In this article, we dive deep into how we can harness parallel computing—specifically MPI—to scale the Discrete Fourier Transform algorithm from a single-core, “serial” version all the way to runs on the ARCHER2 supercomputer. Along the way, I explore the nuances of Amdahl’s Law and Gustafson’s Law, showing how these classic models help predict and interpret speedup behaviour as we add more processors.
Even if you’re not into HPC, the concepts of strong scaling (speeding up the same problem by adding more resources) and weak scaling (tackling proportionally bigger problems as we add more processors) are essential across various compute‐intensive fields. I illustrate these principles with real performance measurements and detailed code snippets demonstrating the MPI implementation.
Whether you’re looking to optimize scientific simulations, process massive datasets, or simply curious about HPC, I hope you’ll find some helpful insights! Feel free to comment with any thoughts or questions.
