LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

arXiv:2403.11522v1 Announce Type: cross
Abstract: While polyhedral compilers have shown success in implementing advanced code transformations, they still have challenges in selecting the most profitable transformations that lead to the best speedups. This has motivated the use of machine learning to build cost models to guide the search for polyhedral optimizations. State-of-the-art polyhedral compilers have demonstrated a viable proof-of-concept of this approach. While such a proof-of-concept has shown promise, it still has significant limitations. State-of-the-art polyhedral compilers that use a …

abstract advanced arxiv build challenges code compilers cost cs.dc cs.lg cs.pl guide machine machine learning search success type