Modeling Input-Dependent Error Propagation in Programs
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
Transient hardware faults are increasing in computer systems due to shrinking feature sizes. Traditional methods to mitigate such faults are through hardware duplication, which incurs huge overhead in performance and energy consumption. Therefore, researchers have explored software solutions such as selective instruction duplication, which require fine-grained analysis of instruction vulnerabilities to Silent Data Corruptions (SDCs). These are typically evaluated via Fault Injection (FI), which is often highly time-consuming. Hence, most studies confine their evaluations to a single input for each program. However, there is often significant variation in the SDC probabilities of both the overall program and individual instructions across inputs, which compromises the correctness of results with a single input. In this work, we study the variation of SDC probabilities across different inputs of a program, and identify the reasons for the variations. Based on the observations, we propose a model, VTRIDENT, which predicts the variations in programs' SDC probabilities without any FIs, for a given set of inputs. We find that VTRIDENT is nearly as accurate as FI in identifying the variations in SDC probabilities across inputs. We demonstrate the use of VTRIDENT to bound overall SDC probability of a program under multiple inputs, while performing FI on only a single input.
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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 | 0.000 |
| 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 it