October 15, 2012 Sorting algorithms | |
---|---|

Been watching online lectures that's going into sorting and searching, and from what I'm seeing most sorting algorithms (by using comparison; merge sort, quicksort, etc) and even tree algorithms peak at O(n log n). So an example area to be sorted with 16 elements would take on average about 100 compares while theoretically you can do it in half that number. What algorithms get you closest to that optimal value? If there isn't any I have an idea that may just do it. Either way I'll be trying to implement it and see how it performs. I wonder what happens if I succeed and become famous... O.O |

October 15, 2012 Re: Sorting algorithms | |
---|---|

Posted in reply to Era Scarecrow | ```
On Monday, 15 October 2012 at 09:18:12 UTC, Era Scarecrow wrote:
> So an example area to be sorted with 16 elements would take on
> average about 100 compares while theoretically you can do it in
> half that number.
Correction. 16 numbers would be solved in about 49 compares
while an optimal sorting takes about 45. And for 21 numbers about
74 compares while optimally about 63.
These numbers don't seem that large, but at the same time they
do.
``` |

October 15, 2012 Re: Sorting algorithms | |
---|---|

Posted in reply to Era Scarecrow | On Mon, Oct 15, 2012 at 1:04 PM, Era Scarecrow <rtcvb32@yahoo.com> wrote: > On Monday, 15 October 2012 at 09:18:12 UTC, Era Scarecrow wrote: >> >> So an example area to be sorted with 16 elements would take on average >> about 100 compares while theoretically you can do it in half that number. > > > Correction. 16 numbers would be solved in about 49 compares while an > optimal sorting takes about 45. And for 21 numbers about 74 compares while > optimally about 63. > > These numbers don't seem that large, but at the same time they do. Somewhat related: I once played with sorting networks and how to generate them at compile time in D. It's in template tutorial on Github. Here are some results: https://github.com/PhilippeSigaud/D-templates-tutorial/blob/master/templates_around.tex#L560 Discarding LaTeX markup: n Sorting Standard ratio network sort 5 324 532 1.642 10 555 1096 1.975 15 803 1679 2.091 20 1154 2314 2.005 25 1538 3244 2.109 30 2173 3508 1.614 35 4075 4120 1.011 40 5918 5269 0.890 45 7479 5959 0.797 50 9179 6435 0.701 Were n is the (predetermined) number of elements in an array and a ratio of 2.0 means sorting networks are twice faster than std.algo.sort in this particular case. |

October 15, 2012 Re: Sorting algorithms | |
---|---|

Posted in reply to Era Scarecrow | ```
On Monday, 15 October 2012 at 09:18:12 UTC, Era Scarecrow wrote:
> Been watching online lectures that's going into sorting and
> searching, and from what I'm seeing most sorting algorithms (by
> using comparison; merge sort, quicksort, etc) and even tree
> algorithms peak at O(n log n). So an example area to be sorted
> with 16 elements would take on average about 100 compares while
> theoretically you can do it in half that number.
Big-O notation doesn't give you actual numbers, O(n) = O(25*n).
If you're interested in a practical method, look at TimSort and
similar ones that combine different algorithms.
``` |

October 15, 2012 Re: Sorting algorithms | |
---|---|

Posted in reply to Philippe Sigaud | ```
On 10/15/12 8:11 AM, Philippe Sigaud wrote:
> On Mon, Oct 15, 2012 at 1:04 PM, Era Scarecrow<rtcvb32@yahoo.com> wrote:
>> On Monday, 15 October 2012 at 09:18:12 UTC, Era Scarecrow wrote:
>>>
>>> So an example area to be sorted with 16 elements would take on average
>>> about 100 compares while theoretically you can do it in half that number.
>>
>>
>> Correction. 16 numbers would be solved in about 49 compares while an
>> optimal sorting takes about 45. And for 21 numbers about 74 compares while
>> optimally about 63.
>>
>> These numbers don't seem that large, but at the same time they do.
>
> Somewhat related: I once played with sorting networks and how to
> generate them at compile time in D. It's in template tutorial on
> Github. Here are some results:
>
> https://github.com/PhilippeSigaud/D-templates-tutorial/blob/master/templates_around.tex#L560
>
> Discarding LaTeX markup:
>
>
> n Sorting Standard ratio
> network sort
> 5 324 532 1.642
> 10 555 1096 1.975
> 15 803 1679 2.091
> 20 1154 2314 2.005
> 25 1538 3244 2.109
> 30 2173 3508 1.614
> 35 4075 4120 1.011
> 40 5918 5269 0.890
> 45 7479 5959 0.797
> 50 9179 6435 0.701
>
> Were n is the (predetermined) number of elements in an array and a
> ratio of 2.0 means sorting networks are twice faster than
> std.algo.sort in this particular case.
I wanted to investigate small sorts using sorting networks for ages, but
never got around to it. That's important for quicksort because it
produces many small arrays that need sorting.
Could you also test for very small sizes (2 to 4) and essentially test
for 1-increment speed up to, say, 30 elements? I assume that's where the
major wins come. I think we could use CT-generated sorting networks for
arrays below a specific size. The converse risk is growth of generated code.
Andrei
``` |

October 15, 2012 Re: Sorting algorithms | |
---|---|

Posted in reply to thedeemon | ```
On Monday, 15 October 2012 at 15:49:51 UTC, thedeemon wrote:
> On Monday, 15 October 2012 at 09:18:12 UTC, Era Scarecrow wrote:
> Big-O notation doesn't give you actual numbers, O(n) = O(25*n).
> If you're interested in a practical method, look at TimSort and
> similar ones that combine different algorithms.
Yeah I know it's more of a generalized number of steps, but it
still gives you a good idea of sorting time. I'll give TimSort a
look over.
Currently I'm estimating this will be a variant of merge-sort.
``` |

October 15, 2012 Re: Sorting algorithms | |
---|---|

Posted in reply to Era Scarecrow | On 15-Oct-12 23:15, Era Scarecrow wrote: > On Monday, 15 October 2012 at 15:49:51 UTC, thedeemon wrote: >> On Monday, 15 October 2012 at 09:18:12 UTC, Era Scarecrow wrote: >> Big-O notation doesn't give you actual numbers, O(n) = O(25*n). If >> you're interested in a practical method, look at TimSort and similar >> ones that combine different algorithms. > > Yeah I know it's more of a generalized number of steps, but it still > gives you a good idea of sorting time. I'll give TimSort a look over. > > Currently I'm estimating this will be a variant of merge-sort. A hybrid. I'm currently trying to get into Phobos: https://github.com/D-Programming-Language/phobos/pull/787 -- Dmitry Olshansky |

October 16, 2012 Re: Sorting algorithms | |
---|---|

Posted in reply to Dmitry Olshansky | ```
On Monday, 15 October 2012 at 20:58:36 UTC, Dmitry Olshansky
wrote:
> A hybrid. I'm currently trying to get into Phobos:
> https://github.com/D-Programming-Language/phobos/pull/787
I'll have to look it over in more detail another time.
Although another question comes to mind. How many algorithms are
really 'lazy'? I know some can stop after getting x elements, but
almost all algorithms need to do at least a certain amount of
work before they can pause or stop at a point.
I think I can make mine lazy and range friendly, just not random
access friendly at the same time.
``` |

October 17, 2012 Re: Sorting algorithms | |
---|---|

Posted in reply to Andrei Alexandrescu | On Mon, Oct 15, 2012 at 5:52 PM, Andrei Alexandrescu <SeeWebsiteForEmail@erdani.org> wrote: > I wanted to investigate small sorts using sorting networks for ages, but > never got around to it. That's important for quicksort because it produces > many small arrays that need sorting. > > Could you also test for very small sizes (2 to 4) and essentially test for > 1-increment speed up to, say, 30 elements? I assume that's where the major > wins come. I think we could use CT-generated sorting networks for arrays > below a specific size. The converse risk is growth of generated code. Here: http://dpaste.dzfl.pl/42fac981 I don't know if the benchmarking code is OK. I substract a reference because randomly shuffling an array takes some time. Results for my computer (smaller ratio means faster network sort compared to std.algorithm.sort) Size 1, network: 2.10, std.algorithm.sort: 15.86, ratio network/std.algo: 0.13 Size 2, network: 2.23, std.algorithm.sort: 14.26, ratio network/std.algo: 0.16 Size 3, network: 6.22, std.algorithm.sort: 20.75, ratio network/std.algo: 0.30 Size 4, network: 8.25, std.algorithm.sort: 28.36, ratio network/std.algo: 0.29 Size 5, network: 18.54, std.algorithm.sort: 39.02, ratio network/std.algo: 0.48 Size 6, network: 20.12, std.algorithm.sort: 45.58, ratio network/std.algo: 0.44 Size 7, network: 27.49, std.algorithm.sort: 55.53, ratio network/std.algo: 0.50 Size 8, network: 33.91, std.algorithm.sort: 66.02, ratio network/std.algo: 0.51 Size 9, network: 53.98, std.algorithm.sort: 75.54, ratio network/std.algo: 0.71 Size 10, network: 46.66, std.algorithm.sort: 81.68, ratio network/std.algo: 0.57 Size 11, network: 65.06, std.algorithm.sort: 111.25, ratio network/std.algo: 0.58 Size 12, network: 66.31, std.algorithm.sort: 109.40, ratio network/std.algo: 0.61 Size 13, network: 74.84, std.algorithm.sort: 115.94, ratio network/std.algo: 0.65 Size 14, network: 90.05, std.algorithm.sort: 131.84, ratio network/std.algo: 0.68 Size 15, network: 95.23, std.algorithm.sort: 145.31, ratio network/std.algo: 0.66 Size 16, network: 104.66, std.algorithm.sort: 162.84, ratio network/std.algo: 0.64 Size 17, network: 125.30, std.algorithm.sort: 167.49, ratio network/std.algo: 0.75 Size 18, network: 133.10, std.algorithm.sort: 182.20, ratio network/std.algo: 0.73 Size 19, network: 143.92, std.algorithm.sort: 195.58, ratio network/std.algo: 0.74 Size 20, network: 155.01, std.algorithm.sort: 211.59, ratio network/std.algo: 0.73 Size 21, network: 171.43, std.algorithm.sort: 224.47, ratio network/std.algo: 0.76 Size 22, network: 177.46, std.algorithm.sort: 236.92, ratio network/std.algo: 0.75 Size 23, network: 192.22, std.algorithm.sort: 253.38, ratio network/std.algo: 0.76 Size 24, network: 205.39, std.algorithm.sort: 270.83, ratio network/std.algo: 0.76 Size 25, network: 213.25, std.algorithm.sort: 281.01, ratio network/std.algo: 0.76 Size 26, network: 233.96, std.algorithm.sort: 283.57, ratio network/std.algo: 0.83 Size 27, network: 246.73, std.algorithm.sort: 297.67, ratio network/std.algo: 0.83 Size 28, network: 260.41, std.algorithm.sort: 313.88, ratio network/std.algo: 0.83 Size 29, network: 280.06, std.algorithm.sort: 321.01, ratio network/std.algo: 0.87 Size 30, network: 298.65, std.algorithm.sort: 342.55, ratio network/std.algo: 0.87 Size 31, network: 308.09, std.algorithm.sort: 355.70, ratio network/std.algo: 0.87 Size 32, network: 323.89, std.algorithm.sort: 380.31, ratio network/std.algo: 0.85 On the computers I tested it (Windows, Linux, different machines), the cutoff is at about 35-38 elements. |

October 17, 2012 Re: Sorting algorithms | |
---|---|

Posted in reply to Philippe Sigaud | ```
On 10/17/12 3:07 PM, Philippe Sigaud wrote:
> On Mon, Oct 15, 2012 at 5:52 PM, Andrei Alexandrescu
> <SeeWebsiteForEmail@erdani.org> wrote:
>
>> I wanted to investigate small sorts using sorting networks for ages, but
>> never got around to it. That's important for quicksort because it produces
>> many small arrays that need sorting.
>>
>> Could you also test for very small sizes (2 to 4) and essentially test for
>> 1-increment speed up to, say, 30 elements? I assume that's where the major
>> wins come. I think we could use CT-generated sorting networks for arrays
>> below a specific size. The converse risk is growth of generated code.
>
> Here:
>
> http://dpaste.dzfl.pl/42fac981
>
> I don't know if the benchmarking code is OK. I substract a reference
> because randomly shuffling an array takes some time.
>
> Results for my computer (smaller ratio means faster network sort
> compared to std.algorithm.sort)
>
> Size 1, network: 2.10, std.algorithm.sort: 15.86, ratio network/std.algo: 0.13
> Size 2, network: 2.23, std.algorithm.sort: 14.26, ratio network/std.algo: 0.16
> Size 3, network: 6.22, std.algorithm.sort: 20.75, ratio network/std.algo: 0.30
> Size 4, network: 8.25, std.algorithm.sort: 28.36, ratio network/std.algo: 0.29
> Size 5, network: 18.54, std.algorithm.sort: 39.02, ratio network/std.algo: 0.48
> Size 6, network: 20.12, std.algorithm.sort: 45.58, ratio network/std.algo: 0.44
> Size 7, network: 27.49, std.algorithm.sort: 55.53, ratio network/std.algo: 0.50
> Size 8, network: 33.91, std.algorithm.sort: 66.02, ratio network/std.algo: 0.51
[snip]
Looking great, thanks. I'm on the road with little time and
connectivity, but I want to encourage you with integrating this with
std.sort. There seems to be a big gain drop off at size 9, so we could
use sorting networks for size <= 8. (I'm also worried about generated
code size.) So next step would be to integrate the sorting network
within std.sort and see how it works there.
Please don't let this good work go to waste!
Andrei
``` |