Channel Synchronization in Standard ML

(* We can use channels to synchronize execution across threads. 
   Here's an example of using a blocking receive to wait for a thread to finish. *)

(* Import necessary modules *)
structure Time = Time
structure TextIO = TextIO

(* This is the function we'll run in a separate thread. The
   'done' channel will be used to notify another thread
   that this function's work is done. *)
fun worker (done: bool Channel.chan) =
    let
        val _ = TextIO.print "working..."
        val _ = Time.sleep (Time.fromSeconds 1)
        val _ = TextIO.print "done\n"
    in
        Channel.send (done, true)
    end

(* Main function *)
fun main () =
    let
        (* Start a worker thread, giving it the channel to notify on *)
        val done = Channel.newChannel () : bool Channel.chan
        val _ = Thread.spawn (fn () => worker done)
    in
        (* Block until we receive a notification from the worker on the channel *)
        Channel.recv done;
        ()
    end

(* Run the main function *)
val _ = main ()

This Standard ML code demonstrates channel synchronization, which is similar to the concept in the original example. Here’s a breakdown of the translation:

1. We use the Channel module to create and manage channels, which are similar to channels in the original example.

2. The worker function is defined to simulate some work and then send a signal on the done channel.

3. In the main function, we create a new channel and spawn a new thread to run the worker function.

4. We then use Channel.recv to block and wait for the worker to finish, similar to the <-done in the original example.

5. The Time.sleep function is used to simulate work being done, similar to time.Sleep in the original.

To run this program, you would typically save it in a file with a .sml extension and use an Standard ML compiler or interpreter. For example, using the MLton compiler:

$ mlton channel-synchronization.sml
$ ./channel-synchronization
working...done

If you removed the Channel.recv done line from this program, the program would exit before the worker even started, similar to the behavior described in the original example.

Note that Standard ML’s concurrency model is different from some other languages, and the exact behavior may depend on the specific Standard ML implementation you’re using. This example assumes the existence of a Channel module and Thread.spawn function, which might need to be imported or defined separately in some Standard ML environments.