Candidate Best Optimizations Sequences for Code Size Reduction
Bibliographic record
Abstract
Recently, the number of smaller and smarter embedded devices have rapidly increased.This increment puts more pressure on the compiler developer to develop more dedicated application programs for these devices.Modern compilers (like LLVM) offer standard optimization levels (flags) that deal with reducing the code size named Os and FOz flags.The question arise in this paper in is: Is it possible to find sequence that deliver smaller code compare to standard flags?A Sign Table, it is the suggested method that is introduced in this paper.It can suggest an optimization sequence that can reduce the code size for set of unseen program.Initially, two thousand optimization sequences are generated randomly.Each sequence is compiled with 50 programs, where the programs that give smaller code size compared with the Os or Oz flags are extracted.After building the signs table, which contains the sequences that give the average programs sizes smaller than the Os or Oz flags, the process of quantifying similarity between the unseen program and the programs contained within the signs table is performed.The sequences that belong to the most similar programs are selected to compile the unseen program.The proposed methodology is assessed through an empirical investigation, employing three benchmark suites, namely PolyBench, Shootout, and Stanford.The experiments show that the proposed method reduces the unseen program size by about 9% compared standsrd optimization flags.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".