Scanning HTML at Tens of Gigabytes Per Second on ARM Processors
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
ABSTRACT Background Modern processors feature Single Instruction, Multiple Data (SIMD) instructions capable of processing 16 bytes or more simultaneously, enabling significant performance enhancements in data‐intensive tasks. Two major Web browser engines (WebKit and Blink) have adopted SIMD algorithms for parsing HTML. Objective This study reviews recent advances in utilizing SIMD instructions to accelerate HTML parsing through vectorized classification techniques. Methods We compare these HTML parsing techniques with a faster alternative. Performance is benchmarked against traditional methods on recent ARM processors. Results Our measurements demonstrate a 20‐fold performance improvement in HTML scanning using SIMD‐based approaches compared to conventional parsing methods on modern ARM architectures. Conclusion These findings underscore the transformative potential of SIMD‐based algorithms in optimizing Web browser performance, offering substantial speedups for processing Internet formats and HTML parsing.
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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 | 0.001 |
| 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 | 0.001 | 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