Encode plaintext at highest used level instead of max level - #3332
Merged
Conversation
j2kun
reviewed
Aug 12, 2026
Comment on lines
+97
to
+99
| Run this after CSE. Identical encode ops that CSE has merged are cheaper as | ||
| one plaintext at the highest level than as several at their own levels, | ||
| since each encode pays a fixed cost on top of the per-limb work. |
j2kun
reviewed
Aug 12, 2026
Collaborator
There was a problem hiding this comment.
This works fine, though this kind of problem is the poster-child for a backwards dataflow analysis (lattice join is to take the max). I'd say only worry about it if this ends up having performance issues.
j2kun
approved these changes
Aug 12, 2026
j2kun
added
the
pull_ready
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
2 participants
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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.
For the Lola test that uses Lattigo, the encode time goes from 357 ms to 288 ms, and the memory use goes from 125 MB to 84 MB.