Removing redundancy via exception check motion
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
Partial redundancy elimination aims to reduce the number of times an expression is computed more than once. The traditional Lazy Code Motion (LCM) algorithm formulated by Knoop, Ruthing and Steffen, through its reliance on unordered bit vectors, is severely limited in its ability to remove redundancy when precise exception semantics are required because bit vectors cannot express the order of exception checks. This paper describes our new PRE algorithm Exception Check Motion that uses the LCM algorithm to treat and optimize exception checks in a similar way to any other expression. Unlike earlier techniques that can remove only the compare instruction of a partially redundant exception check, our solution can eliminate both the compare and trap instructions without any run time code patching or expensive recovery operations. Since it is the trap instructions that restrict subsequent code motions, our technique gives downstream optimizations more flexibility to improve the performance of the resulting code once the partially redundant checks are eliminated. Our analysis has been implemented in the IBM® Testarossa (TR) just-in-time (JIT) compiler in the IBM Developer Kit for Java Release 5.0 as part of the J9 Virtual Machine. We measure performance improvements up to 7.6% and averaging 2.5% across 22 SPEC and DaCapo benchmarks on 4-way IBM pSeries (PowerPC) hardware.
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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 | 0.000 |
| 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 it