Efficient compilation of tail calls and continuations to JavaScript
Bibliographic record
Abstract
This paper describes an approach for compiling Scheme's tail calls and first-class continuations to JavaScript, a dynamic language without those features. Our approach is based on the use of a simple custom virtual machine intermediate representation that is translated to JavaScript. We compare this approach, which is used by the Gambit-JS compiler, to the Replay-C algorithm, used by Scheme2JS (a derivative of Bigloo), and Cheney on the MTA, used by Spock (a derivative of Chicken). We analyse the performance of the three systems with a set of benchmark programs on recent versions of four popular JavaScript VMs (V8, SpiderMonkey, Nitro and Chakra). On the benchmark programs, all systems perform best when executed with V8 and our approach is consistently faster than the others on all VMs. For some VMs and benchmarks our approach is moderately faster than the others (below a factor of 2), but in some cases there is a very large performance gap (with Nitro there is a slowdown of up to 3 orders of magnitude for Scheme2JS, and up to 2 orders of magnitude for Spock).
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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 | 0.006 |
| 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 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".