Auto-Tuning Interactive Ray Tracing using an Analytical GPU Architecture Model (ACM)
This paper presents a method for auto-tuning interactive ray tracing on GPUs using a hardware model. Getting full performance from modern GPUs is a challenging task. Workloads which require a guaranteed performance over several runs must select parameters for the worst performance of all runs. Our method uses an analytical GPU performance model to predict the current frame‘s rendering time using a selected set of parameters. These parameters are then optimised for a selected frame rate performance on the particular GPU architecture. We use auto-tuning to determine parameters such as phong shading, shadow rays and the number of ambient occlusion rays. We sample a priori information about the current rendering load to estimate the frame workload. A GPU model is run iteratively using this information to tune rendering parameters for a target frame rate. We use the OpenCL API allowing tuning across different GPU architectures. Our auto-tuning enables the rendering of each frame to execute in a predicted time, so a target frame rate can be achieved even with widely varying scene complexities. Using this method we can select optimal parameters for the current execution taking into account the current viewpoint and scene, achieving performance improvements over predetermined parameters.
Paper available at ACM.