sort.h is an implementation a ton of sorting algorithms in C with a user-defined type that is defined at include time.

This means you don't have to pay the function call overhead of using standard library routine. This gives us the power of higher-level language generics.

In addition, you don't have to link in a library: the entirety of this sorting library is contained in the header files.

You get the choice of many sorting routines, including:

• Shellsort
• Binary insertion sort
• Heapsort
• Quicksort
• Merge sort (stable)
• In-place merge sort (not stable)
• Selection sort (ugh -- this is really only here for comparison)
• Timsort (stable)
• Grail sort (stable)

  • Based on B-C. Huang and M. A. Langston, *Fast Stable Merging and Sorting in Constant Extra Space* (1989-1992).

    Thanks to Andrey Astrelin for the implementation.

• Sqrt Sort (stable, based on Grail sort, also by Andrey Astrelin).

If you don't know which one to use, you should probably use Timsort.

If you have a lot data that is semi-structured, then you should definitely use Timsort.

If you have data that is really and truly random, quicksort is probably fastest.

To use this library, you need to do three things:

• #define SORT_TYPE to be the type of the elements of the array you want to sort. (For pointers, you should declare this like: #define SORT_TYPE int*)
• #define SORT_NAME to be a unique name that will be prepended to all the routines, i.e., #define SORT_NAME mine would give you routines named mine_heap_sort, and so forth.
• #include "sort.h". Make sure that sort.h and sort_common.h are in your include path.

Then, enjoy using the sorting routines.

Quick example:

#define SORT_NAME int64
#define SORT_TYPE int64_t
#define SORT_CMP(x, y) ((x) - (y))
#include "sort.h"

You would now have access to int64_quick_sort, int64_tim_sort, etc., which you can use like

/* Assumes you have some int *arr or int arr[128]; */
int64_quick_sort(arr, 128);

See demo.c for a more detailed example usage.

If you are going to use your own custom type, you must redefine SORT_CMP(x, y) with your comparison function, so that it returns a value less than zero if x < y, equal to zero if x == y, and greater than 0 if x > y.

The default just uses the builtin <, ==, and > operators:

#define SORT_CMP(x, y)  ((x) < (y) ? -1 : ((x) == (y) ? 0 : 1))

It is often just fine to just subtract the arguments as well (though this can cause some stability problems with floating-point types):

#define SORT_CMP(x, y) ((x) - (y))

The speed of each routine is highly dependent on your computer and the structure of your data.

If your data has a lot of partially sorted sequences, then Tim sort will beat the pants off of anything else.

Timsort is not as good if memory movement is many orders of magnitude more expensive than comparisons (like, many more than for normal int and double). If so, then quick sort is probably your routine. On the other hand, Timsort does extremely well if the comparison operator is very expensive, since it strives hard to minimize comparisons.

Here is the output of demo.c, which will give you the timings for a run of 10,000 int64_ts on 2014-era MacBook Pro:

Running tests
stdlib qsort time:             1285.00 us per iteration
stdlib heapsort time:          2109.00 us per iteration
stdlib mergesort time:         1299.00 us per iteration
quick sort time:                579.00 us per iteration
selection sort time:         127176.00 us per iteration
merge sort time:                999.00 us per iteration
binary insertion sort time:   13443.00 us per iteration
heap sort time:                 592.00 us per iteration
shell sort time:               1054.00 us per iteration
tim sort time:                 1005.00 us per iteration
in-place merge sort time:       903.00 us per iteration
grail sort time:               1220.00 us per iteration
sqrt sort time:                1095.00 us per iteration

Heapsort is the winner here. Quicksort, in-place merge sort, and timsort also often tend to be quite fast.

See CONTRIBUTING.md.

• Wikipedia: Timsort
• timsort.md

Available under the MIT License. See LICENSE.md for details.