Fortran performance optimisation and auto-parallelisation by leveraging MLIR-based domain specific abstractions in Flang
EPCC at the University of Edinburgh, Edinburgh, UK
arXiv:2310.01882 [cs.DC], (3 Oct 2023)
@article{brown2023fortran,
title={Fortran performance optimisation and auto-parallelisation by leveraging MLIR-based domain specific abstractions in Flang},
author={Brown, Nick and Jamieson, Maurice and Lydike, Anton and Bauer, Emilien and Grosser, Tobias},
journal={arXiv preprint arXiv:2310.01882},
year={2023}
}
MLIR has become popular since it was open sourced in 2019. A sub-project of LLVM, the flexibility provided by MLIR to represent Intermediate Representations (IR) as dialects at different abstraction levels, to mix these, and to leverage transformations between dialects provides opportunities for automated program optimisation and parallelisation. In addition to general purpose compilers built upon MLIR, domain specific abstractions have also been developed. In this paper we explore complimenting the Flang MLIR general purpose compiler by combining with the domain specific Open Earth Compiler’s MLIR stencil dialect. Developing transformations to discover and extracts stencils from Fortran, this specialisation delivers between a 2 and 10 times performance improvement for our benchmarks on a Cray supercomputer compared to using Flang alone. Furthermore, by leveraging existing MLIR transformations we develop an auto-parallelisation approach targeting multi-threaded and distributed memory parallelism, and optimised execution on GPUs, without any modifications to the serial Fortran source code.
October 8, 2023 by hgpu
