Towards Parallel Learned Sorting

TL;DR

This paper presents IPLS, a parallel learned sorting algorithm combining ML models with IPS4o, demonstrating competitive performance and showcasing the potential of parallel learned sorting methods.

Contribution

The work introduces IPLS, the first parallel ML-enhanced sorting algorithm integrating IPS4o with learned data partitioning, advancing the field of parallel sorting techniques.

Findings

IPLS is competitive with existing sorting algorithms.

Using IPS4o framework is promising for parallel learned sorting.

Experimental results validate the effectiveness of IPLS.

Abstract

We introduce a new sorting algorithm that is the combination of ML-enhanced sorting with the In-place Super Scalar Sample Sort (IPS4o). The main contribution of our work is to achieve parallel ML-enhanced sorting, as previous algorithms were limited to sequential implementations. We introduce the In-Place Parallel Learned Sort (IPLS) algorithm and compare it extensively against other sorting approaches. IPLS combines the IPS4o framework with linear models trained using the Fastest Minimum Conflict Degree algorithm to partition data. The experimental results do not crown IPLS as the fastest algorithm. However, they do show that IPLS is competitive among its peers and that using the IPS4o framework is a promising approach towards parallel learned sorting.