Auto-tuning Interactive Ray Tracing using an Analytical GPU Architecture Model

Summary, in English

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 analyti- cal GPU performance model to predict the current frame’s render- ing time using a selected set of parameters. These parameters are then optimised for a selected frame rate performance on the partic- ular GPU architecture. We use auto-tuning to determine parameters such as phong shading, shadow rays and the number of ambient oc- clusion rays. We sample a priori information about the current ren- dering 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 render- ing 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 cur- rent execution taking into account the current viewpoint and scene, achieving performance improvements over predetermined parame- ters.

• Author: Per Ganestam; Michael Doggett
• Department/s: Department of Computer Science; ELLIIT: the Linköping-Lund initiative on IT and mobile communication; Parallel Systems
• Publishing year: 2012
• Language: English
• Publication/Series: The ACM International Conference Proceedings Series
• Full text: - 447 kB; Download statistics
• Document type: Conference paper
• Topic: Computer Science
• Keywords: GPU Model; Ray Tracing; Auto-tuning; OpenCL
• Conference name: GPGPU5
• Conference date: 2012-03-03
• Conference place: London, United Kingdom
• Status: Published
• Research group: Computer Graphics