Scientific Computing Energy Footprinting Across CPU Generations
Jacob D. Hauenstein, Timothy S. Newman · 2025
A multi-dimensional study of the energy footprint for scientific computing across CPU generations is presented. The study utilizes a version of the popular Linpack benchmark, which is analogous to the HPL (High Performance Linpack) used today for TOP500 Supercomputer ranking, as a proxy for the computational performance of scientific computing. Here, this Linpack benchmark is applied to three generations of (the same level of) Intel x86 CPUs. Energy consumption of the benchmark's computation is considered via exploitation of onboard monitors. By considering metrics for power usage, time, and computational productivity in conjunction with computational strategies and runtime conditions, this study delivers guidance for achievement of computation goals related to computation time and energy consumption. Tradeoffs between these two, including marginal energy costs and time benefits for computational performance improvement, are also explored. Results for a wide array of experiments are reported.