Thursday, 14 November 2013

lambdas to the slaughter ...

Had a look at a couple of the videos from the AMD developer summit going on at the moment. One was about how Java is a pretty good fit with the heterogeneous (i'm a bit sick of typing that word already) world. A short demo by Gary Frost of aparapi fame got me to finally pull my finger out and finally have a look at lambdas and how they work. Half an hour of hacking later and I think i've pretty much sussed them out for what i'm interested in!

As i've mentioned several times on the blog and elsewhere i'm pretty excited about the possibilities HSA provide, and i'm still surprised at how good a fit Java is for it - all because of the JVM and that 'pesky' bytecode and a few -very well thought out- language extensions. Until now I just haven't really had the time to look into them and have been limited by using JDK 7 as well. I'm also worried that once I use it I wont want to go back to the old way of doing things ...

I'm still using netbeans 7.3 so the lambda support is shit (totally nonexistant) but I played a little bit with a few things ...

Took me a while to realise when you iterate an array you don't iterate the items but the indices, but once that was out of the way it was plain sailing. Also had a look at 2d iteration as well. Some surprising results.

So a simple loop:

  for (int i=0;i<a.length;i++) {
    a[i] = sqrt(a[i] * b[i]);
Can become:
  IntStream.range(0, a.length).forEach(i -> a[i] = sqrt(a[i] * b[i]));
(I'm not really a big fan of the syntax which hides so many details, but whatever).

Knowing that the lambda expression is converted to a private function suggests it should run slower, but thanks to the jvm ... it runs just about as fast as the simple array - infact with some tests it was slightly faster (oddly). Which is nice - because simple arrays are fast.

However the real benefit comes when you can then utilise all cores on your cpu ... (or eventually ... gpu) ...

  IntStream.range(0, a.length).parallel().forEach(i -> a[i] = sqrt(a[i] * b[i]));

Now it uses all CPU cores available on the machine and executes appropriately faster. Well that was hard?

So what about 2D loops? The supplied streams only create 1D sequences.

A typical 2D processing loop:

  float[] values;
  int width;
  int height;

  for (int y=0;y<height;y++) {
    for (int x=0;x<width;x++) {
      float v = values[x+y*width];
      .. do something ..
Which is simple enough but if you type it several times a day for weeks it gets a bit tiring (i'm pretty fucking tired of it). And I rarely even bother to parallelise these things because it's just too much work and I keep writing new code too rapidly. I suppose I could come up with some class to encapsulate that and use a callback, but then it becomes a bit of a pain to use due to finals or an explosion in one-off worker classes.

In a lot of cases 1D operations as above on 2D arrays suffice (when the coordinates don't matter) but sometimes one needs the coordinates too. So my first-cut-worked-first-time approach was just to create a '2D' consumer interface and map the 1D index to 2D using the obvious maths:

  public interface Consumer2D {
    void accept(int x, int y);

  public class Array2D {
    float[] values;
    int width;
    int height;

    public void parallelForeach(Consumer2D ic) {
      IntStream.range(0, width*height).parallel().forEach(i -> {
        int x = i % width;
        int y = i / width;

        ic.accept(x, y);

    public float get(int x, int y) {
      return values[x+y*width];

    a.parallelForeach((x, y) -> {
      float v = a.get(x,y);
      ... do something ...

Now one would think all that extra maths would make it "a bit slow", but at least for my simple tests the JVM must be optimising it to pretty much the same code as it executes at the same speed ... as the straight 1D version!


One still has to be somewhat cognent of the pitfalls of concurrent processing so it doesn't really make the solutions any easier to come up with, but at least it throws out a pile ... a big big pile ... of boilerplate ... which means you don't even have to think about the mechanics anymore and can focus on the maths. And that's only talking about CPU resources, trying to leverage a GPU is even worse (well in some respects it's easier because the job concurrency is automatic, but in other's it's much more painful do the native api's and data conversion). I still think there will be applications where OpenCL is useful (all that LDS bandwidth) - hopefully HSA will make that work nicer with Java as well in the future.

Damn, once I get used to this, Android and it's fucked up ancient shitty version of Java-esque is going to suck even more than it does already.

The other thing I still have to wait for is that HSA capable hardware, hopefully a decent minipc / laptop is available in Australia when they finally arrive early next year. And that it all works properly in Linux.

There's also an effort to port the same stuff to the parallella board, and it will be interesting to see how well that works in practice. I'm keeping an eye on it but it's a bit out of my area of expertise/current interests to help more than that right now.

Who'd have thunk Java being completely in it's own league when it came to support for massive parallelism and the high performance it can provide?

PS on another note it's interesting to see the latest GPUs are becoming completely bounded by both power and heat requirements - given the designs are now quite mature and advanced and there isn't much scope for performance increases due to architectural improvements as there has been in the past. Has a practical total-flop ceiling been hit outside of process changes (and how much can they even provide with the head dissipation issue)? The move to trying to improve utilisation via software improvements - HSA, Mantle, and so on - will only help so far - the more efficiently you utilise these chips the hotter they get too. Food for thought.

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