<i>Ablego</i>: a function outlining and partial inlining framework
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
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 function f host are executed much less frequently than f host itself. Partial inlining is a natural solution to the problems caused by including cold code segments that are seldom executed into hot functions that are frequently invoked. When applying partial inlining, a compiler outlines cold statements from a hot function f host . After outlining, f host becomes smaller and thus can be easily inlined. This paper presents Ablego , 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.002 |
| 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