2 citations · 2 across the 3 of their papers we have counts for
6 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…
Seamless acceleration of Fortran intrinsics via AMD AI engines
Nick Brown, Gabriel Rodríguez 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…
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…
Stencil-HMLS: A multi-layered approach to the automatic optimisation of stencil codes on FPGA
Gabriel Rodriguez-Canal, Nick Brown, Maurice Jamieson +3
The challenges associated with effectively programming FPGAs have been a major blocker in popularising reconfigurable architectures for HPC workloads. However new compiler technolo…
Fortran High-Level Synthesis: Reducing the barriers to accelerating HPC codes on FPGAs
Gabriel Rodriguez-Canal, Nick Brown, Tim Dykes +2
In recent years the use of FPGAs to accelerate scientific applications has grown, with numerous applications demonstrating the benefit of FPGAs for high performance workloads. Howe…