A Novel Graphical Technique for Optimal High-Performance Computing Hardware Selection

We present a novel graphical methodology for multi-variable, multi-objective optimization of High-Performance Computing (HPC) hardware selection and allocation. HPC performance rarely scales linearly with increasing CPU core count, and this scaling behavior varies for different types of hardware and different categories of models. Our methodology guides the optimization process with novel normalizations of speed and cost and an accompanying plotting method. This produces a Pareto front allowing easy and intuitive comparison of alternatives and reevaluation of such when the user's goals change. We apply this methodology and demonstrate universal scaling behavior across four hardware types and three computational fluid dynamics models with mesh sizes from 3E5 to 5E7 cells.