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Programmer productivity and performance on AMD's AI Engines: Offloading Fortran intrinsics via MLIR a case-study
Session11th International Workshop on Heterogeneous High-Performance Reconfigurable Computing (H2RC 2025)
DescriptionA major challenge the HPC community faces is how to deliver increased performance demanded by scientific programmers, whilst addressing an increased emphasis on sustainable operations. Specialised architectures, such as FPGAs and AMD's AI Engines (AIEs), have demonstrated significant energy efficiency advantages, however a major challenge is that substantial expertise and investment of time is required to gain best performance from this hardware which is a major blocker.
Fortran in the lingua franca of scientific computing, and in this paper we explore the automatic offloading of Fortran intrinsics to the AIEs in AMD's Ryzen AI CPU as a case study, demonstrating how the MLIR compiler ecosystem can provide performance and programmer productivity. 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.
Fortran in the lingua franca of scientific computing, and in this paper we explore the automatic offloading of Fortran intrinsics to the AIEs in AMD's Ryzen AI CPU as a case study, demonstrating how the MLIR compiler ecosystem can provide performance and programmer productivity. 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.
