high performance computing on graphics processing units: hgpu.org

In this thesis we show the benefits of the novel MLIR compiler technology to the generation of code from a DSL, namely EasyML used in openCARP, a widely used simulator in the cardiac electrophysiology community. Building on an existing work we deeply modified openCARP’s native code generator to enable efficient vectorized CPU and GPU code generation (Nvidia CUDA and AMD ROCm). Generating optimized code for different accelerators requires specific optimizations and we review how MLIR has been used to enable multi-target code generation from an integrated generator. To our knowledge, this is the first work that deeply connects an optimizing compiler infrastructure to electrophysiology models of the human body, showing the potential benefits of using compiler technology in the simulation of human cell interactions. Additionally, we did a study on the polyhedral compilers and generalized our techniques using Polygeist to improve the vectorization and heterogeneous code generation of polyhedral compilers.