A Dynamic Programming Framework for DVFS-Based Energy-Efficiency in Multicore Systems

Per-core Dynamic Voltage and Frequency (V/F) Scaling (DVFS) is a well-known methodology for achieving energy efficiency in multicore systems. Heuristic DVFS techniques provide fast, suboptimal V/F predictions while Dynamic Programming (DP) methods solve smaller sub-problems iteratively and use their outcomes to evaluate V/F levels globally, but at the cost of overhead delays. We propose an efficient DP framework using the Viterbi algorithm, which uses the Energy-Delay Product (EDP) as an objective function to predict the best V/F levels using applications' profiled information, to minimize energy consumption and execution time. Experimental results show that our framework outperforms heuristics using the EDP criteria and provides near-optimal solutions when maximizing energy saving is as, or more, important than minimizing execution time penalty. In fact, across several benchmarks, our proposed algorithm provides from a 12 to 75 percent improvement in EDP compared to heuristic methods. Furthermore, using a Pareto frontier to evaluate solutions of the algorithms under study, we demonstrate that our framework's energy-time solution is on average only 9 percent worse than the optimal solution. In addition, we show that our dynamic programming solution is 3 to 18 percent closer to a theoretical lower-bound when compared to the studied heuristic methods.