Back in September 2016, I was lucky enough to attend a talk from Ben Deane entitled std::accumulate: Exploring an Algorithmic Empire. In this talk, he presents the unknown possibilities of std::accumulate, an algorithm also known as fold or reduce depending on the language.

The arguments were already quite compelling… then came the slide in which he announced he had implemented almost all the algorithms of the STL based on std::accumulate (41st minute in the video).

From the Haskell world, I already knew most one-pass algorithm could be implemented in terms of a fold. But there are algorithms in the list that are not simple linear scan, like std::stable_sort. How is it possible?

I searched his blog and could not find a hit on how he did it. So I write this post to describe the solution I found. I hope you will find it as elegant as I do.

### Small ranges and binary counters

The solution relies on using a binary counter like data-structure. Our binary counter will hold at each level L a range of size 2^L. The binary counter will thus be limited in size to log(N) if N is the number of elements of the collection to sort.

We populate the binary counter by successively adding ranges of size one to the counter. Each time we have a collision of “bit” (range of the same size), we merge them and consider it as a “carry”. We keep propagating the carry up the levels, until we reach an empty slot.

This is best explained by an example:

```Initially
Counter = [[1, 2], [3, 4, 5, 6]]

Counter = [, [1, 2], [3, 4, 5, 6]]

Counter = [merge  and , [1, 2], [3, 4, 5, 6]]
Counter = [merge [7, 8] and [1, 2], [3, 4, 5, 6]]
Counter = [merge [1, 2, 7, 8] and [3, 4, 5, 6]]
Counter = [[1, 2, 3, 4, 5, 6, 7, 8]]

Adding elements 9, 10 and 11
Counter = [, [9, 10], [1, 2, 3, 4, 5, 6, 7, 8]]
```

At this point, we are almost done, but not quite. We still need to collapse all the remaining “bits” of our counter into one answer. We do this by accumulating over these “bits” with a merge operation.

```[, [9, 10], [1, 2, 3, 4, 5, 6, 7, 8]]
[[9, 10, 11], [1, 2, 3, 4, 5, 6, 7, 8]]
[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]]
```

We are done! Let us try to implement this in Haskell.

The implementation in Haskell follows pretty closely the description we gave of the solution in the previous paragraph.

The main difference lies in the use of an auxiliary integer to keep the length of the list. Had we not do that, our solution would have been much less efficient: computing the length of a list in Haskell is linear in the size of the list.

 foldMergeSort :: (Ord a) => [a] -> [a] foldMergeSort = foldl1 (flip merge) . map snd . foldl addToCounter [] where addToCounter counter x = propagate ((1::Int,[x]) : counter) propagate [] = [] propagate [x] = [x] propagate counter@(x:y:xs) -- x arrived last => combine on right | fst x == fst y = propagate ((fst x + fst y, merge (snd y) (snd x)) : xs) | otherwise = counter
• The head of the counter list is the lowest level bit
• The function propagate implements the carry propagation
• The map snd allows to get rid of the integer holding the list length
• The foldl1 (flip merge) implements the collapse of the counter

To ensure our merge sort is stable, ranges that came last must be passed as right parameters to the merge function. This is the reason why we collapse our counter with flip merge instead of merge that would also type check.

This implementation assumes the existence of a merge function that combines two sorted lists into one bigger sorted list. You can implement it as follows (it requires a bit of care to make it stable):

 merge :: (Ord a) => [a] -> [a] -> [a] merge xs [] = xs merge [] ys = ys merge l@(x:xs) r@(y:ys) | y < x = y : merge l ys -- Keeps it stable | otherwise = x : merge xs r

As map (std::transform in the STL) can be itself implemented in terms of fold (std::accumulate), this is one way to implement a “out of place” stable sort in Haskell based only on folds and composition of functions.

### Generalizing to Monoids

Looking back at the code of our solution, we can observe that this algorithm would work for any Monoid. So we can make our algorithm more general, and fold any Monoid following a tree like structure.

 foldMonoidTree :: (Monoid a) => [a] -> a foldMonoidTree = foldl1 (flip (<>)) . map snd . foldl addToCounter [] where addToCounter counter x = propagate ((1::Int,x) : counter) propagate [] = [] propagate [x] = [x] propagate counter@(x:y:xs) -- x arrived last => combine on right | fst x == fst y = propagate ((fst x + fst y, snd y <> snd x) : xs) | otherwise = counter

To get our stable merge sort back, we can create a type for sorted lists, and make it an instance of Monoid. Here is one possible implementation:

 newtype SortedList a = SortedList { unSortedList :: [a] } instance (Ord a) => Monoid (SortedList a) where mempty = SortedList [] mappend (SortedList a) (SortedList b) = SortedList \$ merge a b foldMergeSort :: (Ord a) => [a] -> [a] foldMergeSort = unSortedList . foldMonoidTree . map (\x -> SortedList [x])

### Conclusion and what’s next

I was quite astonished how elegant the solution was and how simple the resulting code. But somehow, you should feel unsatisfied with the performance.

I cheated. I did not use C++ as the original presentation did. And this implementation of stable_sort creates a new list. Haskell did not give me the choice.

So next time we will have a look at an implementation in C++ that stable sorts the container provided as input.