<subject change>

On Sun, Sep 29, 2013 at 3:31 AM, Mike Izbicki <mike@izbicki.me> wrote:
I've got a Partitionable class that I've been using for this purpose:
https://github.com/mikeizbicki/ConstraintKinds/blob/master/src/Control/ConstraintKinds/Partitionable.hs

Mike -- Neat, that's a cool library!

Edward --  ideally, where in the standard libraries should the Partitionable comonoid go?  

Btw, I'm not sure what the ideal return type for comappend is, given that it needs to be able to "bottom out".  Mike, our partition function's list return type seems more reasonable.  Or maybe something simple would be this:

class Partitionable t where
  partition :: t -> Maybe (t,t)

That is, at some point its not worth splitting and returns Nothing, and you'd better be able to deal with the 't' directly.

So what I really want is for the containers package to please get some kind of Partitionable instances!  Johan & others, I would be happy to provide a patch if the class can be agreed on. This is important because currently the balanced tree structure of Data.Set/Map is an amazing and beneficial property that is not exposed at all through the API.  
   For example, it would be great to have a parallel traverse_ for Maps and Sets in the Par monad.  The particular impetus is that our new and enhanced Par monad makes extensive use of Maps and Sets, both the pure, balanced ones, and lockfree/inplace ones based on concurrent skip lists:

    http://www.cs.indiana.edu/~rrnewton/haddock/lvish/

Alternatively, it would be ok if there were a "Data.Map.Internal" module that exposed the Bin/Tip, but I assume people would rather have a clean Partitionable instance...

Best,
  -Ryan


On Sun, Sep 29, 2013 at 3:31 AM, Mike Izbicki <mike@izbicki.me> wrote:
I've got a Partitionable class that I've been using for this purpose:
The function called "parallel" in the HLearn library will automatically parallelize any homomorphism from a Partionable to a Monoid.  I specifically use that to parallelize machine learning algorithms.

I have two thoughts for better abstractions:

1)  This Partitionable class is essentially a comonoid.  By reversing the arrows of mappend, we get:

comappend :: a -> (a,a)

By itself, this works well if the number of processors you have is a power of two, but it needs some more fanciness to get things balanced properly for other numbers of processors.  I bet there's another algebraic structure that would capture these other cases, but I'm not sure what it is.

2) I'm working with parallelizing tree structures right now (kd-trees, cover trees, oct-trees, etc.).  The real problem is not splitting the number of data points equally (this is easy), but splitting the amount of work equally.  Some points take longer to process than others, and this cannot be determined in advance.  Therefore, an equal split of the data points can result in one processor getting 25% of the work load, and the second processor getting 75%.  Some sort of lazy Partitionable class that was aware of processor loads and didn't split data points until they were needed would be ideal for this scenario.

On Sat, Sep 28, 2013 at 6:46 PM, adam vogt <vogt.adam@gmail.com> wrote:
On Sat, Sep 28, 2013 at 1:09 PM, Ryan Newton <rrnewton@gmail.com> wrote:
> Hi all,
>
> We all know and love Data.Foldable and are familiar with left folds and
> right folds.  But what you want in a parallel program is a balanced fold
> over a tree.  Fortunately, many of our datatypes (Sets, Maps) actually ARE
> balanced trees.  Hmm, but how do we expose that?

Hi Ryan,

At least for Data.Map, the Foldable instance seems to have a
reasonably balanced fold called fold (or foldMap):

>  fold t = go t
>    where   go (Bin _ _ v l r) = go l `mappend` (v `mappend` go r)

This doesn't seem to be guaranteed though. For example ghc's derived
instance writes the foldr only, so fold would be right-associated for
a:

> data T a = B (T a) (T a) | L a deriving (Foldable)

Regards,
Adam
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