Seamless acceleration of Fortran intrinsics via AMD AI engines
EPCC at the University of Edinburgh, Edinburgh, UK
arXiv:2502.10254 [cs.DC]
@misc{brown2025seamlessaccelerationfortranintrinsics,
title={Seamless acceleration of Fortran intrinsics via AMD AI engines},
author={Nick Brown and Gabriel Rodríguez Canal},
year={2025},
eprint={2502.10254},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2502.10254}
}
A major challenge that the HPC community faces is how to continue delivering the performance demanded by scientific programmers, whilst meeting an increased emphasis on sustainable operations. Specialised architectures, such as FPGAs and AMD’s AI Engines (AIEs), have been demonstrated to provide significant energy efficiency advantages, however a major challenge is that to most effectively program these architectures requires significant expertise and investment of time which is a major blocker. Fortran in the lingua franca of scientific computing, and in this paper we explore automatically accelerating Fortran intrinsics via the AIEs in AMD’s Ryzen AI CPU. Leveraging the open source Flang compiler and MLIR ecosystem, we describe an approach that lowers the MLIR linear algebra dialect to AMD’s AIE dialects, and demonstrate that for suitable workloads the AIEs can provide significant performance advantages over the CPU without any code modifications required by the programmer.
February 24, 2025 by hgpu
