Interprocedural induction variable analysis based on interprocedural SSA form IR
Why this work is in the frame
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Bibliographic record
Abstract
The induction variable analysis is a fundamental component of loop optimizations in compilers. Algorithms in literature and implementations in free-source compilers such as GCC and LLVM rely on SSA form IR. However, only the uses of scalar stack variables whose address is not taken are replaced with a single definition in the SSA form IR. In this paper, we describe how Interprocedural SSA (ISSA) form IR can be leveraged to extend the induction variable analysis interprocedurally to: globals, singleton heap variables, record elements, and files. We implemented our induction variable analysis and compared it against the LLVM infrastructure for a set of MediaBench and SPEC2K benchmarks. We observed an average increase of 8.1% and 58.4% in the number of polynomial and monotonic induction variables, respectively. Furthermore, due to ISSA form IR and our induction variable analysis we computed 1.02 times more constant tripcounts and 2.06 times more loop invariant tripcounts.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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