Fix parallel handling of data in repeated trajectory runs for 'rotate' and 'average' #1113
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
are using it in a new trajectory processing run
…t when broadcasting dataset size.
ranks for future use. Addresses Amber-MD#1096.
during trajectory processing in a subsequent trajectory processing run for rotate and average commands.
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Version 6.29.7. Previously, if a dataset was created with
rotateoraveragein parallel the initial trajectory processing run would be fine, but if that data was to be used in a subsequent trajectory processing run that would fail. This is because data sets are synced to master for output, but not broadcast back out to non-master ranks. This PR fixes this behavior forrotateandaverage. Also adds a test to ensure this works properly forrotate. Should address #1096.Under the hood, a bunch of MPI broadcast routines have been added to almost all of the DataSet classes. This is to eventually enable syncing datasets across all ranks for running commands like
filterin parallel (some time in the future).