Repository navigation
GH-109369: Add machinery for deoptimizing tier2 executors, both individually and globally. - #110358
Closed
markshannon wants to merge 6 commits into
Closed
markshannon wants to merge 6 commits into
markshannon wants to merge 6 commits into
Conversation
Copy link
Copy Markdown
Member
Sorry, something went wrong.
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
1 participant
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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 PR just provides the machinery; we still need to add support to the executors and to make the necessary calls when instrumenting.
The implementation uses a bloom filter.
The advantage of a bloom filter is that it requires no coupling between the executors and the objects they depend on, plus it is simpler to implement and uses less memory than a precise mapping.
I've chosen k = 6 and m = 256.
This should give a low enough false positive rate for most cases. We want to keep the false positive rate very low, as spurious de-optimizations could be expensive.