Result-scalability: Following the evolution of selected social impact of HPC

Abstract

While the scientific community traditionally relies on various computational metrics to assess the performance of HPC systems –such as the TOP500 list (based on HPL performance), HPCG, Graph500, IO500– these metrics do not capture how HPC contributes to social progress. We propose a novel approach to follow how the growth of HPC systems and the advances of HPC research address concrete social challenges. The uniqueness of these new metrics lies in their ability to not only measure the capabilities of HPC architectures but also to gauge the concrete social advancements achieved through their use: it focuses on the output of the computation instead of its input. Contrarily to current measure, it also promotes the diversity of machines by evaluating the Pareto front created between size and result. We emphasize the need for dynamic, community-driven metrics that can evolve with emerging social needs.