Polyhedral Compiler

Polyhedral compilers optimize program code by analyzing and transforming loop structures, aiming to significantly improve execution speed. Current research focuses on developing more efficient algorithms for selecting optimal transformations, often employing machine learning models to predict performance gains and guide the search process across complex loop nests and diverse architectures. This work is crucial for enhancing the performance of computationally intensive applications, particularly in areas like deep learning and robotics, where even small speedups can have a substantial impact. The development of configurable and adaptable polyhedral schedulers further addresses the need for optimized code generation across heterogeneous hardware platforms.