06:34PM EDT - Facebook is presenting details on Zion, its next generation in-memory unified training platform.

06:35PM EDT - Zion designed for Facebook sparse workloads

06:35PM EDT - Many teams at Facebook

06:35PM EDT - expertise from networking to infrastructure to compute

06:36PM EDT - 12 month ML Training growth is 3x compute

06:36PM EDT - ML pipeline data growth in 2018 was 30%, now is 50%

06:36PM EDT - That's 30% of the DC, now 50%

06:37PM EDT - The size of the datacenter has doubled in the same timeframe, so overall 3x

06:37PM EDT - Number of engineers on ML experimenting with models has 2x in the last 12-months

06:37PM EDT - These place significant strains on the systems

06:37PM EDT - ML engineers expect to do it in a very agile form

06:38PM EDT - They need flexibility and efficiency

06:38PM EDT - Motivated SW/HW co-design

06:38PM EDT - Strains on different parts of the datacenter

06:38PM EDT - Major AI services at Facebook

06:39PM EDT - Recommendations, vision, language

06:39PM EDT - Three main high-level services

06:40PM EDT - The recommendation models are among the most important models, for news feed and such

06:40PM EDT - DL Models

06:41PM EDT - Many features are sparse in the workloads

06:41PM EDT - e.g. friend and user pages

06:41PM EDT - Need numerical model for training

06:41PM EDT - translated into embedded table lookups

06:42PM EDT - Develop interaction between features to help compute potential user interactivity

06:42PM EDT - Not every user will interact with every feature

06:43PM EDT - The models are quite extensive in resource requirements

06:43PM EDT - Stretch every element of the infrastructure

06:43PM EDT - Embedded tables are order of 10+ GB per user

06:43PM EDT - Low algorithmic intensity

06:44PM EDT - Aim for model parallelism

06:44PM EDT - Have to get good load balancing across multiple devices

06:44PM EDT - MLP (multi-layer perceptron) requires model or data parallelism

06:45PM EDT - Tall and skinny GEMM models

06:45PM EDT - Putting it all together

06:45PM EDT - Training each of the models

06:45PM EDT - All-to-all communications which strains the infrastructure

06:46PM EDT - Unified BF16 format with CPU and Accelerators

06:47PM EDT - 8 socket CPU system with 8 accelerators

06:47PM EDT - 8x100W CPU with 8x200W Accelerator

06:47PM EDT - Not all parts use BF16

06:48PM EDT - CPU organized in hypercube mesh with separate fabric to accelerator fabric

06:48PM EDT - Designed to scale out

06:49PM EDT - (CPU must have BF16 = Cooper Lake?)

06:49PM EDT - Modular system design

06:49PM EDT - dual socket MB module

06:50PM EDT - four dual socket modules make an 8-socket system

06:51PM EDT - Large number of different accelerators available. Facebook led OAM effort to device vendor agnostic common form factor

06:51PM EDT - Some vendors have cube mesh, some are fully connected

06:51PM EDT - Solution was the super set topology

06:52PM EDT - Can enable all other topologies via superset

06:53PM EDT - Software flexibility

06:53PM EDT - Can work on CPU-only, and can increase the model on better hardware

06:53PM EDT - Creates a continuum of performance vs developer efficiency/time

06:54PM EDT - System can be configured as 4x2S, 2x4S, or 1x8S

06:54PM EDT - All works on PCIe

06:55PM EDT - Performance results, CPU only

06:56PM EDT - Comparison to NVIDIA solution

06:58PM EDT - That's a wrap

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