TY - GEN
T1 - Remote parallel rendering for high-resolution tiled display walls
AU - Nachbaur, Daniel
AU - Dumusc, Raphael
AU - Bilgili, Ahmet
AU - Hernando, Juan
AU - Eilemann, Stefan
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported in part by the Blue Brain Project, the SwissNational Science Foundation under Grant 200020-129525, by theSpanish Ministry of Economy and Competitiveness under the CajalBlue Brain Project, the European Union Seventh Framework Programme(FP7/2007-2013) under grant agreement no. 604102 (HumanBrain Project) and the King Abdullah University of Science andTechnology (KAUST) through the KAUST-EPFL alliance for NeuroinspiredHigh Performance Computing. We would also like to thankgithub for providing an excellent infrastructure hosting our projects athttp://github.com/Bluebrain and http://github.com/Eyescale.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
PY - 2014/11
Y1 - 2014/11
N2 - © 2014 IEEE. We present a complete, robust and simple to use hardware and software stack delivering remote parallel rendering of complex geometrical and volumetric models to high resolution tiled display walls in a production environment. We describe the setup and configuration, present preliminary benchmarks showing interactive framerates, and describe our contributions for a seamless integration of all the software components.
AB - © 2014 IEEE. We present a complete, robust and simple to use hardware and software stack delivering remote parallel rendering of complex geometrical and volumetric models to high resolution tiled display walls in a production environment. We describe the setup and configuration, present preliminary benchmarks showing interactive framerates, and describe our contributions for a seamless integration of all the software components.
UR - http://hdl.handle.net/10754/599496
UR - http://ieeexplore.ieee.org/document/7013217/
UR - http://www.scopus.com/inward/record.url?scp=84946688141&partnerID=8YFLogxK
U2 - 10.1109/ldav.2014.7013217
DO - 10.1109/ldav.2014.7013217
M3 - Conference contribution
SN - 9781479952151
SP - 117
EP - 118
BT - 2014 IEEE 4th Symposium on Large Data Analysis and Visualization (LDAV)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -