EvoSort: a genetic-algorithm-based adaptive parallel sorting framework for large-scale high performance computing
Shashank Raj, Kalyanmoy Deb · International Journal of Parallel Emergent and Distributed Systems · 2025
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.