GPU Transcoding

This feature provides support for leveraging Graphics Processing Units (GPUs) for increasing transcoding capacity of SBC SWe instances, and the ability to instantiate the same on an OpenStack cloud. GPU acceleration significantly increases transcoding capacity of virtual instances, which otherwise would have a limited scale. In many cases GPU-accelerated solution performs better than specialized DSP hardware-based solutions. GPU-based solutions have the following benefits:

• GPUs are ubiquitous, they are being offered as Commercial Off-the-Shelf (COTS) solutions by major hardware and cloud vendors.
• GPU-based solutions help leverage the steep rise in computing power delivered by increased industry investments in GPU technology.
• GPUs have diverse applications. Unlike specialized DSP hardware, GPU devices can be reused for other applications.
• GPUs can be leveraged by cloud-based virtual instances.

Supported Deployment Scenarios

GPU acceleration is currently supported on SBC SWe cloud-based T-SBC instances on OpenStack (Newton and above). T-SBC is a component in Distributed SBC architecture that provides transcoding service.

GPU devices are attached to SBC cloud instances through PCIe pass-through – a single GPU device can be used by only one instance at a time. The process of enabling PCIe pass-through in OpenStack is detailed later in this document. For performance considerations, NUMA locality of devices should be ensured.

Supported GPU Device

NVIDIA V100(PCIe)

Supported Codecs

• AMR-NB
• G729
• G722
• AMR-WB

In addition, G.711 is supported for GPU instances, but only when G.711 is being transcoded to a non-G.711 codec. You cannot currently configure transcoding from G.711 to G.711 on GPU instances. The coding rates and packetization times for the supported codecs are shown in the tables on the Audio Codecs page.

Feature Comparison of CPU and GPU T-SBC Solutions

While GPU-based T-SBCs offer marked increase in scale when compared to CPU based T-SBCs, there are some caveats with the GPU solution that are highlighted in the following table.