Thank you! This identifies a space leak in base which went unnoticed for 7 years.

Your implementation can be improved further. Instead of splitting into pairs, you could instead split into lists of sorted sublists by replacing the pairs function with the following

    pair = foldr f []
      where
        f x [] = [[x]]
        f x (y:ys)
          | x `cmp` head y == LT = (x:y):ys
          | otherwise            = [x]:y:ys

This should give you the same performance improvements for sorting random lists, but better performance while sorting ascending lists.

The version in base takes it one step further by using a DList to handle the descending case efficiently as well, except there's a space leak right now because of which it is slower.

On Sun, Mar 26, 2017 at 7:21 AM, Gregory Popovitch <greg7mdp@gmail.com> wrote:

Motivation:
----------

Data.List.sort is a very important functionality in Haskell. I believe that
the proposed implementation is:

- significantly faster than the current implementation on unsorted lists,
typically 14% to 27% faster
- more laziness-friendly, i.e.:
    take 3 $ sort l
  will require significantly less comparisons than the current
implementation

Proposed Implementation
-----------------------

sort :: (Ord a) => [a] -> [a]
sort =  sortBy compare

sortBy cmp [] = []
sortBy cmp xs = head $ until (null.tail) reduce (pair xs)
  where
    pair (x:y:t) | x `cmp` y == GT  = [y, x] : pair t
                 | otherwise        = [x, y] : pair t
    pair [x] = [[x]]
    pair []  = []

    reduce (v:w:x:y:t) = merge v' x' : reduce t
                         where v' = merge v w
                               x' = merge x y

    reduce (x:y:t) = merge x y : reduce t
    reduce xs      = xs

    merge xs []           = xs
    merge []  ys          = ys
    merge xs@(x:xs') ys@(y:ys')
         | x `cmp` y == GT  = y : merge xs  ys'
         | otherwise        = x : merge xs' ys


Effect and Interactions
-----------------------

I have a stack project with a criterion test for this new implementation,
available at https://github.com/greg7mdp/ghc-sort.
I ran the tests on an Ubuntu 14.0.2 VM and GHC 8.0.2, and had the following
results:

- sorting of random lists of integers is 27% faster
- sorting of random lists of strings is 14% faster
- sorting of already sorted lists is significantly slower, but still much
faster than sorting random lists
- proposed version is more laziness friendly. For example this version of
sortBy requires 11 comparisons to find
  the smallest element of a 15 element list, while the default
Data.List.sortBy requires 15 comparisons.
  (see
https://github.com/greg7mdp/ghc-sort/blob/master/src/sort_with_trace.hs)


Test results
------------

Criterion output (descending/ascending results are for already sorted
lists).
I barely understand what Criterion does, and I am puzzled with the various
"T" output - maybe there is a bug in my bench code:

vagrant@vagrant-ubuntu-trusty-64:/vagrant$ stack exec ghc-sort
benchmarking ascending ints/ghc
TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTtime                 160.6 ms   (153.4
ms .. 167.8 ms)
                     0.997 R²   (0.986 R² .. 1.000 R²)
mean                 161.7 ms   (158.3 ms .. 165.9 ms)
std dev              5.210 ms   (3.193 ms .. 7.006 ms)
variance introduced by outliers: 12% (moderately inflated)

benchmarking ascending ints/greg
TTTTTTTTTTTTTTTTtime                 473.8 ms   (398.6 ms .. 554.9 ms)
                     0.996 R²   (0.987 R² .. 1.000 R²)
mean                 466.2 ms   (449.0 ms .. 475.0 ms)
std dev              14.94 ms   (0.0 s .. 15.29 ms)
variance introduced by outliers: 19% (moderately inflated)

benchmarking descending ints/ghc
TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTtime                 165.1 ms   (148.2
ms .. 178.2 ms)
                     0.991 R²   (0.957 R² .. 1.000 R²)
mean                 158.7 ms   (154.0 ms .. 164.3 ms)
std dev              7.075 ms   (4.152 ms .. 9.903 ms)
variance introduced by outliers: 12% (moderately inflated)

benchmarking descending ints/greg
TTTTTTTTTTTTTTTTtime                 471.7 ms   (419.8 ms .. 508.3 ms)
                     0.999 R²   (0.995 R² .. 1.000 R²)
mean                 476.0 ms   (467.5 ms .. 480.0 ms)
std dev              7.447 ms   (67.99 as .. 7.865 ms)
variance introduced by outliers: 19% (moderately inflated)

benchmarking random ints/ghc
TTTTTTTTTTTTTTTTtime                 2.852 s    (2.564 s .. 3.019 s)
                     0.999 R²   (0.997 R² .. 1.000 R²)
mean                 2.812 s    (2.785 s .. 2.838 s)
std dev              44.06 ms   (543.9 as .. 44.97 ms)
variance introduced by outliers: 19% (moderately inflated)

benchmarking random ints/greg
TTTTTTTTTTTTTTTTtime                 2.032 s    (1.993 s .. 2.076 s)
                     1.000 R²   (1.000 R² .. 1.000 R²)
mean                 2.028 s    (2.019 s .. 2.033 s)
std dev              7.832 ms   (0.0 s .. 8.178 ms)
variance introduced by outliers: 19% (moderately inflated)

benchmarking shakespeare/ghc
TTTTTTTTTTTTTTTTtime                 6.504 s    (6.391 s .. 6.694 s)
                     1.000 R²   (1.000 R² .. 1.000 R²)
mean                 6.499 s    (6.468 s .. 6.518 s)
std dev              28.85 ms   (0.0 s .. 32.62 ms)
variance introduced by outliers: 19% (moderately inflated)

benchmarking shakespeare/greg
TTTTTTTTTTTTTTTTtime                 5.560 s    (5.307 s .. 5.763 s)
                     1.000 R²   (0.999 R² .. 1.000 R²)
mean                 5.582 s    (5.537 s .. 5.607 s)
std dev              39.30 ms   (0.0 s .. 43.49 ms)
variance introduced by outliers: 19% (moderately inflated)


Costs and Drawbacks
-------------------

The only cost I see is the reduced performance when sorting already sorted
lists. However, since this remains quite efficient, indeed over 4 times
faster than sorting unsorted lists, I think it is an acceptable tradeoff.

Final note
----------

My Haskell is very rusty. I worked on this a couple years ago when I was
learning Haskell, and meant to propose it to the Haskell community, but
never got to it at the time.

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