An Executable Semantics for Faster Development of Optimizing Python Compilers
Bibliographic record
Abstract
Python is a popular programming language whose performance is known to be uncompetitive in comparison to static languages such as C. Although significant efforts have already accelerated implementations of the language, more efficient ones are still required. The development of such optimized implementations is nevertheless hampered by its complex semantics and the lack of an official formal semantics. We address this issue by presenting an approach to define an executable semantics targeting the development of optimizing compilers. This executable semantics is written in a format that highlights type checks, primitive values boxing and unboxing, and function calls, which are all known sources of overhead. We also present semPy, a partial evaluator of our executable semantics that can be used to remove redundant operations when evaluating arithmetic operators. Finally, we present Zipi, a Python optimizing compiler prototype developed with the aid of semPy. On some tasks, Zipi displays performance competitive with that of state-of-the-art Python implementations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".