Compiler Training System for Software Performance Portability

The system performs preliminary actions by training the machine learning model in advance with extensive hardware performance data before actual compilation. The model learns optimal transformation strategies beforehand, enabling it to make accurate predictions even with limited target hardware knowledge during compilation.

The patent replaces conventional deterministic compiler transformation methods with a machine learning-based probabilistic system. The ML model substitutes traditional static analysis and heuristics, enabling adaptive transformation selection based on learned patterns from diverse hardware architectures.

The system creates a copy of hardware performance characteristics in the form of training data and learned models. Instead of directly accessing complex hardware details during compilation, the compiler uses copied performance patterns from the training phase, simplifying the compilation process while maintaining optimization quality.

The complex work of learning hardware characteristics and optimal transformations is performed in advance during the training phase. This preliminary action separates the complex learning process from the compilation process, allowing the compiler to use pre-learned knowledge without bearing the full complexity burden during actual use.

The system performs comprehensive hardware profiling and data collection in advance during the training phase. By moving this time-consuming activity to the training stage, the patent enables fast, accurate transformations during actual compilation without repeating the extensive profiling process.

The system uses feedback from performance measurements during training to continuously improve transformation accuracy. The ML model learns from measured performance data and adjusts its predictions, achieving high precision through iterative feedback rather than requiring exhaustive profiling at compilation time.