The potential of trace-level parallelism in Java programs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The exploitation of parallelism among traces, i.e. hot paths of execution in programs, is a novel approach to the automatic parallelization of Java programs and it has many advantages. However, to date, the extent to which parallelism exists among traces in programs has not been made clear. The goal of this study is to measure the amount of trace-level parallelism in several Java programs. We extend the Jupiter Java Virtual Machine with a simulator that models an abstract parallel system. We use this simulator to measure trace-level parallelism. We further use it to examine the effects of the number of processors, trace window size, and communication type and cost on performance. Our results indicate that enough trace-level parallelism exists for a modest number of processors. Thus, we conclude that trace-based parallelization is a potentially viable approach to improve the performance of Java programs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it