Science per Dollar: Modeling Emerging Node Architectures for Accelerator-centric Computing
Jordan M. Abt, Ali Farazdaghi, Elizabeth Reid, Curtis Shorts, Tooraj Taraz, Zachary Silva, Ethan Shama, Scott Levy, Whit Schonbein, Matthew G. F. Dosanjh, Amirreza Barati Sedeh, Ryan Eric Grant · 2025
New High Performance Computing (HPC) node architectures such as superchips and Composable Disaggregated Infrastructure have recently emerged as potential directions for the future of supercomputing. However, with new architectures available at different costs, it can be difficult to forecast the correct design decision to maximize science performed per dollar. In this paper, we present a model designed to determine the performance of workloads on different node architectures with a fixed budget. We demonstrate how this model can be applied to narrow design decisions when using a variety of workloads including molecular dynamics applications, machine learning applications, a heat diffusion simulator, and an atomic reactor simulator. Using detailed traces of GPU operations, we justify the components of the model and determine if the model is accurate enough to have decision assisting capability. Specifically, we analyze the results of the model in relation to the GPU utilization and data movement of the workloads and discuss other factors to consider when choosing a node architecture for a datacenter. We find that the model can aid in deciding which node architectures to consider for a supercomputer deployment, using detailed tracing of a variety of representative applications and proxy applications for HPC to demonstrate the utility of the model.