Mc2FOR: A tool for automatically translating MATLAB to FORTRAN 95
Bibliographic record
Abstract
MATLAB is a dynamic numerical scripting language widely used by scientists, engineers and students. While MATLAB's high-level syntax and dynamic types make it ideal for prototyping, programmers often prefer using high-performance static languages such as FORTRAN for their final distributable code. Rather than rewriting the code by hand, our solution is to provide a tool that automatically translates the original MATLAB program to an equivalent FORTRAN program. There are several important challenges for automatically translating MATLAB to FORTRAN, such as correctly estimating the static type characteristics of all the variables in a MATLAB program, mapping MATLAB built-in functions, and effectively mapping MATLAB constructs to equivalent FORTRAN constructs. In this paper, we introduce Mc2FOR, a tool which automatically translates MATLAB to FORTRAN. This tool consists of two major parts. The first part is an interprocedural analysis component to estimate the static type characteristics, such as the shape of arrays and the range of scalars, which are used to generate variable declarations and to remove unnecessary array bounds checking in the translated FORTRAN program. The second part is an extensible FORTRAN code generation framework automatically transforming MATLAB constructs to FORTRAN. This work has been implemented within the McLab framework, and we demonstrate the performance of the translated FORTRAN code on a collection of MATLAB benchmarks.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.022 |
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".