Bibliographic record
Abstract
Most implementations of critical Internet protocols are written in type-unsafe languages such as C or C++ and are regularly vulnerable to serious security and reliability problems. Type-safe languages eliminate many errors but are not used to due to the perceived performance overheads. We combine two techniques to eliminate this performance penalty in a practical fashion: strong static typing and generative meta-programming. Static typing eliminates run-time type information by checking safety at compile-time and minimises dynamic checks. Meta-programming uses a single specification to abstract the low-level code required to transmit and receive packets. Our domain-specific language, MPL, describes Internet packet protocols and compiles into fast, zero-copy code for both parsing and creating these packets. MPL is designed for implementing quirky Internet protocols ranging from the low-level: Ethernet, IPv4, ICMP and TCP; to the complex application-level: SSH, DNS and BGP; and even file-system protocols such as 9P. We report on fully-featured SSH and DNS servers constructed using MPL and our OCaml framework Melange, and measure greater throughput, lower latency, better flexibility and more succinct source code than their C equivalents OpenSSH and BIND. Our quantitative analysis shows that the benefits of MPL-generated code overcomes the additional overheads of automatic garbage collection and dynamic bounds checking. Qualitatively, the flexibility of our approach shows that dramatic optimisations are easily possible.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.290 | 0.168 |
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".