Architecture and performance of Devito, a system for automated stencil computation

TitleArchitecture and performance of Devito, a system for automated stencil computation
Publication TypeJournal Article
Year of Publication2020
AuthorsFabio Luporini, Mathias Louboutin, Michael Lange, Navjot Kukreja, Philipp A. Witte, Jan Hückelheim, Charles Yount, Paul H. J. Kelly, Felix J. Herrmann, Gerard J. Gorman
JournalACM Trans. Math. Softw.
Keywordscompiler, finite difference method, performance optimization, stencil, structured grid, symbolic processing

Stencil computations are a key part of many high-performance computing applications, such as image processing, convolutional neural networks, and finite-difference solvers for partial differential equations. Devito is a framework capable of generating highly-optimized code given symbolic equations expressed in Python, specialized in, but not limited to, affine (stencil) codes. The lowering process – from mathematical equations down to C++ code – is performed by the Devito compiler through a series of intermediate representations. Several performance optimizations are introduced, including advanced common sub-expressions elimination, tiling and parallelization. Some of these are obtained through well-established stencil optimizers, integrated in the back-end of the Devito compiler. The architecture of the Devito compiler, as well as the performance optimizations that are applied when generating code, are presented. The effectiveness of such performance optimizations is demonstrated using operators drawn from seismic imaging applications.


(ACM Trans. Math. Softw.)

Citation Keyluporini2018aap