Avid by Shailendra Mathur
Hi! Just to introduce myself, I am the Chief Architect for Video products at Avid. In this role, I provide architecture and technology oversight to the editing, video server and broadcast graphics products at Avid. In this blog, I'd like to provide a preview of a topic I will be presenting on at the NVIDIA GPU Technology Conference (GTC) 2013 at the San Jose Convention Center in California on March 19th. The topic of my presentation is Avid's Stereoscopic Editorial Architecture - Light Fields, Intelligent Computing and Beyond.
The formats required for stereoscopic production and post production are a good indication of how the data rates are increasing in video and film productions, and why scalable and adaptable compute architectures are needed. Beyond the trend of increased resolution, frame rate and color bit-depth, the other trend in data rates that stereoscopic workflows represent is the number of views that need to be processed when editing a scene. Multiple possibilities for more creative story telling open up when we go beyond mono and stereo image capture. To build toward these possibilities, we, the Avid design and engineering team, inspired ourselves from an area of research called Light Fields, as well as a storied past in Multi-cam editing to develop the stereoscopic editing and data management architecture in Avid Media Composer 6.0. These topics will be the subject of another blog to follow.
In this blog, I will give you a brief introduction to another aspect of the GTC talkâ€”a unique heterogeneous compute architecture that we built to scale up to the performance requirement that high data rate formats such as Stereoscopic 3D pose to maintain a seamless editing experience. We call this the Avid Intelligent Compute Architecture. Some of you may have also heard this referred to by the engineering name of the original project, ACPL (Avid Component Processing Library). Boy, we love our acronyms at Avid!
This compute architecture was initially developed when Avid Media Composer v3.0 and DS v10 came out in 2008 and has since been leveraged to rapidly add new processing formats and video processing functionality. The architecture served to replace the older FPGA-only Nitris classic acceleration with a player that could load balance processing across FPGA, Multi-threaded CPUs and GPU based processing. Rather than targeting just the GPU, just the CPU, or just the FPGA based cards, the philosophy changed to use them all in a holistic fashion. Rather than using a single PCIe card or a break-out box with FPGA compute acceleration, the whole system is turned into an accelerator. This required us to build a player as well as a scalable hardware abstraction framework that allowed new compute hardware and corresponding processors running on them to be plugged into existing applications without having to change the application code to accommodate them. The Intelligent media player in the application acts as an orchestra conductor, keeping as many of the resources playing to provide the performance required. Keeping a holistic view of the whole system in mind, particular attention is paid to the cost of transferring heavy video data across the system bus when deciding which compute hardware should be used for a particular process. read more...