Bibliographic record
Abstract
MATLAB is a popular dynamic array-based language commonly used by students, scientists and engineers who appreciate the interactive development style, the rich set of array operators, the extensive builtin library, and the fact that they do not have to declare static types. Even though these users like to program in MATLAB, their computations are often very compute-intensive and are better suited for emerging high performance computing systems. This thesis reports on MIX10, a source-to-source compiler that automatically translates MATLAB programs to X10, a language designed for "Performance and Productivity at Scale"; thus, helping scientific programmers make better use of high performance computing systems.There is a large semantic gap between the array-based dynamically-typed nature of MATLAB and the object-oriented, statically-typed, and high-level array abstractions of X10. This thesis addresses the major challenges that must be overcome to produce sequential X10 code that is competitive with state-of-the-art static compilers for MATLAB which target more conventional imperative languages such as C and Fortran. Given that efficient basis, the thesis then provides a translation for the MATLAB parfor construct that leverages the powerful concurrency constructs in X10.The MIX10 compiler has been implemented using the McLab compiler tools, is open source, and is available both for compiler researchers and end-user MATLAB programmers. We have used the implementation to perform many empirical measurements on a set of 17 MATLAB benchmarks. We show that our best MIX10-generated code is significantly faster than the de facto Mathworks' MATLAB system, and that our results are competitive with state-of-the-art static compilers that target C and Fortran. We also show the importance of finding the correct approach to representing the arrays in X10, and the necessity of an IntegerOkay analysis that determines which double variables can be safely represented as integers. Finally, we show that our X10-based handling of the MATLAB parfor greatly outperforms the de facto MATLAB implementation.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".