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

TL;DR

Devito is a Python-based framework that automates the generation of optimized code for stencil computations, significantly improving performance in high-performance computing applications like seismic imaging.

Contribution

This paper introduces the Devito compiler architecture and its optimization techniques for automated, high-performance stencil code generation from symbolic Python equations.

Findings

Demonstrated improved performance in seismic imaging operators

Integrated advanced optimizations like tiling and parallelization

Showed effectiveness of compiler optimizations in real-world applications

Abstract

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.