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The SBC can leverage Graphics Processing Units (GPUs) for increased transcoding capacity of T-SBC instances deployed in OpenStack cloud environments (Newton and above). GPU acceleration significantly increases transcoding capacity of virtual instances, which otherwise would have a limited scale. In many cases a 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 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.

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G.722_Silence_Suppression_Note G.722_Silence_Suppression_Note

GPU Transcoding

Prior to SBC 8.0 release, for GPU-based solutions, the 

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could not offer more than one transcodable codec in the outgoing offer for following reasons:

• Unlike CPU-based DSP that supports all the codecs, the GPU-based DSP supports specific codecs.
• For GPU, if the configured codecs do not reside on the same DSP process, they cannot be used with a single DSP allocation.
• Unaware of the cluster capabilities, the signaling SBC removes the codecs from the outgoing offer, which cannot be supported by the reserved DSP.

 

The

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 is enhanced to offer all the configured transcodable codecs that the 

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supports, in the outgoing offer and address the above GPU gaps.

The configured codecs include:

• Codecs configured for transcoding
• Codecs supported by same T-SBC instance that supports codecs received in the offer.

Supported Deployment Scenarios

GPU acceleration is currently supported on SBC SWe 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 the SBC SWe 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 Devices

The following NVIDIA GPU devices are supported.

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.

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OPUS Encoding

OPUS on GPU supports the following:

• Input sampling rates of 8Khz (NB) and 16Khz (WB) is supported on the IDP interface (encoder input and decoder output)
• For the OPUS encoder, the only supported frame size is 20ms . 
• However, the OPUS decoder supports decoding of 10ms, 20ms, 40ms and 60ms frames and packets of size 10ms through 60ms.
• Encoding output bandwidths of 8Khz and 16Khz are supported for Opus .
• Inband FEC encoding is supported.
• Inband FEC decoding is supported except for 10ms frame size.
• Variable Bit Rate (VBR) as well as Constant Bit Rate (CBR) is supported
• DTX mode is supported.
• Only single channel mode is supported
• RFC2833 and OOB transmission and reception is supported on OPUS leg.

For the Opus Encoder, the following configs are supported.

• Application   - VOIP mode
• Complexity   - 3
• Channels      - 1 ( single channel encoding)
• Frame size   - 20ms 
• cVbr              - 0 (disabled)
• Mode            - SILK mode only

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0 Table 1 Feature Differences between CPU and GPU
Feature CPU GPU Native Frame Size (for Encoder) 10ms 20ms Packetization Times Supported 10ms, 20ms, 30ms, 40ms, 50ms, 60ms 20ms, 40ms and 60ms

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. 

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