Anas Gamal Aly

Parallel and Distributed Computing (PDC) is a critical yet conceptually challenging area of the undergraduate computer science curriculum. While students often encounter these concepts in theory, few gain exposure to experience in real high-performance computing (HPC) environments. Research shows that when students are engaged in project-based learning they retain knowledge more effectively. They also develop a deeper understanding of concepts taught in the classroom. This paper presents a practical assignment in which students engage directly with the University of Florida’s HiPerGator supercomputer to implement and benchmark matrix multiplication using Python and C (via POSIX threads and OpenMP). Students navigate batch scheduling, core allocation, and performance tuning, experiences that are rarely accessible at the undergraduate level. We describe the assignment in detail and provide a three-year evaluation across multiple course offerings, highlighting how structured access to real HPC infrastructure can deepen student understanding of parallelism and multithreading.

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.