Author(s): Lukas Schneider and Miriam Vogel

Abstract: This research examines cache-friendly data structures in the context of beginner-level algorithms, focusing on how memory access patterns influence practical performance beyond asymptotic complexity. While introductory algorithm courses emphasize Big-O analysis, modern processors rely heavily on cache hierarchies, making spatial and temporal locality critical to execution efficiency. The research compares arrays, linked lists, dynamic arrays, hash tables, and tree-based structures under common beginner algorithms such as linear search, traversal, insertion, and simple sorting. Controlled experiments were conducted using identical datasets, fixed compiler optimizations, and consistent hardware configurations to isolate cache behavior effects. Performance metrics included execution time, cache miss rates, and instruction counts. Results indicate that contiguous-memory structures, particularly arrays and dynamic arrays, consistently outperform pointer-based structures in traversal-heavy tasks due to superior cache utilization. Linked lists and naïve tree implementations exhibited higher cache miss penalties, even when theoretical complexity was comparable. Hash tables demonstrated mixed behavior, with cache efficiency strongly dependent on load factor and collision resolution strategy. The findings highlight a persistent gap between theoretical instruction and real-world performance intuition for novice programmers. By demonstrating measurable performance differences using simple algorithms, the research provides pedagogical evidence that cache awareness can be introduced early without overwhelming learners. The comparative analysis supports integrating memory locality concepts into beginner curricula to foster more accurate mental models of performance. Ultimately, the research argues that teaching cache-friendly data structure selection alongside algorithmic complexity improves code efficiency, scalability, and systems-level understanding. These insights are intended to guide educators in curriculum design and help beginners develop performance-conscious programming habits from the outset, aligning foundational algorithm education with contemporary hardware realities. Such alignment reinforces practical reasoning, encourages empirical evaluation, and bridges theory with systems thinking, enabling novices to write efficient programs while appreciating hardware constraints encountered in modern computing environments during early academic and professional development.

DOI: 10.33545/27076636.2026.v7.i1a.145

Pages: 01-05 | Views: 101 | Downloads: 35

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