I’m a third-year Computer Science student at Stetson University researching Algorithms and AI efficiency. My work integrating AlphaDev’s sorting networks with classical algorithms has achieved up to 140% performance improvements (accepted at ACMSE 2025). I serve as a Teaching Assistant for CS141 and previously taught data science at Hack The Hood. As Computer Science Club president, I’ve led technical initiatives including building a departmental server. I’m currently working on SunPark, which simplifies beach access in Florida by connecting beach-goers with available beach parking options.
Recent work by Google DeepMind introduced assembly-optimized sorting networks that achieve faster performance for small fixed-size arrays (3-8). In this research, we investigate the integration of these networks as base cases in classical divide-and-conquer sorting algorithms, specifically Merge Sort and Quick Sort, to leverage these efficient sorting networks for small subarrays generated during the recursive process. We conducted benchmarks with 11 different optimization configurations and compared them to classical Merge Sort and Quick Sort. We tested the configurations with random, sorted and nearly sorted arrays. Our optimized Merge Sort, using a configuration of three sorting networks (sizes 6, 7, and 8), achieves at least 1.5x speedup for random and nearly sorted arrays, and at least 2x speedup for sorted arrays, in comparison to classical Merge Sort. This optimized Merge Sort surpasses both classical Quick Sort and similarly optimized Quick Sort variants when sorting random arrays of size 10,000 and larger. When comparing our optimized Quick Sort to classical Quick Sort, we observe a 1.5x speedup using the 3-to-5 configuration on sorted arrays of size 10,000. The 6-to-8 configuration maintains a consistent 1.5x improvement across sorted arrays from 25,000 to 1 million elements. Our findings demonstrate the potential of integrating AI-optimized sorting networks to enhance the performance of classical sorting algorithms.
@inproceedings{aly2025improving,title={Improving {Merge Sort} and {Quick Sort} Performance by Utilizing {Alphadev's} Sorting Networks as Base Cases},author={Aly, Anas Gamal and Jensen, Anders E. and ElAarag, Hala},booktitle={Proceedings of the 2025 {ACM} Southeast Conference},series={ACMSE '25},year={2025},month=apr,pages={1--6},numpages={6},location={Cape Girardeau, MO, USA},publisher={Association for Computing Machinery},address={New York, NY, USA},note={In press},}
