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

Per Ganestam, Michael Doggett

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceedingPeer review

194 Nedladdningar (Pure)


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.
Titel på värdpublikationThe ACM International Conference Proceedings Series
Antal sidor7
StatusPublished - 2012
EvenemangGPGPU5 - London, Storbritannien
Varaktighet: 2012 mars 3 → …


Period2012/03/03 → …

Ämnesklassifikation (UKÄ)

  • Datavetenskap (datalogi)


Utforska forskningsämnen för ”Auto-tuning Interactive Ray Tracing using an Analytical GPU Architecture Model”. Tillsammans bildar de ett unikt fingeravtryck.

Citera det här