SeeSAw: Optimizing Performance of In-Situ Analytics Applications under Power Constraints

Abstract

Future supercomputers will need to operate under a power budget. At the same time, in-situ analysis—where a set of analysis tasks are concurrently executed and periodically communicate with a scientific simulation—is expected to be a primary HPC workload to overcome the increasing gap between the performance of the storage system relative to the computational capabilities of these machines. Ongoing research focuses on efficient coupling of simulation and analysis considering memory or I/O constraints, but power poses a new constraint that has not yet been addressed for these workflows. There are two state-of-the-art HPC power management approaches: 1) a power-aware scheme that measures and reallocates power based on observed usage and 2) a time-aware scheme that measures the relative time between communicating software modules and reallocates power based on timing differences. We find that considering only one feedback metric has two major drawbacks: 1) both approaches miss opportunities to improve performance and 2) they often make incorrect decisions when facing the unique requirements of in-situ analysis. We therefore propose SeeSAw—an application-aware power management approach, which uses both time and power feedback to balance a power budget and maximize performance for in-situ analysis workloads. We evaluate SeeSAw using the molecular dynamics simulation LAMMPS with a set of built-in analyses running on the Theta supercomputer on up to 1024 nodes. We find that the strictly power-aware approach slows down LAMMPS as much as ∼25%. The strictly time-aware approach shows improvements of up to ∼13% and slowdowns as much as ∼60%. In contrast, SeeSAw achieves ∼4–30% performance improvements.