Low-cost register-pressure prediction for scalar replacement using pseudo-schedules
Bibliographic record
Abstract
Scalar replacement is an effective optimization for removing memory accesses. However, exposing all possible array reuse with scalars may cause a significant increase in register pressure, resulting in register spilling and performance degradation. In this paper, we present a low cost method to predict the register pressure of a loop before applying scalar replacement on high-level source code, called Pseudo-schedule Register Prediction (PRP), that takes into account the effects of both software pipelining and register allocation. PRP attempts to eliminate the possibility of degradation from scalar replacement due to register spilling while providing opportunities for a good speedup. PRP uses three approximation algorithms: one for constructing a data dependence graph, one for computing the recurrence constraints of a software pipelined loop, and one for building a pseudo-schedule. Our experiments show that PRP predicts the floating-point register pressure within 2 registers and the integer register pressure within 2.7 registers on average with a time complexity of O(n 2) in practice. PRP achieves similar performance to the best previous approach, having O(n 3) complexity, with less than one-fourth of the compilation time on our test suite. 1.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".