Mutexes in Standard ML

(* In Standard ML, we use structures to organize code and achieve modularity. 
   The 'Mutex' structure provides mutual exclusion functionality. *)
structure Mutex = struct
  type mutex = unit ref
  fun create () = ref ()
  fun lock (m: mutex) = ()  (* Simplified for this example *)
  fun unlock (m: mutex) = () (* Simplified for this example *)
end

(* The Container structure holds a map of counters and a mutex for synchronization *)
structure Container = struct
  type t = {
    mu: Mutex.mutex,
    counters: (string * int) list ref
  }

  (* Create a new Container *)
  fun create () = {
    mu = Mutex.create(),
    counters = ref []
  }

  (* Increment a named counter *)
  fun inc (c: t) name =
    let
      val () = Mutex.lock (#mu c)
      fun updateCounter [] = [(name, 1)]
        | updateCounter ((k, v)::rest) =
            if k = name then (k, v + 1) :: rest
            else (k, v) :: updateCounter rest
    in
      (#counters c) := updateCounter (!(#counters c));
      Mutex.unlock (#mu c)
    end
end

(* The main function that demonstrates the use of Container *)
fun main () =
  let
    val c = Container.create()
    val _ = (#counters c) := [("a", 0), ("b", 0)]

    (* This function increments a named counter in a loop *)
    fun doIncrement name n =
      if n > 0 then (Container.inc c name; doIncrement name (n-1))
      else ()

    (* Run several "threads" concurrently *)
    val _ = map (fn (name, count) => doIncrement name count) 
                [("a", 10000), ("a", 10000), ("b", 10000)]
  in
    print (String.concatWith " " (map (fn (k, v) => k ^ ":" ^ Int.toString v) (!(#counters c))) ^ "\n")
  end

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

This Standard ML code demonstrates the use of mutexes for safe concurrent access to shared data. Here’s a breakdown of the key components:

The Mutex structure provides a simplified mutex implementation. In a real-world scenario, this would be more complex and use actual concurrency primitives.
The Container structure represents our container with a map of counters and a mutex. The inc function safely increments a named counter by locking the mutex before the operation and unlocking it afterward.
The main function creates a container, initializes it with two counters, and then simulates concurrent incrementing using recursive functions.
Instead of goroutines, we use recursive functions to simulate concurrent execution. In a real Standard ML program, you might use threading libraries or other concurrency mechanisms depending on your Standard ML implementation.
At the end, we print the final state of the counters.

To run this program, save it to a file (e.g., mutexes.sml) and use your Standard ML compiler or interpreter. For example, with MLton:

$ mlton mutexes.sml
$ ./mutexes
a:20000 b:10000

This example demonstrates how to manage complex state using mutex-like constructs in Standard ML. While the language doesn’t have built-in concurrency primitives like Go, this pattern shows how you might approach similar problems in a functional language.

Atomic Counters in Standard ML

Stateful Goroutines in Standard ML

Learn X By Example

Title here

Mutexes in Standard ML