Strided Difference Bound Matrices
Abstract
A wide range of symbolic analysis and optimization problems can be formalized using polyhedra. Sub-classes of polyhedra, also known as sub-polyhedral domains, are sought for their lower space and time complexity. We introduce the Strided Difference Bound Matrix (SDBM) domain, which represents a sweet spot in the context of optimizing compilers. Its expressiveness and efficient algorithms are particularly well suited to the construction of machine learning compilers. We present decision algorithms, abstract domain operators and computational complexity proofs for SDBM. We also conduct an empirical study with the MLIR compiler framework to validate the domain's practical applicability. We characterize a sub-class of SDBMs that frequently occurs in practice, and demonstrate even faster algorithms on this sub-class.
Cite
@article{arxiv.2405.11244,
title = {Strided Difference Bound Matrices},
author = {Arjun Pitchanathan and Albert Cohen and Oleksandr Zinenko and Tobias Grosser},
journal= {arXiv preprint arXiv:2405.11244},
year = {2024}
}
Comments
Preprint and extended from the CAV 2024 conference version. Fixed issue in arxiv version where URLs were not wrapped