<i>Ablego</i>: a function outlining and partial inlining framework
Bibliographic record
Abstract
Abstract Frequently invoked large functions are common in non‐numeric applications. These large functions present challenges to modern compilers not only because they require more time and resources at compilation time, but also because they may prevent optimizations such as function inlining. Often large portions of the code in a hot functionfhostare executed much less frequently thanfhostitself. Partial inlining is a natural solution to the problems caused by includingcoldcode segments that are seldom executed intohotfunctions that are frequently invoked. When applying partial inlining, a compileroutlinescold statements from a hot functionfhost. After outlining,fhostbecomes smaller and thus can be easily inlined. This paper presentsAblego, a framework for function outlining and partial inlining that includes several innovations: (1) an abstract‐syntax‐tree‐based analysis and transformation to form cold regions for outlining; (2) a set of flexible heuristics to control the aggressiveness of function outlining; (3) several possible function outlining strategies; (4)explicit variable spilling, a new technique that overcomes negative side‐effects of function outlining. With the proper strategy, partial inlining improves performance by up to 5.75%. A performance study also suggests that partial inlining's effect on enabling more aggressive inlining is limited. The performance improvement from partial inlining actually comes from better code placement and better code generation. Copyright © 2006 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".