Dynamic voltage and frequency scaling for 3D graphics applications on the state-of-the-art mobile GPUs

of the Dissertation Dynamic Voltage and Frequency Scaling for 3D Graphics Applications on the State-Of-The-Art Mobile GPUs by Navid Farazmand Doctor of Philosophy in Computer Engineering Northeastern University, February 2018 Dr. Kaeli, Adviser Today, there are more mobile computing devices running on batteries than computers connected to a power source, and this trend is bound to continue. Power consumption has always been one of the design considerations in digital electronic devices. However, low-power design in battery powered mobile devices has become a first-order design objective due to their unique characteristics of mobile applications. Dynamic Voltage and Frequency Scaling (DVFS) is a runtime power management technique commonly utilized to reduce power consumption, while achieving a predetermined level of performance. There have been numerous DVFS studies for general-purpose computing on CPUs and desktop Graphics Processing Units (GPU) over the past two decades. Nevertheless, DVFS for graphics workloads running on mobile GPUs has gained more attention in recent years and there remains a need for more research in this area. Mobile graphics workloads are an integrated part of the user experience on smartphones. Typical workloads range from User Interfaces (UIs), to mapping applications to browsers and mobile games. Thus, optimizing the energy consumption of mobile graphics workloads is essential for satisfactory operation of these devices. Early research into the potential benefits of DVFS for mobile graphics applications utilized emulated GPUs for evaluation. Follow up research focused on porting existing DVFS algorithms to mobile GPUs or enabling workload prediction for DVFS by taking advantage of the asynchronous nature of CPU and GPU operation. The goal of this thesis is to propose novel DVFS solutions for state-of-the-art mobile GPUs that improve energy consumption without negatively impacting performance guarantees. To achieve this goal, in this thesis we first introduce a fine-grained workload performance and energy consumption analysis framework for real-world use cases. Using this framework, we present a thorough