With modern hardware utilizing multiple cores, it can be highly advantageous to do as much parallel processing as possible. I think the most elegant way of doing this is to use thread pools which allocate tasks to a limited number of threads. Unfortunately, multi-threading support isn’t fully implemented in Haxe—but it is on the neko and cpp targets, so I wrote a simple thread pool to take advantage of multi-threading on those platforms!

The ThreadPools class is pretty easy to use, and I’ve added some features which enable you to leave it in your source code despite what platform you’re compiling against—if you compile against neko or cpp, full multi-threading will be enabled; if you compile against anything else, the class will still work & compile fine, it just won’t run multi-threaded.

Here’s some sample code which runs two tasks which take a different amount of time to run:

// this will create a thread pool with 8 threads on neko and cpp platforms
// on all other platforms, no threads will be created
// and the pool will use the main thread
var threadPool:ThreadPool = new ThreadPool(8);

// add a task that will take a while to complete
threadPool.addTask(function(x:Dynamic):Int {
    var li:Int = 0;
    for (i in 0...10)
    {
        li += i;
        for(n in 0...10000) {}
    }
    return li;
}, null, onFinish);

// add a task that returns right away
threadPool.addTask(function(x:Dynamic):String {
    return "herp derp";
}, null, onFinish);

// this is a blocking call that will run all the tasks
// across the pool's threads
// or just in the main thread if not on neko or cpp
threadPool.blockRunAllTasks();

// ...

// report the results of the above tasks
private function onFinish(x:Dynamic):Void
{
    trace(x);
}

On neko or cpp, this will output:

herp derp
45

Since the “herp derp”-returning class will finish much sooner than the summing task when they’re both running in parallel.

On all other platforms, this will output:

45
herp derp

Since the tasks will be executed in the order they were added.

The code is available in a Gist: hamaluik / ThreadPool.hx.