-
Notifications
You must be signed in to change notification settings - Fork 6.1k
8368321: Rethink compilation delay strategy for lukewarm methods #27926
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
base: master
Are you sure you want to change the base?
Conversation
|
👋 Welcome back iveresov! A progress list of the required criteria for merging this PR into |
|
@veresov This change now passes all automated pre-integration checks. ℹ️ This project also has non-automated pre-integration requirements. Please see the file CONTRIBUTING.md for details. After integration, the commit message for the final commit will be: You can use pull request commands such as /summary, /contributor and /issue to adjust it as needed. At the time when this comment was updated there had been 286 new commits pushed to the
As there are no conflicts, your changes will automatically be rebased on top of these commits when integrating. If you prefer to avoid this automatic rebasing, please check the documentation for the /integrate command for further details. ➡️ To integrate this PR with the above commit message to the |
|
/label add hotspot-compiler |
|
@veresov |
|
@veresov |
Webrevs
|
|
Testing looks ok |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Seems fine.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Looks good.
In the current implementation we delay profiling of lukewarm methods (those that were never compiled by C2 during training) by increasing the 2->3 threshold by a factor. That may shift profiling of those too much into the future if a large factor is used, if we use a small factor, however, profiling may happen within the training run window so to speak. The solution I came up with it to delay profiling until we reach the number of invocations of a method equal to the number we had in the training run. After that we use the normal policy.
Here is an example. I trained our JavacBenchApp for 5 iterations (which is artificially low and therefore many methods would be classified as lukewarm). Then I ran it for 200 iterations with AOT replay.
While initially the performance is similar it quickly diverges. With the new approach we move to standard handling of lukewarm methods after 5 iterations and they get compiled with C2. With the old approach we don't.
Progress
Issue
Reviewers
Reviewing
Using
gitCheckout this PR locally:
$ git fetch https://git.openjdk.org/jdk.git pull/27926/head:pull/27926$ git checkout pull/27926Update a local copy of the PR:
$ git checkout pull/27926$ git pull https://git.openjdk.org/jdk.git pull/27926/headUsing Skara CLI tools
Checkout this PR locally:
$ git pr checkout 27926View PR using the GUI difftool:
$ git pr show -t 27926Using diff file
Download this PR as a diff file:
https://git.openjdk.org/jdk/pull/27926.diff
Using Webrev
Link to Webrev Comment