A State-Based Energy/Performance Model for Parallel Applications on Multicore Computers

In this paper, we propose a state-based energy/performance model for a given parallel application on multicore computer systems. By quantifying energy consumptions at fine-grained levels, defined as states, we analyze the energy/performance impact by taking into account the application characteristics and energy features of multicore computers. By combining Amdahl´s Law with our proposed model, we investigate the parallel degree and computation intensity of a given application, and derive the optimal number of cores and frequencies to achieve the minimum energy consumption. We also explore the extensions of energy/performance-efficiency metrics including Energy Per Speedup^α (EPS^α), Power Per Speedup^α (PPS^α), Dynamic Energy Per Speedup^α (DEPS^α) and Dynamic Power Per Speedup^α (DPPS^α), which use speedup with a weight α to better reflect the energy/performance tradeoffs, especially for parallel applications on multicore platforms. Our proposed state-based energy/performance model and metrics provide novel approaches on estimating the energy/performance impact at the fine-grained level, and offer guidance in achieving tradeoffs between performance and energy consumption for parallel applications on multicore platforms.