My thesis progress presentation
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Presentation I had to give, half way through my thesis, to discuss my progress.
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My thesis progress presentation
- 1. Machine Learning for Garbage Collection James Thomas MSc Advanced Computer Science “Machine Learning for Garbage Collection” Gavin Brown & Mikel Lujan
- 2. Garbage Collection Traditional manual memory management by the programmer is prone to error Automatic memory management by the runtime environment, takes out the “garbage” Stop the World GC, in Object Orientated environment (Java) GC time in this context is dead time, algorithm extremely important for efficiency
- 3. Generational Garbage Collection Generational hypothesis, “The majority of objects die young” - D. Ungar, 1984 Separate heap objects into different generations, vary collection frequency. Reduce scanning and pause time. Copying time increases GC time, copying reserve reduces heap size. Allocate directly into mature generation?
- 5. Machine Learning Introduction Automatically extract patterns and processes underlying data generation Classification, Regression, Unsupervised learning (Clustering etc.) Algorithm extremely important “Use ML to predict whether an object will live long enough to be tenured into the mature space”
- 6. Project Progress Existing literature review used to generate hypothesises about which heuristics could indicate object lifetime behaviour Modified Jikes RVM to trace object allocation and tenuring data DaCapo benchmarks, varying heap sizes. Mutual Information used to test correlation of example heuristics as lifetime predictor.
- 9. Current Project Results Allocation site, allocation method and method trace are good object lifetime indicators for both scalars and arrays Object type exhibits high MI for scalar, but less so for array, objects Object size exhibits high MI for array objects, but minimal for scalars. Generalised object type name characteristics exists e.g. Iterator or Enum classes are short lived.
- 10. Machine Learning plan Treat as classification problem not regression, predict tenuring decision not exact object lifetime. Mixture of categorical and ordinal data makes learning more complex. Lack of linear separability between classes. Large alphabets for many attributes, e.g. type, reduces generality of advice.
- 11. Decision Tree Machine learning technique using tree structure to represent classifier. Branch nodes consist of attribute tests Branches are attributes values Leaf nodes represent classification Can handle our complex data input characteristics, e.g. categorical data.
- 12. Future Project Plan Begin with offline learning. Train decision tree offline and plug results back into Jikes RVM for execution. Evaluate effect on garbage collection and benchmark execution. Consider online learning within the JVM, training stage now done during application execution not before. Performance impact of this stage is crucial. Extensions? Increased granularity of object lifetime prediction.
- 13. Current Project Conclusions Mutual Information can be used to correlate heuristics from with object lifetimes, not previously used in the literature, and confirm hypothesises. Previously unknown heuristic found. Objects with the same generalised object type have similar lifetime characteristics. Decision tree may be appropriate for learning with available data.
- 14. Questions?