1. AppliancesClustersMicrocode Trends in Post-production Infrastructure
Tom Burns
Technicolor Creative Services

2. Disclaimer
The views expressed herein are exclusively those of the presenter, and are not indicative of any Thomson / Technicolor official position nor an endorsement of any current or future technology development or strategy.

3. Appliances Turnkey workstations
Bosch FGS-4000, Quantel Paintbox, Ampex ADO
Proprietary circuit boards
Expensive & time-consuming to improve
Custom software
Steep learning curve for developers

4. Clusters High Performance Computing Fast, low-latency network
Shared storage
All nodes work on the same task
3D Render farm
“embarrassingly parallel” i.e. 1 frame per CPU

5. Microcode

6. Technology Migration over time
“Software running on general-purpose computers will outlast custom hardware every time” – but it might take years to catch up

7. Up and Down the Technology Stack
How can we predict which innovations are likely to succeed?
Business processes (VFX == “pipeline”, Post == “workflow”) move up the Stack
Appliances move down the Stack
HW & SW solutions (once paid off) become Appliances
Software evolves much more quickly than either of these
Confusion between continuous and discontinuous innovation is the cause of many technology product failures

8. VFX Technology Stack

9. VFX Render Farm

10. Location render farming

11. Merging infrastructure and workflow == pipelining
Compression
JPEG-2000
H.264
Transcoding
Software VBR
Multi-pass
Audio QC
Automated, file-based
Faster than real-time
Deliverables
Multiple simultaneous file-based renders

12. Migrate bottlenecked processes to GPU
SIMD – Single Instruction Multiple data
Shared memory instead of message-passing architecture
Memory accesses are expensive
Code and Data packing since computation is cheap
Rapid Mind development tools
AMD, Intel Multi-core x86 CPUs
ATI/AMD FireStream 9250 GPU
NVIDIA Tesla 10P
Cell Broadband Engine, PS3

13. GPU De-Bayering = Data Level parallelism
Packing scalar data into RGBA in texture memory suits GPU architecture very well

14. Object intelligence migrates from blocks to files
A file system contains a certain amount of intelligence in the file itself, in the form of:
the filename + numerical extension
directory placement (pathname)
attributes (e.g. atime, mtime)
Post uses SAN, VFX uses NAS
Performance and cost
Intelligence “built-in” to the object level allows more flexibility in the pipeline without changing the infrastructure
HDD with 5 MB storage in 1956

15. Enterprise Service Bus Goals:
Remap the fixed stack into a flexible pipeline
Plan exit costs of HW as well as entry costs
Adapt to changing business cycles

16. Designing an Enterprise Service Bus for Post
Virtualize dedicated h/w processes on clusters
Profile and provide GPU support for bottlenecks
Re-factor pipeline for different projects
Swap out software to adapt to business cycles
Service Orchestration
The “Project Coordinator” moves up the stack
QC, Delivery, Audit, Monitoring
Scale to distributed bus
Decentralized – smart endpoints
Post facility becomes hub of networked community
Open-source Software such as Apache ServiceMix, ESB product
And Apache CXF: Web Services