Project Meeting 2021.01.12

Technical Call

• Discuss Doyle completing estimation mode for trip mode choice
  • Trip mode choice now done and was quite complex and needed to update the 2 and 3 examples as well
  • Results not exactly the same since we merged in some other changes that changed results
  • I can now review the estimation branch
  • Doyle to now work on performance tuning (see below)
• Discuss Jeff Newman's nmtf estimation notebook
  • Will rebase his cdap and nmtf notebooks off of estimation branch
  • And send me note to review when ready
  • Jeff will also test the other estimation notebooks and update them to be more automatable/testable since they're sort of software and sort of training materials right now
  • Then we'll pause the task and discuss how to make fewer smarter / more generic notebooks rather than a bunch of separate notebooks for each submodel
• Discuss Joe's copyright and licensing history
  • The basic plan is to:
  • Update the license so it says Copyright AMPORF
  • Make sure work for hire is in the contracts
  • Add a Contributor License Agreement like this one and have GitHub automatically manage it with Pull Requests
  • Refactor out the orca code under a bench contract work order
  • The license will stay BSD-3
  • Let's discuss the partner MOU/payment agreement on Thursday
• Discuss PSRC progress and need for location sampling improvements
  • 40k MAZ model with 100k HHs is up and running from start to finish
  • Ran into issues with location sampling since it's considering all 40k alternatives every time for every chooser
  • Would be good to do something like DaySim's two stage sampling approach - basically do it at the taz level and then pick an maz within each taz based on the maz's share of the size term. It also pre-calculates the taz to taz probability matrix and then just draws random numbers and picks a zone.
  • We could also filter on size term > 0 before sol ving expressions, which would help with sparse alternative sets like school/university
  • These issues are similar to the performance improvements tasks below so let's add this to that discussion for consideration
  • Using importance sampling like DaySim is a good approach and we'll need to implement something like it for the SANDAG cross border model
  • Could do it at the start of each process since its fast and then we don't have to persist / share across processes
• Discuss Doyle's list of potential performance improvements
  • expression file optimization
    • good to make these templates as smart as possible since new users rely on them
    • could speed up the model maybe 50%?
  • finish adaptive chunking
    • automatically determine chunksize based on available memory
    • explore adaptive chunking based on actual memory usage rather than ‘registered’ data objects
  • deduplicate alternatives as discussed for ARC
  • cache logsums, which needs categories/market segments defined
    • would help a lot; would require a lot of plumbing updates
  • data format and size optimization.
    • (e.g. rightsize numbers and convert strings to factors)
  • pipeline optimization
    • alternative pipeline file format (e.g. feather)
    • improve control over checkpointing (pipeline footprint and read/write time)
    • Alex likes this one, see his email
  • two stage location sampli ng for PSRC like DaySim
  • run arc and optimize where appropriate
  • run psrc and optimize where appropriate
  • We will run the ARC and PSRC versions of the model and review submodel and component runtimes and memory usage to help inform our discussion of what to work on
  • Review logs and snakeviz profiler
  • Stefan and Guy/Clint to share setups
  • It would be good to understand level-of-effort since maybe we can do a few smaller easier ones and then a big one or two?