Standalone Generic Scala Generator

A generic Scala generator class built directly on Scala's shift and reset operators.

In my previous post I showed a generic generator class built on top of my coroutines library. I commented in that post that I was taking the expedient approach of using my existing library, but that it would be possible to package all of the functionality into the Generator class.

I soon realized that it would in fact be relatively easy to do that packaging, and that the resulting relatively simple class would probably make a good vehicle for demonstrating the usefulness of Scala's delimited continuations. Thus I give you the StandaloneGenerator class:

package net.jimmc.scoroutine

import scala.collection.Iterator
import scala.util.continuations._

class StandaloneGenerator[T] extends Iterator[T] {
    private var nextValue:Option[T] = None
    private var nextStep:Option[Unit=>Unit] = None

    /** Subclass calls this method to generate values.
     * @param body The code for your generator.
     */
    protected def generate(body: => Unit @suspendable) {
        reset {
            suspend
            body
        }
    }

    /** Yield the next generated value.
     * Call this code from your generator to deliver the next value.
     */
    protected def yld(x:T):Unit @suspendable = {
        nextValue = Some(x)
        suspend
    }

    /** Retrieve the next generated value.
     * Call this from your main code.
     */
    def next:T = {
        step
        nextValue match {
            case None => throw new NoSuchElementException("next on empty generator")
                    //make it similar to the equivalent Iterator exception
            case Some(x) => nextValue = None; x
        }
    }

    /** True if there is another value to retrieve.
     * Call this from your main code.
     */
    def hasNext:Boolean = {
        step
        nextValue.isDefined
    }

    /** Save our continuation to resume later. */
    private def suspend:Unit @suspendable = {
        shift { k:(Unit=>Unit) =>
            nextStep = Some(k)
        }
    }

    /** If we have a next step but we don't have a value queued up,
     * run the generator until it calls yld or completes. */
    private def step = {
        if (nextValue.isEmpty) nextStep foreach { nextStep = None; _() }
    }
}

The StandaloneGenerator class is a plug-compatible replacement for the Generator class described in my previous post, as long as the derived generator does not use fancy scheduling as in the Primes Generator example. So, for example, you could take the Integers Generator example from that post, replace the two occurences of Generator with StandaloneGenerator, and everything would work the same way.

We have two variables: nextValue is our one-element queue where we store the next value to be returned by the generator, and nextStep is our "scheduler queue" where we store our continuation each time we suspend the generator to return a value. Both of these variables are of type Option so that we can tell when they hold a value.

Control is managed by the two functions suspend and step. The suspend method has a shift block that could not be much simpler: all it does is store the passed-in continuation in the nextStep variable. Since the body of a shift block is always the last thing executed within the scope of the CPS code (delimited either by the enclosing reset block or an explicitly invoked continuation), the fact that suspend does not execute its continuation means that after suspend does its thing, control returns to the point just past the enclosing reset, or to the next statement after the explicit continuation call.

I considered calling the step method resume instead, to make it clear that it was the complement to suspend, but from the point of view of the code calling step, by the time control returns to the point after the call to step, the generator code has already been suspended again: it has completed running one step, which is from one yld call to the next.

The step function executes our continuation if we have one, but only if we don't already have a value in nextValue. Using foreach on an Option is a neat way to execute a block of code only if the Option has a value (i.e. is not None). In this case, since the contents of nextStep (if it has any) is a function, the placeholder variable _ gets set to the continuation and the code fragment _() is what actually executes the continuation. Here are two other ways this function could be implemented that do the same thing:

    private def step1 = {
        if (nextValue.isEmpty) nextStep match {
            case None => //do nothing
            case Some(p) => nextStep = None; p()
        }
    }

    private def step2 = {
        if (nextValue.isEmpty && nextStep.isDefined) {
            val p = nextStep.get; nextStep = None; p()
        }
    }

Let's walk through this and see how it works (assuming the generator generates at least one value):

1. The main code instantiates a generator, which passes its generator body to the generate function, which calls suspend, which saves that body in nextStep without executing it.
2. The main code calls hasNext, which calls step, which runs the continuation. This starts the generator body code running, which runs until it calls yld with the first value. That first value is stored in nextValue and the generator code is suspended, with the continuation stored in nextStep. Since we now have a value stored in nextValue, hasNext returns true.
3. The main code calls next. Since we have a value in nextValue, the call to step does nothing (it is only there in case the caller calls next without first calling hasNext). The value in nextValue is returned, and that variable is cleared (set to None).
4. Each time the main code calls hasNext and it calls step, the continuation stored in nextStep is executed, the generator code calls yld with the next value, that value is stored into nextValue, the continuation is stored in nextStep, and the generator is suspended, and hasNext returns true.
5. Eventually (for a finite generator) there is a call to step where the generator finishes execution without calling yld. When this happens, no values are stored in either nextValue or nextStep. Since there is no value in nextValue, hasNext returns false. Since there is also no value in nextStep, further calls to step do nothing, and hasNext will continue to return false.

I dare say this might be one of the simplest realistic examples of the use of Scala's reset and shift operators that you will find. Two variables, six functions each not more than four lines of body code, 16 lines of function body code in total, 35 lines of code altogether.

Scala Generators

An implementation of generators on top of Scala's delimited continuations.

In my previous post I described a library that supports coroutines on top of Scala's delimited continuations capability. In this post I show how you can easily create generators on top of that coroutine library (net.jimmc.scoroutine). This is a second example of the kind of interesting construct that can be built on top of Scala's delimited continuations.

As with my previous post on coroutines, you don't need to understand reset and shift if you just want to use the Generator class shown here to write and use your own generators. But, as with coroutines, you should have a basic understanding of CPS code and its restrictions when writing generators.

Contents

• Generators
• Integers Generator
• Primes Generator
• Same Fringe
• More Possibilities

Generators

A generator is a routine that produces values like an iterator but is structured as a function. The generated values are returned by calling a special function, typically called yield, with each value that is generated. In our case, since yield is a reserved word in Scala, we will use yld instead.

Generators and coroutines are closely related. Depending on the implementation, generators and coroutines may be almost the same thing or fairly different, but in any case, if you have either one, you can implement the other one on top of it. Since we already have coroutines in the net.jimmc.scoroutine library described in my previous post, we will implement generators on top of coroutines using that library.

You can think of this approach as using the Producer-Consumer pattern, where we set up a generator as the producer and we allow the main code to act as the consumer. We create a generic Generator class that does the following:

• Creates a CoScheduler that we use to control the generator.
• Creates a CoQueue buffer into which we will place the generated values.
• Provides convenience functions yld (in place of the reserved word yield) and generate.
• Provides next and hasNext functions for the consuming code to call from a non-CPS context, and so that a Generator can be used as an Iterator.

This is all simple and straightforward. Here is the code for Generator:

package net.jimmc.scoroutine

import scala.collection.Iterator
import scala.util.continuations._

/** Generic generator class.
 */
class Generator[T] extends Iterator[T] {
    val sched = new DefaultCoScheduler
    val buf = new CoQueue[T](sched,1)

    /** Subclass calls this method to generate values.
     * @param body The code for your generator.
     */
    def generate(body: => Unit @suspendable) {
        sched.addRoutine("gen") { body }
        sched.run
    }

    /** Yield the next generated value.
     * Call this code from your generator to deliver the next value.
     */
    protected def yld(x:T):Unit @suspendable = {
        buf.blockingEnqueue(x)
    }

    /** Retrieve the next generated value.
     * Call this from your main code.
     */
    def next:T = {
        sched.run
        buf.dequeue
    }

    /** True if there is another value to retrieve.
     * Call this from your main code.
     */
    def hasNext:Boolean = {
        sched.run
        !buf.dequeueBlocker.isBlocked
    }
}

We are not concerning ourselves with performance here, so we are simply using the available DefaultCoScheduler as our scheduler. As a future optimization, we could develop a scheduler optimized for a single coroutine and use that as our scheduler for simple generators that fit that criterion. We could go further and use neither a scheduler nor CoQueue, packaging all of the functionality directly into the Generator class; but we are using the more expedient approach of using those two pieces, since we already have them and are familiar with their use from our experience with coroutines.

Integers Generator

Here is how we would use our generic Generator class to create a generator that will generate integers up to a specified maximum value:

import net.jimmc.scoroutine.Generator

class IntGen(max:Int) extends Generator[Int] {
    generate {
        var x = 1
        while (x<=max) {
            yld(x)
            x = x + 1
        }
    }
}

The one catch to remember here is that the body of the generate call is CPS code, so as with the body of a coroutine, there are some restrictions on what control constructs we can use. Thus we use a while loop with a var rather than a for loop, since the latter does not yet work with the continuations compiler plugin.

Given the above generator class, here is a simple GenInts object with a main function that creates an instance of that generator, then calls it to print out its values:

object GenInts {
    def main(args:Array[String]) = {
        val gen = new IntGen(4)
        for (i <- gen)
            println(i)
    }
}

Alternatively, we could replace the for loop with direct calls to hasNext and next:

object GenInts {
    def main(args:Array[String]) = {
        val gen = new IntGen(4)
        while (gen.hasNext)
            println(gen.next)
    }
}

Primes Generator

It is possible to use shift and reset directly to code up a generator, but because our coroutine library implements a scheduler to which new coroutines can be added at any time, this gives you the ability to create generators that include dynamic filter pipelines.

The example I use for this is the Sieve of Eratosthenes, a method of calculating primes in which, each time a prime is found, it is added to a list of prime divisors that are used for testing each new candidate. In this GenPrimes example, I create a new filter for each prime and add it to the pipeline. You can do this much more efficiently in Scala using a Stream, but this example illustrates the technique of dynamically building a pipeline within a generator.

import scala.util.continuations._

import net.jimmc.scoroutine.CoQueue
import net.jimmc.scoroutine.Generator

object GenPrimes {
    def main(args:Array[String]) = {
        val gen = new PrimeGen()
        for (i <- gen) {
            println("Prime: "+i)
        }
    }
}

class PrimeGen extends Generator[Int] {
    val bufSize = 1
    val out1 = new CoQueue[Int](sched,bufSize)

    sched.addRoutine("prime2")(nextPrime(2,out1))
    generate {
        def gen(n:Int):Unit @suspendable = {
            out1.blockingEnqueue(n)
            gen(n+1)
        }
        gen(2)
    }

    def nextPrime(p:Int, in:CoQueue[Int]):Unit @suspendable = {
        var out:Option[CoQueue[Int]] = None
        yld(p)
        def sieve():Unit @suspendable = {
            val n = in.blockingDequeue()
            if ((n%p)!=0) {
                if (!out.isDefined) {
                    out = Some(new CoQueue[Int](sched,bufSize))
                    val rName = "prime"+n
                    sched.addRoutine(rName)(nextPrime(n,out.get))
                }
                out.get.blockingEnqueue(n)
            } else {
                in.dequeueBlocker.waitUntilNotBlocked
            }
            sieve()
        }
        sieve()
    }
}

This example starts by setting up two coroutines: the addRoutine call sets up the first filter in the pipeline, which reads values from the out1 queue and filters our all numbers divisible by 2. The generator call sets up the other initial coroutine, which generates every integer and feeds it into the first filter in the pipeline. We start off this counting generator with the first prime number, 2.

The nextPrime function is called each time we see a new prime. It starts by outputting its prime parameter value p as a value of the GenPrimes generator. It then goes into a loop reading its input buffer and looking for values which are not divisible by its prime number. The first time it finds one (when out is not yet defined) it registers (with a call to addRoutine) a new coroutine based on a new instance of nextPrime that uses our output as its input. It then passes each candidate prime along to that next filter in the sieve pipeline.

You can tell this is CPS code because of the suspendable annotations, which is a cue to realizing that the code might not behave quite as you think. For example, the gen function within the body of the generate call is recursive, so you might think it would cause a stack overflow. But since we are in a CPS function and the call to blockingEnqueue is a call to a CPS function, the recursive call to gen is turned into a continuation and executed later from the scheduler, so it is in fact not recursive. Likewise the recursive call to sieve is not really recursive for the same reason.

Another CPS detail is the call to waitUntilNotBlocked. It would seem to be functionally unnecessary, since the first thing in the sieve function is a call to blockingDequeue. However, this is the same attempt to avoid blocking as discussed in my previous post; without this call our code will not work.

Same Fringe

The SameFringe problem has been called the "killer application" for coroutines. Given two trees, they have the same fringe if the leaves of the two trees, read from left to right, are the same.

With coroutines, or in this case generators, the simple solution to this problem is to create a generator that takes a tree and returns the sequence of leaves of that tree, then compare the outputs of two of those generators on the two trees to be compared.

We start with a simple tree definition:

sealed abstract class Tree[T]
case class Branch[T](left:Tree[T], right:Tree[T]) extends Tree[T]
case class Leaf[T](x:T) extends Tree[T]

Given this tree definition, we write a generator that walks a tree and yields all of the leaves:

import scala.util.continuations._
import net.jimmc.scoroutine.Generator

class TreeFringe[T](tree:Tree[T]) extends Generator[T] {
    generate {
        def walk(t:Tree[T]):Unit @suspendable = {
            t match {
                case Leaf(x) => yld(x)
                case Branch(left,right) => walk(left); walk(right)
            }
        }
        walk(tree)
    }
}

Since our generators implement the Iterator trait, we can compare two generators as two iterators with this little piece of code, making the assumption that the tree leaf values are never null:

    def sameFringe[T](tree1:Tree[T], tree2:Tree[T]):Boolean = {
        !((new TreeFringe(tree1)).zipAll(new TreeFringe(tree2),null,null).
            exists(p=>p._1!=p._2))
    }

Alternatively, we could use this more verbose version:

    def sameFringe[T](tree1:Tree[T], tree2:Tree[T]):Boolean = {
        val fringe1 = new TreeFringe(tree1)
        val fringe2 = new TreeFringe(tree2)
        while(fringe1.hasNext && fringe2.hasNext) {
            if (fringe1.next != fringe2.next)
                return false;
        }
        !(fringe1.hasNext || fringe2.hasNext)
    }

We add a SameFringe object with a main method that creates some test trees, prints out the leaves of each tree using our generator, then calls our sameFringe method to check for equality.

object SameFringe {
    def main(args:Array[String]) = {
        val t1 = Branch(Branch(Leaf(1),Leaf(2)),Leaf(3))
        val t2 = Branch(Leaf(1),Branch(Leaf(2),Leaf(3)))
        val t3 = Branch(Leaf(1),Branch(Leaf(2),Leaf(4)))
        println("t1:"); for (x <- (new TreeFringe(t1))) println(x)
        println("t2:"); for (x <- (new TreeFringe(t2))) println(x)
        println("t3:"); for (x <- (new TreeFringe(t3))) println(x)
        println("sameFringe(t1,t2)="+sameFringe(t1,t2))
        println("sameFringe(t1,t3)="+sameFringe(t1,t3))
    }
    //include the sameFringe method in this object
}

More Possibilities

Some other possible uses for generators or coroutines:

• Pipelines: A sequence of tasks can operate on a stream of data, with each task reading data from an input queue and writing to an output queue which is the input queue of the next task in the sequence.
• Fan-out: A single producer with multiple consumers can be implemented by using a fan-out coroutine that reads from its input queue and writes the same data to multiple output queues, each of which is the input queue to one of the multiple consumers.
• Fan-in: Multiple producers can use a single shared output queue so that the coroutine using that queue as its input queue receives data from multiple sources. If you stick with a single-thread scheduler, you don't have to worry about synchronization or other concurrent access issues on the shared queue. By combining Pipelines, Fan-out and Fan-in, we can create arbitrary networks of communicating coroutines.
• State machines: For any situation in which a task has to maintain state based on one or more inputs, a coroutine or generator can be used to allow some of that state to be stored as the location of current execution in the code, which often makes the code simpler to write and maintain.
• Parsers: A parser is a typical example of a producer that reads an input stream and maintains state. As the parser collects input characters (which could be provided by another coroutine in a pipeline) and resolves them into tokens, it writes them to its output queue where the tokens are available to the routine handling the next level of analysis.

Scala Coroutines

An implementation of coroutines on top of Scala's delimited continuations.

In my previous post I said that delimited continuations could be used to create interesting control constructs. In this post I give examples of one such construct: coroutines.

I describe the implementation of a library to make it easier to write coroutines, and I give an example that is built on that library. If you want to go straight to the example, see the section Producer-Consumer.

Like my previous post on delimited continuations, this is a long post. However, though long, it should be much easier going than that post. You don't need to read and understand that post in order to understand this post, but it will help in two places:

1. In the discussion of the CoScheduler API and implementation, an understanding of reset and shift is required.
2. You need to understand at some level about CPS code and its restrictions when writing coroutines or using the CPS-only methods in CoQueue and Blocker.

Contents

• Coroutines
• Why Use Coroutines
• Building a Coroutine Library
• Blocker
• CoQueue
• An Attempt to Avoid Blocking
• CoScheduler API
• CoScheduler Implementation
• DefaultCoScheduler
• Other Schedulers
• Producer-Consumer
• Resources

Coroutines

The Wikipdeia coroutines page says coroutines are a generalization of subroutines: whereas a subroutine starts at the beginning every time it is called, a coroutine starts at the beginning the first time it is called, but can start at any other point on successive calls. Generally this means that on successive calls, it continues running from the point where it most recently returned.

Instead of using a return statement, coroutines typically use a yield statement. A yield statement indicates that the routine is done executing for now, and will resume execution following the yield statement the next time it is called.

In the classic definition of coroutines, the yield statement indicates which coroutine is to be run next. With two coroutines, each always yields to the other; with more than two, a coroutine might have code to determine which other coroutine to yield to.

Unfortunately for coroutines, yield is already a keyword in Scala, so we can't use it for our purposes. We can either pick a slightly different word such as yld or yieldTo, or we can just use a different term altogether.

The classic example of the use of coroutines is a pair of routines, one of which (the producer) generates data and one of which (the consumer) consumes data. These two routines are connected by a queue; the producer puts data into the queue and the consumer takes data out of the queue.

This same producer-consumer pair is also a typical example of multi-thread code. Ted Neward uses this producer-consumer example in his blog post describing the concurrency benefits of using Scala.

In the multi-thread producer-consumer example, the producer thread runs and places data into the queue until the queue is full, at which point that thread stops running until the queue empties out enough for it to add more data. Meanwhile the consumer thread runs and takes data out of the queue until the queue is empty, at which point that thread stop running until the queue contains more data. If the host on which the multi-thread application is running contains more than one processor, both of these threads might be running at the same time.

I like to think of coroutines as being like multi-thread code, only without multiple threads. The coroutine version of the producer-consumer example works essentially the same as the multi-thread version, except that only one of the two is ever running at one point in time. The producer runs and places data into the queue until it is full, at which point it pauses and the consumer starts running. The consumer takes data out of the queue until it is empty, at which point it pauses and the producer starts running again.

In both the multi-thread and the coroutine version of this example, there is some state that is saved while each routine is paused waiting for the queue to fill or empty. In the multi-thread example, that state is saved in the thread. In our coroutine example, we use a different mechanism to save that state: a delimited continuation.

Why Use Coroutines

If coroutines are like multi-thread code, why use them and have to deal with continuations rather than just using threads? Here are some possible reasons:

• With the default scheduler that runs everything in one thread, you don't need to worry about concurrency issues such as multiple concurrent access to shared state.
• You can create your own scheduler to control when to run each of your coroutines. If you want that scheduler to use a thread pool and run coroutines concurrently, you can do that (assuming you then deal with concurrency issues in your coroutines).
• An application can handle more suspended continuations than it can suspended threads (for example, see slide 19 of Phillip Haller's presentation on Actors, where he says an app can handle up to 5,000 threads, but up to 1,200,000 actors).

Building a Coroutine Library

It is possible to write coroutines directly in Scala code using reset and shift, but dealing with delimited continuations can be tricky, so I wanted to isolate all of that code into a reusable library that would make it easier to write coroutines as well as allow encapsulating more sophisticated capabilities within the library. The package name I selected for this library is net.jimmc.scoroutine. The source code is available on github.

I started with a change that makes these coroutines look less like coroutines and more like the multi-thread model: rather than have a coroutine specify what other coroutine is to be run, I wanted to be able to specify only that the coroutine is ready to give up control. Essentially, rather than yielding to another coroutine, I always yield to a scheduler, and the scheduler selects and then yields to the next coroutine. Given such a scheduler (described below), we can create a few simple constructs on which to build our coroutine API.

Blocker

In the typical producer-consumer example, there is an explicit check to see if the routine is blocked, and if so, then a call to yield is made. I wanted something more generic, so I created an abstract trait Blocker to represent the condition that a routine could be blocked by something:

package net.jimmc.scoroutine

trait Blocker {
    def isBlocked: Boolean
}

The implementations of this for the producer and consumer are straightforward: for the producer, isBlocked returns true when the queue is full; for the consumer, isBlocked returns true when the queue is empty.

Given the isBlocked method, the typical coroutine always includes a code fragment that looks something like this:

while (isBlocked)
    yield control

Since we will always be yielding control to the scheduler, we can encapsulate this into a more convenient method, which I have called waitUntilNotBlocked. I added this function to my Blocker trait, and delegated it to a scheduler of type CoScheduler:

package net.jimmc.scoroutine

trait Blocker {
    val scheduler: CoScheduler  //class must override to provide instance
    def isBlocked: Boolean

    def waitUntilNotBlocked:Unit = {
        scheduler.waitUntilNotBlocked(this)
    }
}

We pass this to the scheduler so that it can call our isBlocked method and continue our execution only when isBlocked returns false.

There is one more detail to be added to Blocker, but it is an important one. I stated above that this implementation is built on top of delimited continuations. When we call waitUntilNotBlocked and we are blocked, we want the coroutine library to wait until we are no longer blocked and then continue execution of our routine. The coroutine library will be using delimited continuations to do this, and since our routine might be suspended, the signature for the waitUntilNotBlocked method must include the suspendable annotation. We add that annotation along with a suitable import statement to get our final version of Blocker:

package net.jimmc.scoroutine

import scala.util.continuations._

trait Blocker {
    val scheduler: CoScheduler  //class must override to provide instance
    def isBlocked: Boolean

    def waitUntilNotBlocked:Unit @suspendable = {
        scheduler.waitUntilNotBlocked(this)
    }
}

CoQueue

Once we have Blocker, it is easy to compose a blocking version of the standard Queue class (where by "blocking" I mean that the coroutine will be suspended until the specified Blocker is no longer blocked). We want a version of the Queue.enqueue function that blocks when the queue is full, and a version of the Queue.dequeue function that blocks when the queue is empty. We create a thin wrapper around the Queue class, which we call CoQueue, to implement our blocking methods for use with our coroutines. For each of our two blocking conditions, we create an instance of Blocker with those conditions as each of their isBlocked functions, and we use those Blocker instances to create our blockingEnqueue and blockingDequeue methods.

Because the blockingEnqueue and blockingDequeue methods might block, they must be annotated as suspendable, which means they can only be called from within CPS code. Here is our entire CoQueue class:

package net.jimmc.scoroutine

import scala.util.continuations._
import scala.collection.mutable.Queue

class CoQueue[A](val scheduler:CoScheduler, val waitSize:Int)
        extends Queue[A] { coqueue =>

    val enqueueBlocker = new Blocker() {
        val scheduler = coqueue.scheduler
        def isBlocked() = length >= waitSize
    }

    val dequeueBlocker = new Blocker() {
        val scheduler = coqueue.scheduler
        def isBlocked() = isEmpty
    }

    def blockingEnqueue(x:A):Unit @suspendable = {
        enqueueBlocker.waitUntilNotBlocked
        enqueue(x)
    }

    def blockingDequeue():A @suspendable = {
        dequeueBlocker.waitUntilNotBlocked
        dequeue
    }
}

An Attempt to Avoid Blocking

You might wonder if we could implement blockingEnqueue as follows so as to avoid the call to the scheduler's waitUntilNotBlocked method:

//this won't compile
    def blockingEnqueue(x:A):Unit @suspendable = {
        if (enqueueBlocker.isBlocked)
            enqueueBlocker.waitUntilNotBlocked
        enqueue(x)
    }

However, the compiler gives this error message:

CoQueue.scala:22: error: type mismatch;
 found   : Unit
 required: Unit @scala.util.continuations.cpsParam[Unit,Unit]
        if (enqueueBlocker.isBlocked)
        ^

Syntactically, the problem is that the above one-sided if statement is equivalent to

        if (enqueueBlocker.isBlocked)
            enqueueBlocker.waitUntilNotBlocked
        else
            ()

The type of () is Unit, but the type of waitUntilNotBlocked is Unit @suspendable, so there is a type mismatch.

You might think we could use some trickery such as this:

//compiles, but won't run properly
    def unitSuspendable:Unit @suspendable = ()

    def blockingEnqueue(x:A):Unit @suspendable = {
        if (enqueueBlocker.isBlocked)
            enqueueBlocker.waitUntilNotBlocked
        else
            unitSuspendable
        enqueue(x)
    }

This will compile without errors, but will not work properly. The problem is that we are trying to define one code path that is CPS (through waitUntilNotBlocked) and one path that is not (through unitSuspendable). The CPS compiler plugin transforms the code to use continuations, which, as you might recall from the discussion in my previous post, packages the rest of the function up in a continuation and passes it along to the called function. But the unitSuspendable expression does nothing with that continuation; it neither executes it nor saves it for later execution. Thus as soon as this code path is taken, the continuation - which represents the remainder of the execution of the entire delimited continuation, including the caller of this function - is dropped, and the whole delimited continuation is done.

CoScheduler API

The scheduler is the piece of the coroutine library that saves the continuations for all of the participating coroutines and determines when to execute those continuations. By design, all of the tricky stuff is encapsulated in this class. I have divided the discussion of the scheduler into two section: this first section discusses at a high level the tasks for which the schedule must take responsibility, and defines an API to perform those tasks. The following section describes the implementation of that API. If you don't want to get too far into the delimited continuation stuff, you can read just this section and skip the implementation section.

I started with the requirements that it should be possible to write a scheduler that:

1. uses one thread for all participating coroutines, or uses a thread pool so that multiple coroutines can run at once;
2. uses a simple algorithm to select which coroutine to run next, such as round-robin, or a more complex algorithm, such as a priority-based approach;
3. can be instantiated multiple times so that different collections of coroutines can be managed with different schedulers.

In order to allow all of the above, the scheduler API is defined in a trait, which I call CoScheduler. CoScheduler needs to define three basic functions:

1. Register a coroutine. I chose to do this with a method that accepts a block of code which is the coroutine body. Since the coroutine body might be suspended, the signature for that argument must include the suspendable annotation. I call this method addRoutine. To improve tracing, I also pass in a name argument that can be used to identify that coroutine.
2. Wait until a coroutine is no longer blocked. This is the method to which Blocker.waitUntilNotBlocked will delegate. It is also called waitUntilNotBlocked, and takes an argument which is the Blocker whose isBlocked method will be used to make that determination. Since this method will be called from the coroutine body or a method called from that body, it must be marked as suspendable.
3. Run a coroutine. There are two flavors of this: run a single step, returning as soon as one coroutine has run and has returned control to the scheduler, or run as many steps as possible, until there are no more unblocked coroutines to run. I call these two methods runNextUnblockedRoutine and runUntilBlockedOrDone, respectively. Since these methods are meant to be called from the main application, not from within a coroutine, they are not marked as suspendable.
   
   When one of the run methods returns, we would like to know whether there are some blocked coroutines, all of our coroutines have completed, or we don't know which because we only ran one routine. In order to return this indication, we define a sealed class RunStatus with three case objects representing these three possibilities.

The API of our CoScheduler trait thus looks like this:

package net.jimmc.scoroutine

sealed abstract class RunStatus
case object RanOneRoutine extends RunStatus
case object SomeRoutinesBlocked extends RunStatus
case object AllRoutinesDone extends RunStatus

trait CoScheduler {
    def addRoutine(name:String)(body: => Unit @suspendable)

    def waitUntilNotBlocked(b:Blocker):Unit @suspendable

    def runNextUnblockedRoutine():RunStatus
    def runUntilBlockedOrDone():RunStatus
}

If you are interested in using the scoroutine library to write coroutines, but you don't care how it works internally, then you can skip the next few sections and pick up again at Producer-Consumer.

CoScheduler Implementation

The reason for implementing CoScheduler as a trait rather than a class is to make it easier to create multiple implementations. On the other hand, there is some functionality which is likely to be the same in all implementations, and since Scala allows us to include code in traits, we will add the implementation of those methods to the trait.

For example, given the runNextUnblockedRoutine method, the runUntilBlockedOrDone method is a simple loop that calls runNextUnblockedRoutine until it does not run something.

Likewise, if we internally create an instance of a Blocker in addRoutine, we can implement both that method and waitUntilNotBlocked on top of a single method that stores a continuation, which we will call setRoutineContinuation.

This means we can create a concrete scheduler class that extends the CoScheduler trait by implementing just two functions: runNextUnblockedRoutine and setRoutineContinuation.

Below is what our implementation of CoScheduler looks like after making the above changes.

package net.jimmc.scoroutine

import scala.util.continuations._

sealed class RunStatus
case object RanOneRoutine extends RunStatus
case object SomeRoutinesBlocked extends RunStatus
case object AllRoutinesDone extends RunStatus

trait CoScheduler { cosched =>
    private[scoroutine] def setRoutineContinuation(
            b:Blocker, cont:Option[Unit=>Unit]):Unit
    def runNextUnblockedRoutine():RunStatus

    /* We use a class rather than an object because we are using the
     * instance as a key to find more info about the associated routine. */
    class BlockerNever() extends Blocker {
        val scheduler = cosched
        val isBlocked = false
    }

    def addRoutine(name:String)(body: => Unit @suspendable) {
        reset {
            val blocker = new BlockerNever()
            waitUntilNotBlocked(blocker)
            body
        }
    }

    def runUntilBlockedOrDone():RunStatus = {
        var status:RunStatus = RanOneRoutine
        while (status==RanOneRoutine) {
            status = runNextUnblockedRoutine()
        }
        status
    }

    def waitUntilNotBlocked(b:Blocker):Unit @suspendable = {
        shift( (cont: Unit=>Unit) => {
            setRoutineContinuation(b,Some(cont))
        })
    }
}

As we already knew based on the existence of the suspendable annotation, there are two methods that deal with CPS code: addRoutine and waitUntilNotBlocked.

Before examining these functions, it is worth reviewing one point about how reset and shift work. When there is a shift call within a reset block, the CPS compiler plugin transforms the code within the reset block such that the code after the shift block is passed as a continuation argument to the shift block. The code within the shift block is thus the last code to be executed within the reset block. The code that is within the reset but outside of the shift is CPS code, but the code within the shift block is not CPS code. In other words, once within a reset block, we are executing CPS code until we get into the shift block, at which point we are no longer executing CPS code. The continuation contains CPS code; if we call the continuation from within our shift code, we transition from non-CPS code into CPS code. But if we happen to save the continuation, we can directly execute it later from any non-CPS code, and that will likewise transition us into the CPS code of the continuation.

The waitUntilNotBlocked function takes as an argument the Blocker that will tell us when the calling coroutine is allowed to run again, and, along with the continuation passed in to the shift, passes it to setRoutineContinuation. That function saves the continuation and its associated Blocker and returns without executing the continuation. Because we are returning without executing the continuation, control returns to the first statement past the end of the enclosing reset block, or, if the current code/continuation (i.e. the CPS code containing the call to waitUntilNotBlocked) was directly executed from non-CPS code, to the statement after the point at which that continuation was executed.

The addRoutine function creates a Blocker that never blocks, then calls waitUntilNotBlocked with that blocker. Since waitUntilNotBlocked is a CPS function (marked with the suspendable annotation), the remainder of the code in the reset block is turned into a continuation and passed along to waitUntilNotBlocked. When that method calls shift, the continuation we passed to waitUntilNotBlocked - i.e. our call to body - is part of the continuation passed to the shift. Thus when that method saves the continuation, that saved continuation includes the call to the coroutine body. Since the continuation is not immediately executed, control returns to the end of the reset block, and we return from addRoutine with our coroutine sitting in the scheduler ready to start running.

DefaultCoScheduler

Given the CoScheduler trait described above, the only functionality that remains for our concrete class is to implement a mechanism for storing continuations and selecting the next one to run. The DefaultCoScheduler implements a simple round-robin scheduling mechanism, selecting the next runnable continuation each time it is invoked. Note that this implementation has been designed to be simple, but is not very efficient. In particular, it will not exhibit good performance when there are a large number of coroutines of which only a few are runnable at any time.

We define a case class BlockerInfo to tie together a Blocker and its associated continuation, an ArrayBuffer to store an ordered set of those, and a map to find one given a Blocker.

The setRoutineContinuation function adds a new BlockerInfo to our array if we don't already have one for the given Blocker, or updates the existing one if we do.

The runNextUnblockedRoutine function does a simple linear scan through the array of items, starting just past where we left off the last time, looking for the first unblocked continuation and running it. If there were no runnable continuations, we return a status code without running anything.

package net.jimmc.scoroutine

import scala.collection.mutable.ArrayBuffer
import scala.collection.mutable.HashMap

class DefaultCoScheduler extends CoScheduler {
    val blockerIndexMap = new HashMap[Blocker,Int]
    case class BlockerInfo(val blocker:Blocker, index:Int,
            var cont:Option[Unit=>Unit])
    val blockerList = new ArrayBuffer[BlockerInfo]
    var nextIndex = 0

    private[scoroutine] def setRoutineContinuation(
            b:Blocker,cont:Option[Unit=>Unit]) {
        if (blockerIndexMap.get(b).isEmpty) {
            val nextIndex = blockerIndexMap.size
            blockerIndexMap.put(b,nextIndex)
            blockerList += BlockerInfo(b, nextIndex, cont)
        } else {
            val n = blockerIndexMap(b)
            blockerList(n).cont = cont
        }
    }

    def runNextUnblockedRoutine():RunStatus = {
        var blockedCount = 0
        for (i <- 0 until blockerList.size) {
            val index = (nextIndex + i) % blockerList.size
            val bInfo = blockerList(index)
            if (bInfo.cont.isDefined && bInfo.blocker.isBlocked) {
                blockedCount += 1
            }
            if (bInfo.cont.isDefined && !bInfo.blocker.isBlocked) {
                nextIndex = index + 1
                val nextCont = bInfo.cont
                bInfo.cont = None
                nextCont.get()          //run the continuation
                return RanOneRoutine
            }
        }
        if (blockedCount > 0) {
            SomeRoutinesBlocked
        } else {
            AllRoutinesDone
        }
    }
}

Note that DefaultCoScheduler does not import the continuations package. It does not use reset, shift, or any of the CPS annotations such as suspendable. This is because none of this code is CPS code. runNextUnblockedRoutine is called from non-CPS code, and although setRoutineContinuation is called from CPS code, it does not itself call any CPS functions nor does it use shift, so it does not need to be declared as CPS code.

Other Schedulers

DefaultCoScheduler implements a basic scheduling algorithm. It is intended for use with small numbers of coroutines and has not been optimized. Other schedulers could be written that are optimized for other situations, such as large numbers of coroutines, coroutines with different priorities, or "stickiness" so that a running coroutine continues to run until it is blocked before the next coroutine runs. Since the code that creates the coroutines starts by creating the scheduler that controls those coroutines, it would be simple to create a scheduler other than DefaultCoScheduler for use with a particular set of coroutines.

Producer-Consumer

Let's see how the Producer-Consumer example (ProdConTest) looks using the scoroutine library:

import scala.util.continuations._

import net.jimmc.scoroutine.DefaultCoScheduler
import net.jimmc.scoroutine.CoQueue

object ProdConTest {
    def main(args:Array[String]) = {
        val prodcon = new ProduceAndConsume()
        prodcon.run
    }
}

class ProdCon() {
    val sched = new DefaultCoScheduler
    val buf = new CoQueue[Int](sched,2)

    def run() {
        sched.addRoutine("producer"){
            var i = 0
            while (i < 4) {
                buf.blockingEnqueue(i)
            }
        }
        sched.addRoutine("consumer"){
            val total = buf.blockingDequeue +
                    buf.blockingDequeue + buf.blockingDequeue
            println("consume total is "+total)
        }
        sched.runUntilBlockedOrDone
    }
}

After the imports and a simple main method for testing, we have the ProdCon class with the actual producer-consumer definition.

We start by setting up two objects: the scheduler that will control our two coroutines, and a queue for communication between them. We then register two coroutines with our scheduler, one for the producer and one for the consumer, and we call sched.runUntilBlockedOrDone to run the coroutines until there is nothing runnable left on that scheduler.

You can't tell in this example, but the code blocks being passed to addRoutine are CPS code, the same as the body of a reset block. If you decide to refactor this code and push the blockingEnqueue or blockingDequeue calls down into a subroutine, that subroutine will have to be marked with the suspendable annotation. Also, because coroutine bodies are CPS code, there are there are some restrictions on what control constructs can be used. You can see what this looks like in the ProdConTestWithSubs source code in the scoroutine examples.

I will give some more examples in my next post, a followup about Generators.

Resources

• My original blog post on Delimited Continuations, which includes its own Resources section.
• The net.jimmc.scoroutine library on github.
• python-multitask, a similar framework for Python.