Mustafa Mert Özyılmaz

Mustafa Mert Özyılmaz

ARM-based and x86-64 laptop processors differ not only in instruction-set design, but also in memory hierarchy, core organization, system integration, and power-management mechanisms. This study presents a combined architectural and experimental comparison of an Apple M3 system and an AMD Ryzen 7 3750H system. The architectural analysis contrasts AArch64's fixed-width load-store design with the variable-length, memory-operand-rich x86-64 instruction model, and discusses how register organization, calling conventions, heterogeneous core organization, memory behavior, and low-power mechanisms shape observed performance and energy characteristics. The experimental part uses two native assembly benchmarks: a recursive Fibonacci workload and an integer matrix-multiplication workload. The analysis combines repeated timing measurements, processor-energy measurements, and cross-platform microarchitectural counter measurements from matched portable-C profiling runs. The Ryzen platform is decisively faster on the branch-heavy Fibonacci benchmark, while matrix multiplication shows no meaningful timing advantage for either platform in the present measurements. In contrast, the Apple platform is markedly more energy-efficient, reducing energy-to-solution by approximately 5.82$\times$ on Fibonacci and 6.38$\times$ on matrix multiplication. These results are interpreted as platform-level findings rather than as pure ISA-only effects, reflecting differences in implementation, system integration, and measurement methodology in addition to instruction-set structure.

Mustafa Mert Özyılmaz

The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) is a fundamental NP-hard optimization problem in logistics. Solving large-scale instances remains computationally challenging for exact solvers. This work introduces a multilevel graph coarsening and refinement framework that aggregates customers into meta-nodes using a spatio-temporal distance metric. The reduced problem is solved with classical heuristics and subsequently expanded back into the original space with feasibility corrections. Preliminary experiments on Solomon benchmark instances show that the proposed method reduces computation time while preserving or improving solution quality, particularly with respect to capacity and time window constraints. The paper also explores the integration of quantum-inspired optimization techniques, highlighting their potential to further accelerate large-scale vehicle routing tasks.