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:
- Binary insertion sort
- Merge sort (stable)
- In-place merge sort (not stable)
- Selection sort (ugh -- this is really only here for comparison)
- Timsort (stable)
Grail sort (stable)
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_TYPEto 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_NAMEto be a unique name that will be prepended to all the routines, i.e.,
#define SORT_NAME minewould give you routines named
mine_heap_sort, and so forth.
#include "sort.h". Make sure that
sort_common.hare in your include path.
Then, enjoy using the sorting routines.
#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
which you can use like
/* Assumes you have some int *arr or int arr; */ int64_quick_sort(arr, 128);
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
#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))
You can also redefine
TIM_SORT_STACK_SIZE (default 128) to control
the size of the tim sort stack (which can be used to reduce memory).
Reducing it too far can cause tim sort to overflow the stack though.
Speed of routines
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
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
Quicksort is the winner here. Heapsort, in-place merge sort, and timsort also often tend to be quite fast.
Available under the MIT License. See LICENSE.md for details.