EvoSort: a genetic-algorithm-based adaptive parallel sorting framework for large-scale high performance computing
Year: 2025
Type: article
Abstract: We present EvoSort, a general-purpose adaptive parallel sorting framework accessible at the Python level. EvoSort employs a Genetic Algorithm (GA) to automatically discover and refine critical parameters, including insertion sort thresholds and algorithm selection (mergesort vs. LSD radix sort). By adapting continuously to input data and system architecture, EvoSort provides a drop-in replacement for standard Python routines like NumPy and Pandas. Experiments on up to 1010 (10 billion) elements across nine data distributions and two hardware platforms demonstrate that EvoSort consistently outperforms competing methods. Results show speedups of up to 225×, exemplifying a powerful auto-tuning solution for large-scale data processing.
Authors: Shashank Raj, Kalyanmoy Deb
Institution: Michigan State University
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414581553