AI RESEARCH
Automatic Identification of Parallelizable Loops Using Transformer-Based Source Code Representations
arXiv CS.AI
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ArXi:2603.30040v1 Announce Type: cross Automatic parallelization remains a challenging problem in software engineering, particularly in identifying code regions where loops can be safely executed in parallel on modern multi-core architectures. Traditional static analysis techniques, such as dependence analysis and polyhedral models, often struggle with irregular or dynamically structured code. In this work, we propose a Transformer-based approach to classify the parallelization potential of source code, focusing on distinguishing independent (parallelizable) loops from undefined ones.