An $O(N)$ Sorting Algorithm: Machine Learning Sort

TL;DR

This paper introduces a machine learning-based sorting algorithm with linearithmic complexity, optimized for parallel processing on GPUs or TPUs, and applicable to sparse hash tables, demonstrating potential for large-scale data sorting.

Contribution

It presents a novel $O(N\,M)$ sorting algorithm leveraging machine learning, suitable for parallel hardware acceleration and sparse data structures.

Findings

Potential for efficient large-scale data sorting

Compatibility with GPU and TPU acceleration

Application to sparse hash tables

Abstract

We propose an $O(N\cdot M)$ sorting algorithm by Machine Learning method, which shows a huge potential sorting big data. This sorting algorithm can be applied to parallel sorting and is suitable for GPU or TPU acceleration. Furthermore, we discuss the application of this algorithm to sparse hash table.