It is well known in the high-performance computing (HPC) community that many (perhaps most) HPC workloads exhibit dynamic performance envelopes that can stress the memory, compute, network, and storage capabilities of modern supercomputers. Optimizing HPC workloads to run efficiently on existing hardware systems is challenging, but attempting to quantify the performance envelopes of HPC workloads to extrapolate performance predictions for HPC workloads on new system architectures is even more challenging, albeit essential. This predictive analysis is beneficial because it helps each data center’s supercomputer procurement team extrapolate to the new machines and system architectures that will deliver the most performance for production workloads at their datacenter. However, once a supercomputer is installed, configured, made available to users, and benchmarked, it is too late to consider fundamental architectural changes.