Since total sample and body sample are used to compute hotness threshold in compiler, we found in some services changing the total samples computation will cause noticeable regression. Hence, here we will revert the changes and just keep all total samples number identical to the old tool.
Three changes in this diff:
1. Revert previous diff(https://reviews.llvm.org/D112672: [llvm-profgen] Update total samples by accumulating all its body samples) and put it under a switch.
2. Keep the negative line number. Although compiler doesn't consume the count but it will be used to compute hot threshold.
3. Change to accumulate total samples per byte instead of per instruction.
Reviewed By: hoy, wenlei
Differential Revision: https://reviews.llvm.org/D115013
AutoFDO performance is sensitive to profile density, i.e., the amount of samples in the profile relative to the program size, because profiles with insufficient samples could be inaccurate due to statistical noise and thus hurt AutoFDO performance. A previous investigation showed that AutoFDO performed better on MySQL with increased amount of samples. Therefore, we implement a profile-density computation feature to give hints about profile density to users and the compiler.
We define the density of a profile Prof as follows:
- For each function A in the profile, density(A) = total_samples(A) / sizeof(A).
- density(Prof) = min(density(A)) for all functions A that are warm (defined below).
A function is considered warm if its total-samples is within top N percent of the profile. For implementation, we reuse the `ProfileSummaryBuilder::getHotCountThreshold(..)` as threshold which can be set by percent(`--profile-summary-cutoff-hot`) or by value(`--profile-summary-hot-count`).
We also introduce `--hot-function-density-threshold` to set hot function density threshold and will give suggestion if profile density is below it which implies we should increase samples.
This also applies for CS profile with all profiles merged into base.
Reviewed By: hoy, wenlei
Differential Revision: https://reviews.llvm.org/D113781
wlei