A Novel Performance Prediction Model for Mobile GPUs

Abstract

With the fast-growing development of mobile devices, the application of high-end three-dimensional (3-D) graphics is expanding to include usage in mobile platforms. Recent mobile application processors are equipped with a multicore CPU and a mobile GPU on a single chip, thereby enabling the incorporation of high-end 3-D graphics into mobile devices. The problem is that such features consume large amounts of power. Thus, previous studies focused on estimating the power consumption of mobile GPUs, but such research has been unfamiliar with user perspective. To address this deficiency, the current work developed a novel performance prediction model for mobile GPUs on Adreno. The model uses both the instruction throughput of a unified shader and GFLOPS. The utilization of the Adreno GPUs was adjusted to its maximum value to ensure that their performance is unaffected by dynamic voltage and frequency scaling and throttling functions. The model was validated using GFXBench under real game application environments. The simulation results provided the computational rates of each hardware unit of the Adreno GPUs and the rate of increase in the instruction processing of the unified shader. To verify the accuracy of the model, we compared the difference rates of the prediction results between those derived from the proposed model and those using Snapdragon profiler. The average error rate was 3.32% with three applications running on four different mobile devices.