GPU-based VP8 encoding: Performance in native and virtualized environments

Abstract

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.