Power/accuracy-aware dynamic workload optimization combining application autotuning and runtime resource management on homogeneous architectures

Nowadays, most computing systems experience highly dynamic workloads with performance-demanding applications entering and leaving the system with an unpredictable trend. Ensuring their performance guarantees led to the design of adaptive mechanisms, including (i) application autotuners, able to optimize algorithmic parameters (e.g., frame resolution in a video processing application), and (ii) runtime resource management to distribute computing resources among the running applications and tune architectural knobs (e.g., frequency scaling). Past work investigates the two directions separately, acting on a limited set of control knobs and objective functions; instead, this work proposes a combined framework to integrate these two complementary approaches in a single two-level governor acting on the overall hardware/software stack. The resource manager incorporates a policy for computing resource distribution and architectural knobs to guarantee the required performance of each application while limiting the side effect on results quality and minimizing system power consumption. Meanwhile, the autotuner manages the applications’ software knobs, ensuring results’ quality and performance constraint satisfaction while hiding application details from the controller. Experimental evaluation carried out on a homogeneous architecture for workstation machines demonstrates that the proposed framework is stable and can save more than 72% of the power consumed by one-layer solutions.