Autonomic Power Management in Speculative Simulation Runtime Environments

While transitioning to exascale systems, it has become clear that power management plays a fundamental role to support a viable utilization of the underlying hardware, also performance-wise. To meet power restrictions imposed by future exascale supercomputers, runtime environments will be required to enforce self-tuning schemes to run dynamic workloads under an imposed power cap. Literature results show that, for a wide class of multi-threaded applications, tuning both the degree of parallelism and frequency/voltage of cores allows a more effective use of the budget, compared to techniques that use only one of these mechanisms in isolation. In this paper, we explore the issues associated with applying these techniques on speculative Time-Warp based simulation runtime environments. We discuss how the differences in two antithetical Time Warp-based simulation environments impact the obtained results. Our assessment confirms that the performance gains achieved through a proper allocation of the power budget can be significant. We also identify the research challenges that would make these form of self-tuning more broadly applicable.