McVM: An optimizing virtual machine for the MATLAB programming language
Bibliographic record
Abstract
In recent years, there has been an increase in the popularity of dynamic languages such as Python, Ruby, PHP, JavaScript and MATLAB. Programmers appreciate the productivity gains and ease of use associated with such languages. However, most of them still run in virtual machines which provide no Just-In-Time (JIT) compilation support, and thus perform relatively poorly when compared to their statically compiled counterparts. While the reference MATLAB implementation does include a built-in compiler, this implementation is not open sourced and little is known abouts its internal workings. TheMcVMproject has focused on the design and implementation of an optimizing virtual machine for a subset of the MATLAB programming language. Virtual machines and JIT compilers can benefit from advantages that static compilers do not have. It is possible for virtual machines to make use of more dynamic information than static compilers have access to, and thus, to implement optimization strategies that are more adapted to dynamic languages. Through theMcVMproject, some possible avenues to significantly improve the performance of dynamic languages have been explored. Namely, a just-in-time type-based program specialization scheme has been implemented in order to take advantage of dynamically available type information. One of the main contributions of this project is to provide an alternative implementation of the MATLAB programming language. There is already an open source MATLAB interpreter (GNU Octave), but our implementation also includes an optimizing JIT compiler and will be open sourced under the BSD license. McVM aims to become a viable implementation for end-users, but could also see use in the compiler research community as a testbed for dynamic language optimizations. In addition to the contribution of the McVM framework itself, we also contribute the design and implementation of a novel just-in-time type-based program specialization system aimed at dynamic languages. The novel specialization system implemented in McVM shows much promise in terms of potential speed improvements, yielding performance gains up to 3 orders of magnitude faster than competing implementations such as GNU Octave. It is also easily adaptable to other dynamic programming languages such as Python, Ruby and JavaScript. The investigation of performance issues we make in this thesis also suggests future research directions for the design of dynamic language compilers of the future.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.014 | 0.007 |
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".