I threw two posts(1st post and 2nd post) up on Google’s use of Machine Language in the Data Center and said I would write more. Well here is another one.
Does Google’s Data Center Machine Language Model have a debug mode? The current system describes the use of data collected every 5 minutes over about 2 years.
184,435 time samples at 5 minute resolution (approximately 2 years of operational data
One of the methods almost no one does is debug their mechanical systems as if you were debugging software.
Debugging is a methodical process of finding and reducing the number of bugs, or defects, in a computer program or a piece of electronic hardware, thus making it behave as expected. Debugging tends to be harder when various subsystems are tightly coupled, as changes in one may cause bugs to emerge in another.
What would debugging mode look like in DCMLM (my own acronym for Data Center Machine Language Model)? You are seeing performance that looks like the subsystem is not performing as expected. Change the sampling rate to 1 second. Hopefully the controller will function correctly at a higher sample rate. The controller may work fine, but the transport bus may not. With the 1 second fidelity make changes to settings and collect data. Repeat changes. Compare results. Create other stress cases.
What will you see? From the time you make the changes in a setting how long does it take for you to achieve the desired state. At the 5 minute sampling you cannot see the transition and the possibly delays. Was the transition smooth or a step function. Was there an overshoot in value and then corrections?
The controllers have code running in them, sensors go bad, wiring connections are intermittent. How do you find these problems? Being able to go into Debug mode could be useful.
If Google was able to compare detailed operations of two different installations of the same mechanical system, then they could find whether there was a problem that is unique to a site. Or they may simply compare the same system at different points of time.