Auto-tuning Interactive Ray Tracing using an Analytical GPU Architecture Model
Ganestam, Per LU and Doggett, Michael LU
(2012)
GPGPU5
- Abstract
- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2374593
- author
- Ganestam, Per
LU
and Doggett, Michael
LU
- organization
- publishing date
- 2012
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- GPU Model, Ray Tracing, Auto-tuning, OpenCL
- host publication
- The ACM International Conference Proceedings Series
- pages
- 7 pages
- conference name
- GPGPU5
- conference location
- London, United Kingdom
- conference dates
- 2012-03-03
- external identifiers
-
- scopus:84858791144
- language
- English
- LU publication?
- yes
- id
- d0ec480a-349c-47a8-b893-adfb01879f1e (old id 2374593)
- date added to LUP
- 2016-04-04 13:04:54
- date last changed
- 2022-02-06 17:05:44
@inproceedings{d0ec480a-349c-47a8-b893-adfb01879f1e,
abstract = {{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 = {{Ganestam, Per and Doggett, Michael}},
booktitle = {{The ACM International Conference Proceedings Series}},
keywords = {{GPU Model; Ray Tracing; Auto-tuning; OpenCL}},
language = {{eng}},
title = {{Auto-tuning Interactive Ray Tracing using an Analytical GPU Architecture Model}},
url = {{https://lup.lub.lu.se/search/files/6048879/2374594.pdf}},
year = {{2012}},
}