# Deep Learning Service for Efficient Data Distribution Aware Sorting

arXiv: https://arxiv.org/abs/1907.08817 · HTML: https://mlsanity.com/paper/1907.08817/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [Clustering Plotted Data by Image Segmentation](https://mlsanity.com/paper/2110.05187/) (2021)

## Abstract (arXiv)

In this paper, we present a neural network-enabled data distribution aware sorting method, coined as NN-sort. Our approach explores the potential of developing deep learning techniques to speed up large-scale sort operations, enabling data distribution aware sorting as a deep learning service. Compared to traditional pairwise comparison-based sorting algorithms, which sort data elements by performing pairwise operations, NN-sort leverages the neural network model to learn the data distribution and uses it to map large-scale data elements into ordered ones. Our experiments demonstrate the significant advantage of using NN-sort. Measurements on both synthetic and real-world datasets show that NN-sort yields 2.18x to 10x performance improvement over traditional sorting algorithms.