A self-adaptive approach to efficiently manage energy and performance in tomorrow's heterogeneous computing systems

ICT adoption rate boomed during the last decades as well as the power consumption footprint that generates from those technologies. This footprint is expected to more than triple by 2020. Moreover, we are moving towards an on-demand computing scenario, characterized by varying workloads, constituted of diverse applications with different performance requirements, and criticality. A promising approach to address the challenges posed by this scenario is to better exploit specialized computing resources integrated in a heterogeneous system architecture (HSA) by taking advantage of their individual characteristics to optimize the performance/energy trade-off of the overall system. Better exploitation although comes with higher complexity. System architects need to take into account the efficiency of systems units, i.e. GPP(s) either alone or with a single family of accelerators (e.g., GPUs or FPGAs), as well as the applications workload, which often leads to inefficiency in their exploitation, and therefore in performance/energy. The work presented in this paper will address these limitations by exploiting self-adaptivity to allow the system to autonomously decide which specialized resource to exploit for a carbon footprint reduction, due to a more effective execution of the application, optimizing goals that the user can set (e.g., performance, energy, reliability).