Dynamic profiling and trace cache generation
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
Dynamic program optimization is increasingly important for achieving good runtime performance. A key issue is how to select which code to optimize. One approach is to dynamically detect traces, long sequences of instructions spanning multiple methods, which are likely to execute to completion. Traces are easy to optimize and have been shown to be a good unit for optimization. The paper reports on a new approach for dynamically detecting, creating and storing traces in a Java virtual machine. We first describe four important criteria for a successful trace strategy: good instruction stream coverage, low dispatch rate, cache stability, and optimizability of traces. We then present our approach based on branch correlation graphs. A branch correlation graph stores information about the correlation between pairs of branches, as well as additional state information. We present the complete design for an efficient implementation of the system, including a detailed discussion of the trace cache and profiling mechanisms. We have implemented an experimental framework to measure the traces generated by our approach in a direct-threaded Java VM (SableVM) and we present experimental results to show that the traces we generate meet the design criteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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