4 papers
Lifting to tensors when compiling scientific computing workloads for AI Engines
Nick Brown, Gabriel Rodriguez-Canal
It has been demonstrated that specialised architectures, such as FPGAs and AMD's AI Engines (AIEs), have the potential to deliver energy and performance advantages for scientific c…
An MLIR pipeline for offloading Fortran to FPGAs via OpenMP
Gabriel Rodriguez-Canal, David Katz, Nick Brown
With the slowing of Moore's Law, heterogeneous computing platforms such as Field Programmable Gate Arrays (FPGAs) have gained increasing interest for accelerating HPC workloads. In…
A shared compilation stack for distributed-memory parallelism in stencil DSLs
George Bisbas, Anton Lydike, Emilien Bauer +8
Domain Specific Languages (DSLs) increase programmer productivity and provide high performance. Their targeted abstractions allow scientists to express problems at a high level, pr…
Seamless acceleration of Fortran intrinsics via AMD AI engines
Nick Brown, Gabriel RodrÃguez Canal
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…