Measuring Gauss-Seidel loop-carried dependency and fixing it via loop unrolling
The article examines how the Gauss‑Seidel algorithm’s loop‑carried dependency limits parallel execution. It describes a method to quantify the dependency’s impact on performance and shows that loop unrolling can reduce the dependency’s critical path. By applying this technique, the authors achieve measurable speedups on iterative solvers, demonstrating a practical optimization for high‑performance computing in modern CPU architectures.