Lightweight, generative variant exploration for high-performance graphics applications

Beitrag bei einer Tagung

Details zur Publikation

Autorinnen und Autoren: Selgrad K, Lier A, Köferl F, Stamminger M, Lohmann D
Jahr der Veröffentlichung: 2015
Tagungsband: ACM Bd. 51, Nr. 3
Seitenbereich: 141-150
ISBN: 978-1-4503-3687-1
Sprache: Englisch


Rendering performance is an everlasting goal of computer graphics and significant driver for advances in both, hardware architecture and algorithms. Thereby, it has become possible to apply advanced computer graphics technology even in low-cost embedded appliances, such as car instruments. Yet, to come up with an efficient implementation, developers have to put enormous efforts into hardware/problem-specific tailoring, fine-tuning, and domain exploration, which requires profound expert knowledge. If a good solution has been found, there is a high probability that it does not work as well with other architectures or even the next hardware generation. Generative DSL-based approaches could mitigate these efforts and provide for an efficient exploration of algorithmic variants and hardware-specific tuning ideas. However, in vertically organized industries, such as automotive, suppliers are reluctant to introduce these techniques as they fear loss of control, high introduction costs, and additional constraints imposed by the OEM with respect to software and tool-chain certification. Moreover, suppliers do not want to share their generic solutions with the OEM, but only concrete instances. To this end, we propose a light-weight and incremental approach for meta programming of graphics applications. Our approach relies on an existing formulation of C-like languages that is amenable to meta programming, which we extend to become a lightweight language to combine algorithmic features. Our method provides a concise notation for meta programs and generates easily sharable output in the appropriate C-style target language.

FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Köferl, Franz
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Lier, Alexander
Lehrstuhl für Informatik 9 (Graphische Datenverarbeitung)
Lohmann, Daniel PD Dr.
Technische Fakultät
Selgrad, Kai
Lehrstuhl für Informatik 9 (Graphische Datenverarbeitung)
Stamminger, Marc Prof. Dr.
Lehrstuhl für Informatik 9 (Graphische Datenverarbeitung)


Selgrad, K., Lier, A., Köferl, F., Stamminger, M., & Lohmann, D. (2015). Lightweight, generative variant exploration for high-performance graphics applications. In ACM Bd. 51, Nr. 3 (pp. 141-150). Pittsburgh, PA, US.

Selgrad, Kai, et al. "Lightweight, generative variant exploration for high-performance graphics applications." Proceedings of the 14th International Conference on Generative Programming: Concepts & Experiences (GPCE), Pittsburgh, PA 2015. 141-150.


Zuletzt aktualisiert 2018-26-11 um 20:53