Experiences with Multi-threading and Dynamic Class Loading in a Java Just-In-Time Compiler
Bibliographic record
Abstract
In this paper, we describe the techniques that have been implemented in the IBM TestaRossa (TR) just-in-time (JIT) compiler to safely perform aggressive code patching and collect accurate profiles in the context of a Java application employing multiple threads and dynamic class loading and unloading. Previous work in these areas either did not account for the synchronization cost of safety or dynamic class loading/unloading effects in a heavily multithreaded program or did not consider how different patching techniques may be required for different platforms where instruction cache coherence guarantees vary. We evaluate the space and time overhead to make our profiling framework correct, showing that privatizing the profiling variables to achieve correctness impacts execution time only minimally but it can grow the stack frames for profiled methods by less than 15% on average for the SPECjvm98 and SPECjbb2000 benchmarks. Since methods are profiled for only a brief time and the stack frames themselves are not large, we do not consider this growth to be prohibitive. The techniques reported in this paper are implemented in the 1.5.0 release of the IBM Developer Kit for Java targeting 12 different processor-operating system platforms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".