We're also looking into launching services, which is a great revenue stream for people who prefer to pay for convenience of not having to deal with operations at all.

    qsort :: Ord a => [a] -> [a]
    qsort []     = []
    qsort (p:xs) = qsort lesser ++ [p] ++ qsort greater
        where
                lesser = filter (< p) xs
                greater = filter (>= p) xs

I wish more Haskell proponents would spent less time showing off simple and elegant code, and more time showing fast and correct code.

It's pretty elegant, too, if less than the inefficient pseudo-qsort shown by the OP.

That means there is a lot of overhead for doing something simple like an in-place qsort that should be much faster than a mergesort.

I am at a loss on why the provided code is any more elegant than: http://en.literateprograms.org/Merge_sort_%28C_Plus_Plus%29

or this (also taken from rosetta code)?

  #include <iterator>
  #include <algorithm> // for std::partition
  #include <functional> // for std::less
  
  template<typename RandomAccessIterator,
           typename Order>
  void quicksort(RandomAccessIterator first, RandomAccessIterator last, Order order)
  {
    if (last - first > 1)
    {
      RandomAccessIterator split = std::partition(first+1, last, std::bind2nd(order, *first));
      std::iter_swap(first, split-1);
      quicksort(first, split-1, order);
      quicksort(split, last, order);
    }
  }

  template<typename RandomAccessIterator>
 void quicksort(RandomAccessIterator first, RandomAccessIterator last)
  {
    quicksort(first, last, std::less<typename std::iterator_traits<RandomAccessIterator>::value_type>());
  }

Well, the problem not so much that there is a lot of overhead for doing an in-place qsort. The problem is that "in-place" anything is impossible in Haskell, it violates referential transparency.

Merge sort however needs no in-place update and can trivially (in Haskell world) be parallelized (merge sort is associative, you can use it as the reduce step of MapReduce). So you are correct in concluding that mergesort is used in Haskell.

You state "something simple like an in-place qsort", your statement about qsort being simple makes some assumptions about your programming languages world, which are not the same for all languages especially not Haskell. No whether all this is a pro and a con depends on what sort of coding you're doing of course.

I think that people call Haskell's code more elegant because they (like me) find C++ template and type annotations to be annoyingly verbose and messy for no good reason. For example for the two quicksort functions:

"template<typename RandomAccessIterator> void quicksort(RandomAccessIterator first, RandomAccessIterator last)"

vs

"quicksort :: (Ord a) => [a] -> [a]" (where "Ord a" means that a is an instance of the typeclass of ordered items).

There's a lot of infrastructure behind MapReduce and I bet you any implementation is going to have a lot more code in it than what is in Data.List.

Once things are in RAM (say, less than 1 GB), quicksort is the fastest algorithm (Edit: or some other optimized in-place variation on the theme such as introsort). You can parallelize across cores all you want but all that is going to do is crap up your caches and cause bus contention, making your code more complex and slower. The bottleneck is your databus not the CPU, after all what is the cost of a compare in clock cycles? This is also why mergesort is slower and parallelizing is not going to fix it.

If you want to see things that are parallelizable check out vectorizing libraries such as Thrust that run on massively parallel GPGPUs that have the bus design and RAM to handle that kind of parallelization. They are written in C++ and both the implementation and the code to use them looks almost the same like the code I posted. The compiler technology of other languages is years behind and currently can't even target these kinds of architectures.

I've seen GPU massive parallelization in Haskell and all it did was to generate C code and then invoke the CUDA compiler. This was in a research paper. In the meantime you can fire up nvcc with Thrust and sort bazillion of keys with less than 10 lines of code in C++ using a well-tested library.

Edit: BTW, anyone (and I really don't know who would be doing this) who is even thinking of running a MapReduce Haskell cluster must have lots of free time and $$$ to burn on wasted CPU cycles.

My point wasn't about using Haskell for high performance computing, my point was that Haskell's compiler can convert normal Haskell code to multicore code without (much, if any) work from the programmer. There is a large gap in between sequential single threaded program and a full MapReduce cluster and I think there is a significant amount of software written in this gap where Haskell-like languages could help with the parallelism/concurrency. The only reason I referenced MapReduce is since its a well known example even for people who are not well versed in functional programming (its a very common concept in functional programming).

And it is not true that in-place modification is not possible in Haskell. It is just not considered elegant, but it can be encapsulated in a perfectly Haskell-y way. (Hint: ST monad.)

Also I did not claim that my linked code is more elegant than some other code. Where did I make that claim? So I don't have to defend the claim I did not make, right?

EDIT: As a curious example, Clojure's sort function actually converts the sequence to a (Java) Array and calls java.util.Arrays.sort() on it, then turns it back into a sequence:

https://github.com/clojure/clojure/blob/b578c69d7480f621841e...

You can argue some older languages were written in a way in which it was (reasonably) easy to write a compiler that generates code with little performance overhead when compared to assembly (at worst a factor of 2 to 4, back in the 80s). Some popular languages keep adding abstractions and constructs that the compiler can actually deal with without some new compiler research breakthrough.

Yes; see also http://prog21.dadgum.com/40.html

It's nice he is into the C&C Portland Wiki, there's golden nuggets of wisdom everywhere. I am reposting the link from his blog. http://c2.com/cgi/wiki?SufficientlySmartCompiler

Personally of the higher-level languages, I found SBCL to be crazy good at optimizing, of course given a few nudges with a compiler directive or two. By inspecting the dissasembly, I could validate it was doing the "right-thing" (TM), but then again, I do the same to check the C/C++ compiled code.

Nice thought. What's the purpose of showing code which you won't be using in a real-world application? Making you think the language is more expressive than what it really is?

Many people say Haskell code is elegant and concise. Would this be the case if code we were shown would be real-world Haskell?

Here's a page with some actual haskell quicksorts:

http://www.haskell.org/haskellwiki/Introduction/Direct_Trans...

    qsort :: Ord a => [a] -> [a]
    qsort []     = []
    qsort [x]    = [x]
    qsort (p:xs) = qsort lesser ++ [p] ++ qsort greater
        where
            (lesser, greater) = foldl split ([],[]) xs 
        where 
            split (left, right) x = if x<p then (left:x, right), else (left, right:x)

    split (left, right) x = if x<p then (x:left, right), else (left, x:right)

* especially with naive pivot from the middle of array

http://googleresearch.blogspot.com/2006/06/extra-extra-read-...

  program qsortTest;
  const N = 1000;
  var
    a: array [0..N] of integer;
    i: integer;

  procedure quicksort(var a: array of integer; l, r: integer);
  var
    i, j, temp: integer;
    pivot: integer;
  begin
    pivot := a[(l+r) div 2]; i := l; j := r;
    while i < j do 
    begin
      while a[i] < pivot do inc(i);
      while a[j] > pivot do dec(j);
      if i <= j then 
      begin
        temp := a[i];
        a[i] := a[j];
        a[j] := temp;
        inc(i); dec(j);
      end;
    end;
    if j > l then quicksort(a, l, j);
    if i < r then quicksort(a, i, r);
  end;

  begin
    { populating the array }
    for i := 0 to N do
      a[i] := random(N);
    writeln('initial array');
    for i := 0 to N do write(a[i]:6);
    writeln;
    quicksort(a, 0, N);

    writeln;
    writeln('sorted array');
    for i := 0 to N do write(a[i]:6);
    writeln;

  end.

If they iterate 'up to' then:

It would seem to me that on the initial call to 'quicksort' the '1000' gets passed in to 'r', the value of 'r' then gets passed in to 'j'.

Now if "'a[j]' < p" will refer to the uninitialized value in a[1000] and compare it to p, the first decrement will not take place, i<=j and now the code will swap with a[i] with a[1000].

This should cause the uninitialized value (0?) to end up as the first element in the sorted output array.

I hope I analyzed that correct, I don't have access to a pascal/delphi compiler here.

When you run your code do you see the output starting with a '0' element ?

Is there another element from the input array missing in the output ?

If pascal/delphi loops iterate up-to-and-including did you actually intend to sort an array of 1001 elements ?

Arrays boundaries are [0..N] -> [0..1000] (1001 element actually, sorry if that confused you).

[add]: I could rewrite it in C, it should be trivial bijection, I believe.

Interestingly, I've also read SICP in parallel -- currently reading the last chapter -- and there's some overlap in the topics treated in the two books. I'm also walking through CLRS and TAOCP at the moment, albeit reading the latter with a slower pace compared to the former.

On the non-technical side, I'm reading ``Drawing with the right side of the brain'' by Betty Edwards. I've picked up this book since I've decided to resume drawing one year ago. I highly recommend it if you struggle to draw a realistic rendering of a real life object or a landscape.

    export GIT_PS1_SHOWDIRTYSTATE=yes
    export GIT_PS1_SHOWUNTRACKEDFILES=yes

It's implemented in bash but I use it with almost no modification with the Korn shell.

Recently ZSH support has been added to git-completion.bash, but it's still only in the "next" branch of git: http://git.kernel.org/?p=git/git.git;a=blob_plain;f=contrib/...