Recommandé
Recommandé
Contenu connexe
Similaire à The Art Of Performance Tuning
Similaire à The Art Of Performance Tuning (20)
Dernier
Dernier (20)
The Art Of Performance Tuning
- 1. the art of performance tuning Jonathan Ross
- 2. the art of performance tuning Jonathan Ross science engineering
- 3. trading company big in options and futures based in Amsterdam imc Trading
- 4. me Playing with JVMs since 1997 Paid to do so by IMC Chicago Theoretical physics background CJUG board member JCP associate member @JoroRoss
- 6. have you tried profiling it? it’s too slow
- 7. this talk Part 1: theory • challenges • methodology • implementation Part 2: practice • how to use a profiler • hands-on jmh benchmarks
- 8. this talk is not about gc tuning Part 1: theory • challenges • methodology • implementation Part 2: practice • how to use a profiler • hands-on jmh benchmarks
- 10. challenges objects modern cpus lack of determinism virtualization tooling ignorance confirmation bias premature optimisation over-engineering legacy
- 11. challenges ignorance confirmation bias premature optimisation over-engineering legacy objects modern cpus lack of determinism virtualization tooling
- 12. Methodology find a (proxy) metric for implement automate “success” a way of measuring it a way of monitoring it
- 13. implementation make it easy to find root causes using fix and add regression tests using learn to use and understand reproduce issues production metrics macro benchmarks micro benchmarks profilers
- 14. implementation make it easy to find root causes using fix and add regression tests using learn to use and understand if the going gets tough reproduce issues production metrics macro benchmarks micro benchmarks profilers -XX+UnlockDiagnosticVMOptions -XX:+LogCompilation -XX:+PrintAssembly
- 15. handy-dandy flowchart production metrics reproduce in benchmark profile implement fix metrics better? ditch fix nope yup good enough? nope merge and verify in production yup ? find bottleneck? yes sir
- 16. handy-dandy flowchart production metrics reproduce in benchmark profile implement fix metrics better? ditch fix nope yup good enough? nope merge and verify in production yup ? find bottleneck? yes sir• try harder • rearchitect • seek new job erm…
- 17. handy-dandy flowchart production metrics reproduce in benchmark profile implement fix metrics better? ditch fix nope yup good enough? nope merge and verify in production yup ? find bottleneck? yes sir 1. Only work on improving performance of actual bottle-necks 2. Don’t keep a fix unless it improves performance (or makes the code better) the main take-away:
- 18. Part 2: practice
- 19. A STORY OF ATAN a (redacted) real world performance tuning adventure involving my love for math and some short-sightedness it’s too slow let me try to profile it
- 20. To profile a production system, start your server with the JVM argument: -agentpath:<profiler directory>/bin/linux-x86-64/libyjpagent.so stack telemetry (Yourkit)
- 21. weeding out irrelevant data (tip: turn off filters by default)
- 22. export threads were not supposed to be this busy…
- 23. ordering callees by “own time” shows us the bottleneck
- 24. where is the bottleneck called from? select method back-traces
- 25. (wouldn’t you like to know!) 2 bottlenecks for the price of one
- 26. my favorite feature of Yourkit: focusing on sub-set of data
- 27. eureka! next favorite feature: merged callees
- 28. To the micro benchmarks! (demo)
- 29. normalizedAtan = 2 ⇡ tan 1 ⇡ 2 x
- 30. tan 1 (x) ⇡ ⇡(0.596227x + x2 ) 2(1 + 1.192524x + x2)
- 31. Back To the micro benchmarks! (demo)
- 32. Benchmark Mode Cnt Score Error Units FoobaratorBenchmark.benchmark avgt 100 2164.391 ± 49.520 us/op original Benchmark Mode Cnt Score Error Units FoobaratorBenchmark.benchmark avgt 100 2043.580 ± 80.292 us/op approximate, outside loop (6%) Benchmark Mode Cnt Score Error Units FoobaratorBenchmark.benchmark avgt 100 1991.929 ± 50.966 us/op approximate (8% speedup) Benchmark Mode Cnt Score Error Units FoobaratorBenchmark.benchmark avgt 100 2045.587 ± 55.951 us/op precise, outside loop (6%)
- 34. Benchmark Mode Cnt Score Error Units FoobaratorBenchmark.benchmark avgt 100 2393.484 ± 111.872 us/op original Benchmark Mode Cnt Score Error Units FoobaratorBenchmark.benchmark avgt 100 1163.414 ± 17.417 us/op map of maps (52%) Benchmark Mode Cnt Score Error Units FoobaratorBenchmark.benchmark avgt 100 660.802 ± 5.795 us/op mutable double (72%) Benchmark Mode Cnt Score Error Units FoobaratorBenchmark.benchmark avgt 100 894.116 ± 9.971 us/op map of maps (2) (63%) Benchmark Mode Cnt Score Error Units FoobaratorBenchmark.benchmark avgt 100 547.549 ± 4.122 us/op atan outside loop again (77%)
- 35. challenges revisited ignorance confirmation bias premature optimisation overengineering legacy objects modern cpus lack of determinism virtualization tooling
- 36. ? @JoroRoss www.imc.com www.cjug.org http://openjdk.java.net/projects/code-tools/jmh/ http://hirt.se - Marcus Hirt’s blog on JMC/JFR https://visualvm.github.io The demo code is available at https://github.com/JoroRoss/art-of-performance