Predicated Partial Redundancy Elimination using a Cost Analysis
Why this work is in the frame
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Bibliographic record
Abstract
Partial redundancy elimination (PRE) is a key technology for modern compilers. However traditional approaches are conservative and fail to exploit many opportunities for optimization. New PRE approaches which greatly increase the number of eliminated redundancies have been developed. However, they either cause the code size to explode or they cannot handle statements with side-effects. In this paper we describe a predicated partial redundancy elimination (PPRE) approach which can potentially remove all partial redundancies. To avoid performance overheads caused by predication, PPRE is applied selectively based on a cost model. The cost analysis presented in the paper utilizes probabilistic data-flow information to decide whether PPRE is profitable for each instance of a partially redundant computation. Refinements of the basic PPRE transformation are described in detail. In contrast to some other approaches our transformation is strictly semantics preserving.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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