Predicting Optimal Power Allocation for CPU and DRAM Domains

Constraints imposed by power delivery and costs will be key design impediments to the development of next generation High-Performance Computing (HPC) systems. To remedy these impediments, solutions that impose power bounds (or caps) on over-provisioned computing systems to remain within the physical (and financial) power limits have been proposed. Uninformed power capping can significantly impact performance and power capping's success depends largely on how intelligently a given power budget is allocated across various subsystems of the computing nodes. Since different computations put vastly different demands on various system components, those variations in the demands must be taken into consideration while making power allocation decisions to lessen performance degradation. Given a target power bound, a model-based methodology presented in this paper, which takes computation-specific properties into account, guides power allocations for CPU and DRAM domains to maximize performance. Our methodology is accurate and can predict the performance impacts of the power capping allocation schemes for different types of computations from real applications with absolute mean error of less than 6%.