A Study of Dynamic Phase Adaptation Using a Dynamic Multicore Processor.

Heterogeneous processors such as ARM’s big.LITTLE have become popular for embedded systems. They offer a choice between running workloads on a high performance core or a low-energy core leading to increased energy efficiency. However, the core configurations are fixed at design time which offers a limited amount of adaptation. Dynamic Multicore Processors (DMPs) bridge the gap between homogeneous and fully reconfigurable systems. Cores can fuse dynamically to adapt the computational resources to the needs of different workloads. There exists multiple examples of DMPs in the literature, yet the focus has mainly been on static partitioning. This paper conducts the first thorough study of the potential for dynamic reconfiguration of DMPs at runtime. We study how performance varies with static partitioning and what software optimizations are required to achieve high performance. We show that energy consumption is reduced considerably when adapting the number of cores to program phases, and introduce a simple online model which predicts the optimal number of cores to use to minimize energy consumption while maintaining high performance. Using the San Diego Vision Benchmark Suite as a use case, the dynamic scheme leads to ∼40% energy savings on average without decreasing performance.