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
Using GPUs is an effective way to accelerate regular expression (RE) matching, offering orders of magnitude faster processing than pure CPU approaches. Prior GPU-based RE acceleration methods, however, were developed on older GPU models and primarily aimed at expediting network packet inspection problems. In this work we conduct an updated study aiming to improve performance and enhance generality. We first incorporate prefiltering, verifying whether simpler parts of the RE can match before testing more complex RE components. We also observed that naive implementation of current designs on a modern GPU results in low thread occupancy, limiting performance, and improving the selection of GPU parameters is also crucial to optimizing performance. In combination our optimized design allows us to achieve 40x performance improvement over iNFAnt [5] and up to 1900x faster than ASyncAP [11]. Such an updated approach allows for faster, more general RE matching on modern GPUs.
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 | 0.001 | 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