Towards Parallel Learned Sorting

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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	arXiv.org
1. Verfasser:	Carvalho, Ivan
Format:	Paper
Sprache:	Englisch
Veröffentlicht:	Ithaca Cornell University Library, arXiv.org 14.08.2022
Schlagworte:	Sorting algorithms
ISSN:	2331-8422
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	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.
Bibliographie:	SourceType-Working Papers-1 ObjectType-Working Paper/Pre-Print-1 content type line 50
ISSN:	2331-8422
DOI:	10.48550/arxiv.2208.06902