Attribute preparsing for variable-encoded files in ReadRandomAccess mode: Support modifiable attributes#1735
Merged
franzpoeschel merged 14 commits intoopenPMD:devfrom Mar 25, 2025
Conversation
c5c9726 to
5eaf97b
Compare
d98599b to
361363a
Compare
This file contains hidden or 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
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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.
This tries removing the major of two WIP restrictions for random-access reading of variable-encoded Series by preparsing.
TODO
Discussion: The ADIOS2 backend now contains two implementations for preparsing:
/data/snapshotis preparsed, metadata is otherwise considered to be fully static. Advantage: The file needs to be opened inadios2::Mode::Readfor preparsing only on a single rank, only the collected values for/data/snapshotare distributed in a single MPI operation at the end./data/timeand has (restricted) support for groups that are present only in some steps (as long as they only consist of attributes and not arrays, i.e. as long as they are constant components). Disadvantage: Requires collectively reading the entire dataset and keeping the preparsed attributes for all steps in memory.Hence: