%0 Conference Paper %1 Burchell:2023:Profilers %A Burchell, Humphrey %A Larose, Octave %A Kaleba, Sophie %A Marr, Stefan %B Proceedings of the 20th ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes %D 2023 %I ACM %K CPUSampling Comparison MeMyPublication Precision Profiling myown %P 1--14 %R 10.1145/3617651.3622985 %T Don’t Trust Your Profiler: An Empirical Study on the Precision and Accuracy of Java Profilers %X To identify optimisation opportunities, Java developers often use sampling profilers that attribute a percentage of run time to the methods of a program. Even so these profilers use sampling, are probabilistic in nature, and may suffer for instance from safepoint bias, they are normally considered to be relatively reliable. However, unreliable or inaccurate profiles may misdirect developers in their quest to resolve performance issues by not correctly identifying the program parts that would benefit most from optimisations. With the wider adoption of profilers such as async-profiler and Honest Profiler, which are designed to avoid the safepoint bias, we wanted to investigate how precise and accurate Java sampling profilers are today. We investigate the precision, reliability, accuracy, and overhead of async-profiler, Honest Profiler, Java Flight Recorder, JProfiler, perf, and YourKit, which are all actively maintained. We assess them on the fully deterministic Are We Fast Yet benchmarks to have a stable foundation for the probabilistic profilers. We find that profilers are relatively reliable over 30 runs and normally report the same hottest method. Unfortunately, this is not true for all benchmarks, which suggests their reliability may be application-specific. Different profilers also report different methods as hottest and cannot reliably agree on the set of top 5 hottest methods. On the positive side, the average run time overhead is in the range of 1% to 5.4% for the different profilers. Future work should investigate how results can become more reliable, perhaps by reducing the observer effect of profilers by using optimisation decisions of unprofiled runs or by developing a principled approach of combining multiple profiles that explore different dynamic optimisations.

@inproceedings{Burchell:2023:Profilers, abstract = {To identify optimisation opportunities, Java developers often use sampling profilers that attribute a percentage of run time to the methods of a program. Even so these profilers use sampling, are probabilistic in nature, and may suffer for instance from safepoint bias, they are normally considered to be relatively reliable. However, unreliable or inaccurate profiles may misdirect developers in their quest to resolve performance issues by not correctly identifying the program parts that would benefit most from optimisations. With the wider adoption of profilers such as async-profiler and Honest Profiler, which are designed to avoid the safepoint bias, we wanted to investigate how precise and accurate Java sampling profilers are today. We investigate the precision, reliability, accuracy, and overhead of async-profiler, Honest Profiler, Java Flight Recorder, JProfiler, perf, and YourKit, which are all actively maintained. We assess them on the fully deterministic Are We Fast Yet benchmarks to have a stable foundation for the probabilistic profilers. We find that profilers are relatively reliable over 30 runs and normally report the same hottest method. Unfortunately, this is not true for all benchmarks, which suggests their reliability may be application-specific. Different profilers also report different methods as hottest and cannot reliably agree on the set of top 5 hottest methods. On the positive side, the average run time overhead is in the range of 1% to 5.4% for the different profilers. Future work should investigate how results can become more reliable, perhaps by reducing the observer effect of profilers by using optimisation decisions of unprofiled runs or by developing a principled approach of combining multiple profiles that explore different dynamic optimisations.}, acceptancerate = {0.54}, added-at = {2023-09-19T00:50:21.000+0200}, appendix = {https://github.com/HumphreyHCB/AWFY-Profilers}, author = {Burchell, Humphrey and Larose, Octave and Kaleba, Sophie and Marr, Stefan}, biburl = {https://www.bibsonomy.org/bibtex/2d2dead4cc003f4c28513d640978b30e7/gron}, blog = {https://stefan-marr.de/2023/09/dont-blindly-trust-your-profiler/}, booktitle = {Proceedings of the 20th ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes}, doi = {10.1145/3617651.3622985}, interhash = {52baf4e3e7d39e58e396ee27f3c3fc58}, intrahash = {d2dead4cc003f4c28513d640978b30e7}, keywords = {CPUSampling Comparison MeMyPublication Precision Profiling myown}, month = {October}, pages = {1--14}, pdf = {https://stefan-marr.de/downloads/mplr23-burchell-et-al-dont-trust-your-profiler.pdf}, publisher = {ACM}, series = {MPLR'23}, timestamp = {2023-09-25T17:32:49.000+0200}, title = {{Don’t Trust Your Profiler: An Empirical Study on the Precision and Accuracy of Java Profilers}}, year = 2023 }