In computer science, comparator networks are abstract devices built up of a fixed number of "wires", carrying values, and comparator modules that connect pairs of wires, swapping the values on the wires if they are not in a desired order. Such networks are typically designed to perform sorting on fixed numbers of values, in which case they are called sorting networks. Sorting networks differ from general comparison sorts in that they are not capable of handling arbitrarily large inputs, and in that their sequence of comparisons is set in advance, regardless of the outcome of previous comparisons. In order to sort a larger number of inputs, new sorting networks must be constructed. This independence of comparison sequences is useful for parallel execution and for implementation in hardware. Despite the simplicity of sorting nets, their theory is surprisingly deep and complex. Sorting networks were first studied circa 1954 by Armstrong, Nelson and O'Connor, who subsequently patented the idea. Sorting networks can be implemented either in hardware or in software. Donald Knuth describes how the comparators for binary integers can be implemented as simple, three-state electronic devices. Batcher, in 1968, suggested using them to construct switching networks for computer hardware, replacing both buses and the faster, but more expensive, crossbar switches. Since the 2000s, sorting nets (especially bitonic mergesort) are used by the GPGPU community for constructing sorting algorithms to run on graphics processing units.
Introduction
A sorting network consists of two types of items: comparators and wires. The wires are thought of as running from left to right, carrying values (one per wire) that traverse the network all at the same time. Each comparator connects two wires. When a pair of values, traveling through a pair of wires, encounter a comparator, the comparator swaps the values if and only if the top wire's value is greater or equal to the bottom wire's value. In a formula, if the top wire carries x and the bottom wire carries y, then after hitting a comparator the wires carry x ′ = min ( x , y ) {\displaystyle x'=\min(x,y)} and y ′ = max ( x , y ) {\displaystyle y'=\max(x,y)} , respectively, so the pair of values is sorted. A network of wires and comparators that will correctly sort all possible inputs into ascending order is called a sorting network or Kruskal hub. By reflecting the network, it is also possible to sort all inputs into descending order. The full operation of a simple sorting network is shown below. It is evident why this sorting network will correctly sort the inputs; note that the first four comparators will "sink" the largest value to the bottom and "float" the smallest value to the top. The final comparator sorts out the middle two wires.
Depth and efficiency The efficiency of a sorting network can be measured by its total size, meaning the number of comparators in the network, or by its depth, defined (informally) as the largest number of comparators that any input value can encounter on its way through the network. Noting that sorting networks can perform certain comparisons in parallel (represented in the graphical notation by comparators that lie on the same vertical line), and assuming all comparisons to take unit time, it can be seen that the depth of the network is equal to the number of time steps required to execute it.
Insertion and Bubble networks We can easily construct a network of any size recursively using the principles of insertion and selection. Assuming we have a sorting network of size n, we can construct a network of size n + 1 by "inserting" an additional number into the already sorted subnet (using the principle underlying insertion sort). We can also accomplish the same thing by first "selecting" the lowest value from the inputs and then sort the remaining values recursively (using the principle underlying bubble sort). The structure of these two sorting networks are very similar. A construction of the two different variants, which collapses together comparators that can be performed simultaneously shows that, in fact, they are identical.
The insertion network (or equivalently, bubble network) has a depth of 2n - 3, where n is the number of values. This is better than the O(n log n) time needed by random-access machines, but it turns out that there are much more efficient sorting networks with a depth of just O(log2 n), as described below.
Zero-one principle While it is easy to prove the validity of some sorting networks (like the insertion/bubble sorter), it is not always so easy. There are n! permutations of numbers in an n-wire network, and to test all of them would take a significant amount of time, especially when n is large. The number of test cases can be reduced significantly, to 2n, using the so-called zero-one principle. While still exponential, this is smaller than n! for all n ≥ 4, and the difference grows quite quickly with increasing n. The zero-one principle states that, if a sorting network can correctly sort all 2n sequences of zeros and ones, then it is also valid for arbitrary ordered inputs. This not only drastically cuts down on the number of tests needed to ascertain the validity of a network, it is of great use in creating many constructions of sorting networks as well. The principle can be proven by first observing the following fact about comparators: when a monotonically increasing function f is applied to the inputs, i.e., x and y are replaced by f(x) and f(y), then the comparator produces min(f(x), f(y)) = f(min(x, y)) and max(f(x), f(y)) = f(max(x, y)). By induction on the depth of the network, this result can be extended to a lemma stating that if the network transforms the sequence a1, ..., an into b1, ..., bn, it will transform f(a1), ..., f(an) into f(b1), ..., f(bn). Suppose that some input a1, ..., an contains two items ai < aj, and the network incorrectly swaps these in the output. Then it will also incorrectly sort f(a1), ..., f(an) for the function
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