QTrace: a framework for customizable full system instrumentation
Bibliographic record
Abstract
This work presents QTrace, an open-source instrumentation extension API for QEMU (1) that can instrument unmodified applications and OS binaries for uni- and multi-processor systems. QTrace facilitates the development of custom, full-system instrumentation tools for the X86 guest architecture enabling statistics collection and program execution studies including system-level code. This paper: illustrates QTrace's API through instrumentation examples, discusses how QEMU was modified to implement QTrace, explains the validation testing procedures, shows QTrace's usefulness in comparison to a user-level binary instrumentation tool in workloads that spend significant time in the kernel, and illustrates that QTrace does not impose a significant performance penalty. Experiments show that for an instruction count plug-in, QTrace is 12.2X slower than PIN [2], a user-level only instrumentation tool, and 4.1X faster than BOCHS [3], a full-system emulator. QTrace without instrumentation performs similarly to the un-modified QEMU.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".