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May the source be with you, but remember the KISS principle ;-)
Bigger doesn't imply better. Bigger often is a sign of obesity, of lost control, of overcomplexity, of cancerous cells
As for input data it is useful to distinguish between the following broad categories that all should be used in testing (random number sorting is a very artificial test and as such that estimate it provides does not have much practical value, unless we know that other cases behave similarly or better):
Completely randomly reshuffled array (this is the only test that naive people use in evaluating sorting algorithms) .
Already sorted array (you need to see how horrible quicksort is on this case and how good shellsort is ;-). This is actually a pretty important case as often sorting is done just to ensure that the data are sorted and in most such cases they actually were already sorted.
Already sorted in reverse order array
Array consisting of identical elements
Already sorted array with N permutations
Already sorted array in reverse order array with N permutations
Normal distribution with duplicate (or close) keys (for stable sorting)
Calculation of the number of comparisons and number of data moves can be done in any language. C-language and other compiled languages provide an opportunity to see the effect of computer instruction set and CPU speed on the sorting performance. Usually the test program is written as a subroutine that is called, say, 1000 times. Then entry subroutine (may be with data coping operation is run the same number of times and the result is subtracted from the first. That method can provide more or less accurate estimate of actual algorithms run time on a particular data set and particular CPU architecture. Generally CPU with a lot of common registers tend to perform better on sorting.
Artificial computers like Knuth MIXX can be used too. In this case the time is calculated based on thee time table of performing of each instruction (instruction cost metric).
sortchk - a sort algorithm test suite
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