After a long break, I can finally return to the topic which was started in the previous blog post.

Imagine that we have a small array of compile-time constant size with integers. For instance, N = 32. And we want to sort it as fast as possible. What is the best solution for this problem?

The wisest of you would suggest simply using std::sort, because every modern implementation of it is well-optimized and contains special handling of small subarrays to accelerate the generic quicksort algorithm. The ones who don't trust libraries would suggest using insertion sort: this is the algorithm usually used to handle small cases in std::sort. The performance geeks and regular stackoverflow visitors would definitely point to sorting networks: every question like "the fastest way to sort K integers" ends up with a solution based on them (N=6, N=10, what, why). I'm going to present a much less known way to sort small arrays of 32-bit keys, with performance comparable to sorting networks.

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The previous blog post got some attention and several good questions and suggestions. So I feel that I should sum up the main points noted by readers. Since the main article is already too long, I decided to keep all of this as a separate post.

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While working on an implementation of merge sort promised in the previous article, I realized that I'd like to use one neat little thing, which is worth its own post. It is a simple strategy for sorting or doing comparison-based tasks, which works wonderfully when input data is small enough.

Suppose that we have a very small array and we want to sort it as fast as possible. Indeed, applying some fancy O(N log N) algorithm is not a good idea: although it has optimal asymptotic performance, its logic is too complicated to outperform simple bubble-sort-like algorithms which take O(N^2) time instead. That's why every well-optimized sorting algorithm based on quicksort (e.g. std::sort) or mergesort includes some simple quadratic algorithm which is run for sufficiently small subarrays like N <= 32.

What exactly should we strive for to get an algorithm efficient for small N? Here is the list of things to look for:

1. Avoid branches whenever possible: unpredictable ones are very slow.
2. Reduce data dependency: this allows to fully utilize processing units in CPU pipeline.
3. Prefer simple data access and manipulation patterns: this allows to vectorize the algorithm.
4. Avoid complicated algorithms: they almost always fail on one of the previous points, and they sometimes do too much work for small inputs.

I decided to start investigating a simpler problem first, which is solved by std::lower_bound: given a sorted array of elements and a key, find index of the first array element greater or equal than the key. And this investigation soon developed into a full-length standalone article.

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