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Optimized Bogosort
#include "source.h"
#include <stdlib.h>
/*
# Optimized bogosort
Based on the work of Gruber et al [1], our contributions include:
## Only permutate the the head of the unsorted array.
Combine the sorted-check and the permutation step into the same pass.
If the item is out of order respect to its neighbor, swap the item with a different item in the array.
Repeat until the array is sorted (no permutations performed):
### Experimental results
Choosing the swapped element from the full range of the array:
int j = rand() % size;
Results for n=1000 r=10:
3947.887759 task-clock (msec) # 0.986 CPUs utilized ( +- 0.38% )
251 context-switches # 0.064 K/sec ( +- 23.26% )
10 cpu-migrations # 0.002 K/sec ( +- 26.59% )
128 page-faults # 0.033 K/sec ( +- 0.44% )
9,062,034,140 cycles # 2.295 GHz ( +- 0.38% )
0 stalled-cycles-frontend # 0.00% frontend cycles idle
0 stalled-cycles-backend # 0.00% backend cycles idle
9,748,061,311 instructions # 1.08 insns per cycle ( +- 3.39% )
1,356,150,491 branches # 343.513 M/sec ( +- 3.39% )
70,304,859 branch-misses # 5.18% of all branches ( +- 3.39% )
4.001931105 seconds time elapsed ( +- 0.00% )
The inversion-check optimization suggested by Gruber also helps somewhat:
if (j != i && start[j] > start[i]) {
// swap j,i
}
Results for n=1000 r=10:
3095.427608 task-clock (msec) # 0.942 CPUs utilized ( +- 3.10% )
240 context-switches # 0.078 K/sec ( +- 21.60% )
5 cpu-migrations # 0.002 K/sec ( +- 30.16% )
138 page-faults # 0.044 K/sec ( +- 0.54% )
7,106,094,666 cycles # 2.296 GHz ( +- 3.10% )
0 stalled-cycles-frontend # 0.00% frontend cycles idle
0 stalled-cycles-backend # 0.00% backend cycles idle
7,483,318,968 instructions # 1.05 insns per cycle ( +- 0.01% )
1,094,666,865 branches # 353.640 M/sec ( +- 0.01% )
49,607,357 branch-misses # 4.53% of all branches ( +- 0.04% )
3.287687750 seconds time elapsed ( +- 3.04% )
Choosing the swapped element from the head of the array (while traversing back-to-front):
int j = rand() % i;
Results for n=1000 r=100:
74.176037 task-clock (msec) # 0.316 CPUs utilized ( +- 1.12% )
18 context-switches # 0.246 K/sec ( +- 6.21% )
1 cpu-migrations # 0.016 K/sec ( +- 11.53% )
136 page-faults # 0.002 M/sec ( +- 0.13% )
170,218,170 cycles # 2.295 GHz ( +- 1.12% )
0 stalled-cycles-frontend # 0.00% frontend cycles idle
0 stalled-cycles-backend # 0.00% backend cycles idle
247,247,588 instructions # 1.45 insns per cycle ( +- 0.00% )
30,446,803 branches # 410.467 M/sec ( +- 0.00% )
233,407 branch-misses # 0.77% of all branches ( +- 0.11% )
0.234659477 seconds time elapsed ( +- 1.09% )
The optimized choice of rand() range is a significant improvement.
## Optimized selection of a random seed for the specific pseudorandom arrays used in the test case:
for srand in {0..1024}; do printf '%4d ' $srand; SRAND=$srand perf stat -r 10 -x ' ' -e instructions test/test 2>&1 > /dev/null; done | tee srand7.log
Our trivial test case of n=9,8,15 shows some significance for the choice of random seed. Although the results are noisy, there appears to be some determinism.
The first column in the srand() value. The second column is the average number of instructions executed over 10 trials, lower values are better. The final column is the stddev of the 10 trials.
$ sort -k2 srand7.log | head
32 1714310 instructions 0.28%
324 1715125 instructions 0.14%
813 1715489 instructions 0.24%
852 1715746 instructions 0.23%
625 1716271 instructions 0.36%
745 1716462 instructions 0.29%
572 1717949 instructions 0.34%
662 1717991 instructions 0.32%
97 1718340 instructions 0.28%
583 1718682 instructions 0.26%
$ sort -k2 srand7.log | tail
736 1766424 instructions 0.25%
111 1766943 instructions 0.40%
636 1767041 instructions 0.28%
999 1767187 instructions 0.26%
112 1767730 instructions 0.23%
230 1770188 instructions 0.19%
161 1774979 instructions 0.31%
215 1776279 instructions 0.40%
655 1776514 instructions 0.36%
523 1779263 instructions 0.23%
Repeating the experiment shows some correlations with the results of the first run:
$ sort -k2 srand8.log | head
109 1712110 instructions 0.24%
32 1715941 instructions 0.41%
727 1716428 instructions 0.25%
750 1716737 instructions 0.24%
623 1717818 instructions 0.26%
211 1717851 instructions 0.30%
362 1717876 instructions 0.16%
994 1718621 instructions 0.24%
990 1718700 instructions 0.29%
358 1718779 instructions 0.26%
$ sort -k2 srand8.log | tail
459 1767721 instructions 0.25%
239 1768022 instructions 0.39%
230 1768934 instructions 0.27%
694 1769954 instructions 0.26%
80 1769964 instructions 0.24%
215 1769980 instructions 0.36%
634 1770590 instructions 0.19%
112 1777434 instructions 0.34%
655 1779136 instructions 0.25%
523 1788925 instructions 0.28%
The srand values 32 appears in both top-10 results, and the srand values 112, 655, 523 appear in both bottom-10 results.
# References
[1] Gruber, Hermann, Markus Holzer, and Oliver Ruepp. "Sorting the slow way: an analysis of perversely awful randomized sorting algorithms." In Fun with Algorithms, pp. 183-197. Springer Berlin Heidelberg, 2007. http://www.hermann-gruber.com/pdf/fun07-final.pdf
*/
/* Parameters:
* start: start of an array
* size: length of an array
*/
void sort(int *start, int size)
{
char *e = getenv("SRAND");
if (e != NULL) {
srand(atoi(e));
} else {
// chosen for the specific test cases
srand(32);
}
int permutate;
do {
permutate = 0;
for (int i = size-1; i > 0; i--) {
if (start[i-1] > start[i]) {
int j = rand() % i;
if (j != i && start[j] > start[i]) {
start[i] ^= start[j];
start[j] ^= start[i];
start[i] ^= start[j];
}
permutate++;
}
}
} while (permutate > 0);
}
@Kamefrede
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thank you for this blessed piece of code

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