Automating Stressmark Generation for Testing Processor Voltage Fluctuations
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
Rapid current changes (large di/dt) can lead to significant power supply voltage droops and timing errors in modern microprocessors. To test a processor's resilience to such errors and determine appropriate operating conditions, engineers generally create manual di/dt stressmarks that have large current variations at close to the power distribution network's resonance frequency to induce large voltage droops. This process is time-consuming and might need to be repeated several times to generate appropriate stressmarks for different system conditions (for example, different frequencies or di/dt throttling mechanisms). Furthermore, generating efficient di/dt stressmarks for multicore processors is difficult because of their complexity and synchronization issues. In this article, the authors measure and analyze di/dt issues on state-of-the-art multicore x86 systems. They present an automated di/dt stressmark generation framework called Audit to generate di/dt stressmarks quickly and effectively for multicore systems.
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 | 0.000 |
| 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