Hardware‐assisted software event tracing
Bibliographic record
Abstract
Summary Event tracing is a reliable and a low‐intrusiveness method to debug and optimize systems and processes. Low overhead is particularly important in embedded systems where resources and energy consumption is critical. The most advanced tracing infrastructures achieve a very low footprint on the traced software, bringing each tracepoint overhead to less than a microsecond. To reduce this still non‐negligible impact, the use of dedicated hardware resources is promising. In this paper, we propose complementary methods for tracing that rely on hardware modules to assist software tracing. We designed solutions to take advantage of CoreSight STM, CoreSight ETM, and Intel BTS, which are present on most newer ARM‐based systems‐on‐chip and Intel x86 processors. Our results show that the time overhead for tracing can be reduced by up to 10 times when assisted by hardware, as compared to software tracing with LTTng, a high‐performance tracer for Linux. We also propose a modification to the Perf tool to speed BTS execution tracing up to 65%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".