A key motivation behind the success of Cloud Computing is that virtualization allows significant energy and cost savings by sharing physical resources. Another source of savings in virtualized architectures is the use of h/w accelerators (e.g. GPUs, FPGAs). This paper analyzes the performance achieved by a computationally demanding task running on a commodity server when a GPU-based accelerator is adopted. In the analysis, the VP8 video encoder has been used, with its most intensive functional block (motion estimation) implemented in the GPU. A simple but effective model to predict the achieved CPU usage savings is provided, and experimentally validated. The performance achieved with different numbers of simultaneous encoding sessions and used CPU cores is presented and discussed. The results show that the hybrid CPU-GPU implementation can provide computational time savings from 20% to 300%, without any quality degradation. The presented results have been obtained within the FP7 T-NOVA Project.
GPU-based VP8 encoding: performance in native and virtualized environments / P. Paglierani, G. Grossi, F. Pedersini, A. Petrini - In: Telecommunications and Multimedia (TEMU), 2016 International Conference on[s.l] : IEEE, 2016 Aug 25. - ISBN 9781467384094. - pp. 52-56 (( convegno International Conference on Telecommunications and Multimedia (TEMU) tenutosi a Heraklion nel 2016.
|Titolo:||GPU-based VP8 encoding: performance in native and virtualized environments|
GROSSI, GIULIANO (Secondo)
PEDERSINI, FEDERICO (Penultimo)
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
Settore ING-INF/03 - Telecomunicazioni
|Data di pubblicazione:||25-ago-2016|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/TEMU.2016.7551915|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|