I've done some benchmarking of my own over various dataset sizes.
dataset sizeGrepMaxTernary
100319704.72/s167783.44/s160956.63/s
1000324017.59/s158933.12/s157394.83/s
10000328377.50/s162308.63/s159661.13/s
100000332964.44/s160964.22/s157891.99/s
You will notice that the times for dataset sizes of 100, 1000, 10000 and 100000 are nearly identical for each algorithm. This clearly shows that for datasets of that size it is not the size of the dataset that is the limiting factor in performance. I suspect the preformance issue is "Algorithmic Friction"; the overhead involved in doing the calls themselves. Also remember that the sort is implemented in the C library, so it is really fast. (still O(n log(n)), but a very fast O(n log(n))).

What I expect you're seeing is that it is much quicker to sort (implemented in binary code) and index the last element than it is to iterate through an array (even once) in an interpreted language for datasets of the size we are considering.

You see similar results with sorting algorithms. Quicksort is well know to be the fastest general purpose sorting algorithm for large sets O(n log(n)). However the overhead incurred by quicksort is overkill for small datasets. For datasets with very few elements bubblesort is often faster, even though bubble sort is O(n^2).

In reply to Re: Algorithm Efficiency vs. Specialized Hardware? by lhoward
in thread Algorithm Efficiency vs. Specialized Hardware? by Russ

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