DIME: time-aware dynamic binary instrumentation using rate-based resource allocation
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
Program analysis tools are essential for understanding programs, analyzing performance, and optimizing code. Some of these tools use code instrumentation to extract information at runtime. The instrumentation process can alter program behavior such as timing behavior and memory consumption. Time-sensitive programs, however, must meet specific timing constraints and thus require that the instrumentation process, for instance, bounds the timing overhead. Time-aware instrumentation techniques try to honor the timing constraints of such programs. All previous techniques, however, support only static source-code instrumentation methods. Hence, they become impractical beyond microcontroller code for instrumenting large programs along with all their library dependencies. In this work, we propose DIME, a time-aware dynamic binary instrumentation technique that adds an adjustable bound on the timing overhead to the program under analysis. We implement DIME using the dynamic instrumentation framework, Pin. Quantitative evaluation of the three implementation alternatives shows an average reduction of the instrumentation overhead by 12, 7, and 3 folds compared to native Pin. Instrumenting the VLC media player and a laser beam stabilization experiment demonstrate the practicality and scalability of DIME.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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