Efficiently implementing the copy semantics of MATLAB's arrays in JavaScript
Bibliographic record
Abstract
Compiling MATLAB---a dynamic, array-based language---to JavaScript is an attractive proposal: the output code can be deployed on a platform used by billions and can leverage the countless hours that have gone into making JavaScript JIT engines fast. But before that can happen, the original MATLAB code must be properly translated, making sure to bridge the semantic gaps of the two languages. An important area where MATLAB and JavaScript differ is in their handling of arrays: for example, in MATLAB, arrays are one-indexed and writing at an index beyond the end of an array extends it; in JavaScript, typed arrays are zero-indexed and writing out of bounds is a no-op. A MATLAB-to-JavaScript compiler must address these mismatches. Another salient and pervasive difference between the two languages is the assignment of arrays to variables: in MATLAB, this operation has value semantics, while in JavaScript is has reference semantics. In this paper, we present MatJuice --- a source-to-source, ahead-of-time compiler back-end for MATLAB --- and how it deals efficiently with this last issue. We present an intra-procedural data-flow analysis to track where each array variable may point to and which variables are possibly aliased. We also present the associated copy insertion transformation that uses the points-to information to insert explicit copies when necessary. The resulting JavaScript program respects the MATLAB value semantics and we show that it performs fewer run-time copies than some alternative approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".