Conservative Garbage Collection in WebAssembly
Bibliographic record
Abstract
WebAssembly is an up and coming binary format for the Web. It has been increasingin usage since the release of its minimum viable product in 2017. This minimum viableproduct has succeeded in showing the potential of a binary format for the web. However,this binary format is only most viable when targeted by low-level languages, such as Cor Rust. To increase the viability, and decrease friction for other languages, we present agarbage collector for WebAssembly. This is a mandatory feature for many programminglanguages to manage memory, and its absence complicates the usage of many languageswith WebAssembly. To deal with the limited type system of WebAssembly, a conservativegarbage collection approach is used. We also assemble a collection of benchmarks to evaluateour approach against. We find promising results, with the conservative collector being on-par to better in runtime than at least one commonly used memory management solutionfor compiling C to WebAssembly. We believe we have successfully shown the viability ofconservative garbage collection for WebAssembly, which would provide a means of portingmore new languages to the Web
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| 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".