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.

翻译：本文提出一种神经网络赋能的感知数据分布排序方法，命名为NN-sort。该方法探索了利用深度学习技术加速大规模排序操作的潜力，使感知数据分布的排序成为一种深度学习服务。与传统的基于两两比较的排序算法（通过执行两两操作对数据元素进行排序）相比，NN-sort利用神经网络模型学习数据分布，并利用该分布将大规模数据元素映射为有序序列。实验证明了使用NN-sort的显著优势。在合成数据集和真实数据集上的测量结果表明，NN-sort相比传统排序算法可获得2.18倍至10倍的性能提升。