GDP: A Greedy Based Dynamic Power Budgeting Method for Multi/Many-Core Systems in Dark Silicon

Abstract

Dark silicon phenomenon is significant in today's multi/many-core systems manufactured using new generation technology. In order to enhance performance of dark silicon systems, power budget constrained dynamic optimizations are performed in various ways including dynamic voltage and frequency scaling (DVFS) and task scheduling. However, power budgets given by existing methods are generally over pessimistic, which greatly limit the capability of dynamic performance optimization methods. In order to resolve this problem, we propose a dynamic power budgeting method, called Greedy based Dynamic Power (GDP). Different from existing methods, which are steady state based and ignore active core distributions, GDP formulates the power budgeting problem as a thermal-constrained combinational power optimization problem. To efficiently solve this problem, we propose two new ideas: first, we transform the original power-optimization problem to an easier solving temperature-optimization problem; second, we employ a more efficient greedy based algorithm that finds a sub-optimal active core distribution which maximizes power budget. The new method can consider current temperature states and transient thermal effects, which were ignored by existing methods. Both theoretical studies and experimental results show that GDP outperforms existing methods by providing a higher and less pessimistic power budget with low computing cost and guaranteed thermal safety.