P4Muse: Enabling Modular P4 Programming via Compiler-Managed Code Merging Without Syntax Modifications
Bibliographic record
Abstract
Domain-specific programming languages such as P4 enable flexible and high-performance packet processing for programming network data planes. However, many P4 programs remain monolithic, limiting the development of modular and reusable protocols and libraries. Introducing modularity to P4 has proven challenging, as existing approaches—such as trans-compilers and virtualization—often sidestep direct integration with the P4 language and compiler, constraining backward compatibility and extensibility. This paper introduces P4Muse (P4 Modularity and Unification for Seamless Extensibility), an open-source P4C compiler extension that enhances the modularity of P4 without requiring new syntax or annotations. P4Muse is developed by integrating new compiler passes for automatic code merging, fostering modular design and reuse. We demonstrate its benefits through three classes of use cases that support P4 modularity, enabling code reusability, data plane pipeline composition, and vendor-customer compatibility. Six case studies explore these using the V1Model architecture and the BMv2 software switch. Our results show that P4Muse effectively supports modular P4 program development without altering existing P4 syntax, providing a robust solution that significantly improves code reusability, flexibility, and extensibility while maintaining backward compatibility.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".