Signature-Based Workload Estimation For Mobile 3d Graphics

Until recently, most 3D graphics applications had been regarded as too computationally intensive for devices other than desktop computers and gaming consoles. This notion is rapidly changing due to improving screen resolutions and computing capabilities of mass-market handheld devices such as cellular phones and PDAs. As the mobile 3D gaming industry is poised to expand, significant innovations are required to provide users with high-quality 3D experience under limited processing, memory and energy budgets that are characteristic of the mobile domain.Energy saving schemes such as Dynamic Voltage and Frequency Scaling (DVFS), as well as system-level power and performance optimization methods for mobile devices require accurate and fast workload prediction. In this paper, we address the problem of workload prediction for mobile 3D graphics. We propose and describe a signature-based estimation technique for predicting 3D graphics workloads. By analyzing a gaming benchmark, we show that monitoring specific parameters of the 3D pipeline provides better prediction accuracy over conventional approaches. We describe how signatures capture such parameters concisely to make accurate workload predictions. Signature-based prediction is computationally efficient because first, signatures are compact, and second, they do not require elaborate model evaluations. Thus, they are amenable to efficient, real-time prediction. A fundamental difference between signatures and standard history-based predictors is that signatures capture previous outcomes as well as the cause that led to the outcome, and use both to predict future outcomes. We illustrate the utility of signature-based workload estimation technique by using it as a basis for DVFS in 3D graphics pipelines.