Hybrid Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) parallel programs are pivotal for scalability and efficiency in high-performance computing (HPC), especially as systems approach exa-scale operations. These programs leverage distributed and shared memory systems, making them indispensable for scientific simulations, data analysis, and numerical computations. However, synchronization defects such as deadlocks and race conditions pose significant challenges to reliability and performance, often eluding traditional static and dynamic analysis tools due to the complexity of hybrid systems. This study introduces a deep learning-based models for automated defect prediction in hybrid MPI and OpenMP programs. Using a balanced dataset of 1,500 C++ files, three neural architectures—Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model—were evaluated. To preprocess the code, Abstract Syntax Tree (AST)-based token extraction was employed, capturing structural and semantic relationships critical for detecting defects. Token extraction methods included detailed C++ syntax analysis via the Clang library and a custom regular expression approach. The results reveal that Clang-token-based representation provided the most effective input for defect prediction, enabling CNN models to achieve an accuracy of 97%. By integrating AST-based structural insights with the predictive power of deep learning, this research offers a scalable solution for enhancing the reliability of hybrid parallel programs. The findings establish a foundation for future advancements in automated defect detection, addressing the pressing needs of high-performance computing systems.
