Fast and flexible tracepoints in x86
Bibliographic record
Abstract
Summary Tracing is often the most effective technique for analyzing the performance of complex multithreaded applications. This paper presents an improvement on existing techniques for dynamic tracepoint insertion. To add a tracepoint, the technique inserts a jump at the tracing point, possibly replacing several shorter instructions. This jump embeds trap instructions inside its offset at the address of every replaced instruction. This makes the jump thread safe if any thread is about to execute a replaced instruction. It also makes it jump safe if a jump landing pad is at one of the replaced instructions. In both cases, a trap will be raised, and the thread can be redirected to the out‐of‐line equivalent instruction. The use of a jump instead of a trap to execute the tracepoint improves the performance of the execution. It also adds the flexibility to place the tracepoint at almost any instruction, since multiple instructions can be replaced atomically and safely. The downside of this technique is the increased memory usage, since it requires unaligned allocations with high external fragmentation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".