In recent years, MATLAB's just-in-time (JIT) interpreter has improved the execution time of for-loops to the extent that loops can outperform equivalent array operations in some scenarios. This has caused systematic translation of loops to array operations, a prevalent approach for performance improvement in MATLAB, to sometimes yield a performance loss. Therefore, we propose a selective strategy to loop translation with selection criteria guided by loop profiling data. As a result, only loops with a high-performance speedup potential are selected for translation to array operations. The results of our experiments confirm the efficiency of our approach and illustrate the cases where systematic translation leads to a performance degradation.
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