Architectural support for SWAR text processing with parallel bit streams
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
Parallel bit stream algorithms exploit the SWAR (SIMD within a register) capabilities of commodity processors in high-performance text processing applications such as UTF-8 to UTF-16 transcoding, XML parsing, string search and regular expression matching. Direct architectural support for these algorithms in future SWAR instruction sets could further increase performance as well as simplifying the programming task. A set of simple SWAR instruction set extensions are proposed for this purpose based on the principle of systematic support for inductive doubling as an algorithmic technique. These extensions are shown to significantly reduce instruction count in core parallel bit stream algorithms, often providing a 3X or better improvement. The extensions are also shown to be useful for SWAR programming in other application areas, including providing a systematic treatment for horizontal operations. An implementation model for these extensions involves relatively simple circuitry added to the operand fetch components in a pipelined processor.
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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 | 0.001 |
| 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