Seamless acceleration of Fortran intrinsics via AMD AI engines

TL;DR

This paper presents an approach to automatically accelerate Fortran intrinsics using AMD's AI Engines on Ryzen CPUs, leveraging MLIR and Flang, achieving significant performance gains without programmer modifications.

Contribution

It introduces a compiler-based method to automatically map Fortran intrinsics to AMD's AI Engines, simplifying programming and enhancing performance for scientific workloads.

Findings

AIEs outperform CPU for suitable workloads

No code modifications needed for acceleration

Effective use of MLIR and Flang for automation

Abstract

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.