preprint · Open Collections · 2022
Towards Parallel Learned Sorting
Cited by 0Open access
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.
Cite this paper
Carvalho, I. (2022). Towards Parallel Learned Sorting. Open Collections. https://doi.org/10.14288/1.0417515
Carvalho, Ivan. “Towards Parallel Learned Sorting.” Open Collections, 2022, https://doi.org/10.14288/1.0417515.
Carvalho, Ivan. 2022. “Towards Parallel Learned Sorting.” Open Collections. https://doi.org/10.14288/1.0417515.
Carvalho, I. (2022) ‘Towards Parallel Learned Sorting’, Open Collections. Available at: https://doi.org/10.14288/1.0417515.
@misc{carvalho2022towards,
title = {{Towards Parallel Learned Sorting}},
author = {Carvalho, Ivan},
journal = {Open Collections},
year = {2022},
doi = {10.14288/1.0417515}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.