  Sorting is a fundamental operation in computing. However, the speed of
state-of-the-art sorting algorithms on a single thread has reached their
limits. Meanwhile, deep learning has demonstrated its potential to provide
significant performance improvements in data mining and machine learning tasks.
Therefore, it is interesting to explore whether sorting can also speed up by
deep learning techniques. In this paper, a neural network-based data
distribution aware sorting method named NN-sort is presented. Compared to
traditional comparison-based sorting algorithms, which need to compare the data
elements in pairwise, NN-sort leverages the neural network model to learn the
data distribution and uses it to map disordered data elements into ordered
ones. Although the complexity of NN-sort is $nlogn$ in theory, it can run in
near-linear time as being observed in most of the cases. Experimental results
on both synthetic and real-world datasets show that NN-sort yields performance
improvement by up to 10.9x over traditional sorting algorithms.
