A novel low-overhead flexible instrumentation framework for virtual platforms
Bibliographic record
Abstract
Instrumentation methods for code profiling, tracing and semihosting on virtual platforms (VP) and instruction-set simulators (ISS) rely on function call and system call interception. To reduce instrumentation overhead that can affect program behavior and timing, we propose a novel low-overhead flexible instrumentation framework called Virtual Platform Instrumentation (VPI). The VPI framework uses a new table-based parameter-passing method that reduces the runtime overhead of instrumentation to only that of the interception. Furthermore, it provides a high-level interface to extend the functionality of any VP or ISS with debugging support, without changes to their source code. Our framework unifies the implementation of tracing, profiling and semihosting use cases, while at the same time reducing detrimental runtime overhead on the target as much as 90% compared to widely deployed traditional methods, without significant simulation time penalty.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".