is a multi-paradigm language. It doesn’t have to force threads into an OO paradigm. And the thread function, being a thread equivalent of main doesn’t have to be a method of any object. In my opinion, the functional approach to thread creation would serve D much better.

Here’s how I see it:

class Counter {
public:
    void inc() { ++_cnt; }
    void dec() { --_cnt; }
    int get() const { return _cnt; }
    void set(int cnt) { _cnt = cnt; }
private:
    int _cnt;
}

void counterFun(shared Counter cnt) {
    cnt.inc;
    Thread.sleep(10000);
    synchronized(cnt) {
        int c = cnt.get;
        cnt.set(c * c);
    }
}
// usage:
auto cnt = new shared Counter;
Thread thr = Thread.spawn(&counterFun, cnt);
thr.start;
cnt.inc;
thr.join;

I find it more logical to have start and join operate on a different object, thr, rather than on the counter, cnt. The static template method spawn accepts a function that takes an arbitrary number of arguments that are either values or shared or unique objects. In particular, you could pass to it your (shared) communication channels or message queues to implement the message-passing paradigm.

Such spawn primitive could be used to build more complex classes–including the equivalents of a Java’s Thread or a Scala’s Actor.


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.

Most languages were caught unaware by the multicore revolution. C++, predictably, developed a portable assembly language for multiprocessors (C++0x atomics). Out of sheer desperation some programmers turned to Erlang. Many still try to ignore the elephant in the room, while others try to shoot it with BB guns provided by their favorite languages.

I’ve looked at many languages and decided that none provided the right combination of performance and safety. We are still waiting for the next big one to break into the multicore territory.

Towards this goal, I’ll present a series of posts in which I’m going to develop a
threading model for that hypothetical language. The model is based on several papers that I reviewed in my previous blogs posts. My own contribution is putting the best of those ideas together into one package. I will use syntax similar to that of the D programming language, but C++ and Java programmers shouldn’t have problems following it.

Teaser #1

In one of my previous posts I described a concurrent data structure based on Haskell’s MVar. It’s essentially a one-element message queue. Let’s have a peek at the implementation that uses my proposed multithreaded scheme. Notice the total absence of special annotations–even the synchronized keyword is missing. The only unusual thing is the move operator, :=, introduced in my previous blog. It is needed in case we want to pass unique objects through MVar. You are probably asking yourself, How the heck is this supposed to work in a multithreaded environment? Read on!

class MVar<T> {
private:
    T    _msg;
    bool _full;
public:
    // put: asynchronous (non-blocking)
    // Precondition: MVar must be empty
    void put(T msg) {
        assert (!_full);
        _msg := msg; // move
        _full = true;
        notify();
    }
    // take: If empty, blocks until full.
    // Removes the message and switches state to empty
    T take() {
        while (!_full)
            wait();
        _full = false;
        return := _msg;
    }
}

Let’s instantiate this template as a monitor–that is an object accessible from multiple threads. We do it by specifying the owner as “self” (the this owner is not explicitly listed as a template parameter, but it can be passed to the template during instantiation).

auto mVar = new MVar<owner::self, int>;

In a self-owned object all methods are automatically synchronized. The move operator acting on an int simply copies it.

That was easy! How about instantiating a self-owned MVar for passing unique objects? Piece of cake!

auto q = new MVar<owner::self, unique Foo>;

auto foo = new unique Foo;
q.put(:= foo); // move foo
assert (foo is null);

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

So far data races have been avoided by implicit synchronization in self-owned objects and by the use of move semantics for unique objects.

Of course you know how to break the MVar, right? Instantiate it with a regular, non-unique class objects and watch aliases spread through threads. Let’s try it:

auto mVar = new MVar<owner::self, Foo>;
auto myFoo = new Foo;
mVar.put(myFoo);
// now let me race through my local alias!
myFoo.method();

Surprise! This code will not compile because a self-owned object cannot have a thread-local member. Because of a richer type system, the compiler can easily detect such violations. I’ll explain the details of the ownership scheme in my future posts–for now let me assure you that, no matter how hard you try, you can’t create a data race in this system (unless you bypass it with explicit casts).

How to making concurrent programming safer?

I propose to do this by extending the type system. (You’ve already seen some examples above.) I realize that there is strong resistance to extending a language’s type system. The main problem is increased complexity. The programmers are reluctant to study and use complex annotation schemes. I will argue that, in the case of multithreading, the complexity argument is reversed. Shared-memory multithreading is hard! Figuring out how to avoid all the pitfalls of sharing is harder than learning a type system that prevents them.

If a programmer is not willing to learn or cannot understand the race-free type system, he or she should not be writing multithreaded programs.

How to limit complexity?

To limit the complexity of the type system, I came up with several simple principles. The most important one is that multithreaded extensions should not require modifications to single-threaded programs. That means coming up with reasonable defaults for all new annotations.

If necessary, the compiler should be able to derive some of the defaults from program analysis. Because modularity is very important, such analysis should not creep across compilation unit boundaries. Whole-program analysis is out of the question.

How to keep goals reasonable?

There are two major challenges in multithreaded programming:

  1. Avoiding races
  2. Preventing deadlocks

The first problem is approachable, whereas the second seems to be a pie in the sky. I will therefore concentrate on designing a race-free system that is based on existing academic research.

Such system would be incomplete without some support for lock-free programming. There are very good reasons for the language to enforce sequential consistency. For comparison, C++ committee, after a long struggle, decided to allow non-SC primitives (weak atomics). Java only enforces SC for volatile variables.

Teaser #2

Speaking of lock-free programming, here’s the implementation of the infamous Double-Checked Locking Pattern:

class Singleton<T>
{
private:
    lockfree T _value;
public:
    T get() lockfree
    {
        if (_value is null)   
        {
            synchronized(this)
            {
                if (_value is null)
                   _value = new T;
            }
            return _value;
        }
    }
}

A lockfree variable is guaranteed to be always atomically accessed in a sequentially consistent way. A lockfree method is not synchronized, but it can only operate on lockfree variables (or use a synchronized section). Even though the use of lockfree doesn’t introduce low-level races, it may create high-level races when accessing more that one lockfree variable at a time. But we all know that lock free programming is only for desperadoes.

Lessons learned from previous work

There are many commonalities in various approaches to race freedom.

  • Ownership should be built into the type system. In OO languages each object must have an owner. In C-like languages each piece of data must have an associated lock.
  • There should be efficient mechanisms for passing data between threads
    • By value
    • By reference. (The object being passed must be a monitor.)
    • As immutable data, by const reference
    • By unique reference using move semantics

Most of those ideas are expressible through type qualifiers.

Higher-level models

Explicit synchronization is hard, no doubt about it. In my proposal, the type system makes it safe. There are however other models of concurrency that, although more restrictive, are often easier to use.

One such model is based on message passing. In my opinion, support for message passing should be library-based. The race-free type system will make it safe and flexible. It will, for instance, support passing messages not only by value but also by reference–without sacrificing safety. In traditional type systems there is no way to express the requirement that a message passed by reference must either be a monitor itself (have it’s own lock) or behave like a C++ unique_ptr (an object that leaves no aliases behind). This requirement should be expressible through the type system, allowing the compiler to check for its violations.

I’ve been paying a lot of attention to software transactional memory (STM). I believe that STM could be built into the language–again, with the support of the type system. It looks like the hardest problem with STM is how it should inter-operate with other types of multithreaded access (both locked and lock-free). The simplest solution is to enforce full isolation–no STM object can ever be accessed outside of a transaction. It’s not clear how practical such approach is, but it definitely simplifies things to the point that STM becomes orthogonal to other types of synchronization. And that means it will be possible to tackle it at a later time, after the race-free multithreading model is firmly established.

In the next installment, I will describe the ownership scheme that is the foundation of the race-free type system.

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Bibliography

C. Flanagan, M. Abadi, Object Types against Races. The seminal paper introducing ownership-based type systems.

See also my previous posts on Guava and GRFJ that discuss race free dialects of Java.

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