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Twitters Quest for Holy Grail
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Classroom Contents
Performance Tuning Twitter Services with Graal and Machine Learning
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- 1 Intro
- 2 Performance tuning
- 3 Performance tuning internal
- 4 Bayesian optimization
- 5 How Bayesian optimization works
- 6 What does it do
- 7 How it finds
- 8 Optimality
- 9 Autotune
- 10 What is Autotune
- 11 What is Graal
- 12 Open JDK
- 13 Inlining parameters
- 14 Twitters Quest for Holy Grail
- 15 PS scavenge cycles
- 16 User CPU time
- 17 Ranges
- 18 Test setup
- 19 Objective
- 20 Constraints
- 21 Experiments
- 22 Results
- 23 Results Table
- 24 Results Chart
- 25 Maximum Landing Site
- 26 Low Level Graph
- 27 Verification Experiment
- 28 Data Visualization
- 29 CPU Time
- 30 Latency
- 31 Performance improvements
- 32 Experiment 2 Social Graph
- 33 Experiment 3 Social Graph
- 34 Experiment 4 Orange Control
- 35 Experiment 4 Results
- 36 Verification Run
- 37 Social Graph
- 38 Autotune Social Graph
- 39 Autotune parameters
- 40 Inlining
- 41 Evaluation
- 42 Outcome
- 43 Max in line size
- 44 Inline small code
- 45 Autotuned
