The May 2020 LArSoft Offline Leads status update was handled via email and a google document. Past meeting notes are available at: https://larsoft.org/larsoft-offline-leads-meeting-notes/ Thank you.
LArSoft – Erica Snider
There have been issues with making the most recent version of Geant4, v10.6.p01, work with the legacy LArG4 infrastructure. At least one header that is used in code copied from Geant4 does not exist in this release. The build of the associated legacy LArG4 code was excluded from the recent LArSoft test release. Initial inspection suggests that the changes needed to fix the problem are isolated to the legacy LArG4, and Robert Hatcher is looking into making them, but he has limited time due to other pressing commitments. We cannot migrate to the new Geant4 until this problem is fixed.
- Who is still using the legacy LArG4? For those who are, are there plans to migrate to the new system?
The legacy Redmine git repositories were made read-only as of April 24.
The project continues to make significant efforts toward making important production workflow code for DUNE and SBN thread-safe, and in some cases, making it multi-threaded. This work must be closely coordinated with the experiments so that effort is not spent making unnecessary or low priority changes. Please let us know if you are not fully informed as to the status of on-going work for your experiment in these areas.
Please advise on how the new GitHub / pull-request system for LArSoft is working. Are we meeting the needs of your experiment? Has it raised or lowered barriers to introducing new code or changing existing code? What is the status of plans for your experiment with regard to using GitHub? (We have had extensive conversations and work related to SBN. What about the others?)
DUNE – Andrew John Norman, Heidi Schellman, Tingjun Yang, Michael Kirby
ProtoDUNE has moved to use refactored larg4 in the latest MC production. Also the EM shower daughters were saved in the files. The maximum memory consumption is above 4 GB on average per larg4 job. Studies showed that the biggest memory consumers are: photon library, simb::MCParticles and sim::SimEnergyDeposits. It was suggested to split the photon detector simulation from the rest of larg4 simulation. This reduced the memory consumption of both jobs to below 3 GB.
A 1d convolutional neural network based signal ROI finder was developed and showed good performance on ProtoDUNE simulation and data.
A full reconstruction chain was implemented to reconstruct Iceberg data.
ICARUS – Daniele Gibin, Tracy Usher
LArIAT – Jonathan Asaadi
MicroBooNE – Herbert Greenlee, Tracy Usher
SBND – Roxanne Guenette, Andrzej Szelc
SBN Data/Infrastructure – Joseph Zennamo, Wesley Ketchum
Following up on LArG4: currently both ICARUS and SBND use the legacy larg4, and there may be some issues/guidance needed to update: the key thing right now is the updating the geometry. Given the statement above, we may look to prioritizing this effort.
Currently planning to transition to GitHub May 15. We’ve had some opinions about the extra hurdle on ability to collaborate without having direct access to push branches to the central repos, but some of this may still be growing pains, and some ideas to reduce this on experiment side. If this is an ongoing concern, may want to discuss ways to ease collaborative workflows.
Thanks for hosting the discussion on simulation size/memory at the previous coordination meeting. It will be good to continue to followup/keep track on that.
Please email Katherine Lato or Erica Snider for any corrections or additions to these notes.