Hi all,
Continuing on from an earlier post in the pursuit of model optimisation and performance gains (see here), I was hoping to get some clarification on another set of queries, relating to subsets.
I've seen some collateral insinuating that numerous subsets and/or subsets with most of the list flagged as part of the subset can be detrimental to performance; namely the following resources,
- Planual
- Model Load, Save, and Rollback
What wasn't quite clear were the reasons why they lead to a decrease in performance in certain circumstances?
For context,
- we have a larger model (~77GB)
- most of our numbered lists (which typically have ~20k objects) have a 'core' subset which contains 98% of list itself with,
- several other subsets that are sporadically flagged depending on what type of classification they have.
In the below example, our #Lease list uses 'Subset 1' as the 'core' list with other subsets being used as additional classifications for specific use cases (and to also reduce sparsity).
List example
The justification for using a 'core' subset as the main list is that we may need to de-subset certain leases/reduce sparsity at a point in time, and having the #Lease list being used in the 'Applies to..' wouldn't facilitate this.
Hopefully my explanation makes sense, but in short, I'm looking to understand whether the above scenario and implementation can have a negative impact on model performance.
Cheers,
L