Bibliographic record
Abstract
Java Virtual Machines provide a layer of abstraction supporting any services required for the execution of Java programs; from the viewpoint of Java programs, a Java Virtual Machine is a kind of "virtual hardware". However, fundamentally, any job of this virtual hardware is done by the real low level hardware, and behavioural changes in the virtual hardware are eventually reflected by performance variations in the real hardware. Investigating the real hardware performance is thus important for understanding the behaviour of higher levels, including virtual machines themselves and the Java programs they run. Hardware information also has significant potential for optimizing Java Virtual Machines and achieving better runtime performance for Java programs. In this thesis, we introduce a series of adaptive optimizations in a Java Virtual Machine based on hardware information. We investigate the recurrent behaviour apparent in hardware data and detect the recurrent, periodic phases, i.e., the repetitive behaviour, in high level program execution. These phase detection results can be used for a variety of purposes including optimization and program understanding. For example, phase data can be used to select only the representative portions in program execution for runtime profiling. This selective profiling technique achieves a similar accuracy to that of the continuous profiling with a significant workload reduction. Based on further hardware investigation results we roughly divide the lifetime of a program into different phases and dynamically apply appropriate hot method recompilation strategies which generally improve performance and demonstrate a real world optimization using our technique. Hardware information can also bring benefits to the selection of better garbage collection points. We implement a collector with a garbage collection point analytic model based on our hardware data analyzer and provide a deep study of the relative factors in collection point selection. Our approach and set of techniques highlight a problem for optimization development and a design that adaptively compensates. As hardware performance becomes an increasingly important factor it becomes a greater consideration in the construction of runtime environments, including Java Virtual Machines. We are able to show in our work that hardware monitoring can be the basis of both high level understanding and many new optimizations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".