Memory Throttling on BG/Q: A Case Study with Explicit Hydrodynamics

Power and energy efficiency are major concerns in future supercomputing systems. We expect that applications will be constrained to operate under a power budget and achieving the expected levels of performance will be challenging. Understanding how power is consumed by an application throughout its different phases will be necessary to shift power to those resources on the critical path. In this paper, we identify opportunities for shifting power between components for a representative kernel of explicit hydrodynamics codes. Based on a linear regression model, we dynamically throttle the memory system in regions with low memory bandwidth requirements on an energy-efficient supercomputer. Our results show that we can save a significant amount of power that could be used on resources on the critical path and, thus, maximize performance under the operating power budget.