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Domain-Specific Multi-Level Rewriting for HPC: A Case Study with MLIR

Tobias Gysi
Christoph Mueller
Oleksandr Zinenko
Stephan Andreas Herhut
Eddie Davis
Tobias Wicky
Oliver Fuhrer
Torsten Hoefler
Tobias Grosser
ACM Transactions on Architecture and Code Optimization, vol. 4 (2021), 51:1-51:23

Abstract

Peephole optimizations have proven to be effective on traditional compiler tasks such as instruction selection. MLIR raises the level of abstraction from machine instructions to high-level operations such as matrix-multiplication or an entire stencil application. In this project, we want to show the effectiveness of peephole style optimizations on domain-specific abstractions. We, therefore, optimize stencil programs from the weather and climate domain to demonstrate the effectiveness of the approach in the HPC space. As a vehicle to evaluate our idea, we introduce a high-level stencil dialect that models the data-flow of stencil programs. We deduce a set of high-level peephole optimizations to optimize stencil programs and implement a lowering to the GPU dialect of MLIR. The GPU dialect is a novel abstraction layer the allows compiler engineers to generate code that is performance portable across different GPU architectures.

Research Areas