RegisterIn this webinar, we cover parallel stencil computations in Julia using the ParallelStencil.jl package. This package enables you to write high-level code for fast computations on CPUs and GPUs. These computations are common in all numerical simulations involving the solution of discretized partial differential equations (PDEs) on a grid. ParallelStencil.jl provides high-level functions for computing derivatives and updating arrays. You can execute the same code on a single CPU, multiple CPUs with multithreading via Base.Threads, or on GPUs using either CUDA.jl (NVIDIA GPUs), AMDGPU.jl (AMD GPUs), or Metal.jl (Apple Silicon GPUs).Regardless of the underlying parallel hardware, all low-level communication between threads is hidden behind ParallelStencil.jl’s macro calls, ensuring that it remains invisible in the simulation code. This framework makes it highly accessible to domain scientists. The same high-level code can seamlessly run on CPUs and GPUs without any modifications.Furthermore, you can extend this framework to multiple processes, integrating ParallelStencil.jl with ImplicitGlobalGrid.jl (built upon MPI.jl). This combination facilitates easy scaling to multiple cluster nodes, with further parallelization on multiple cores and GPUs on each node. This architecture has been shown to scale efficiently to hundreds of GPUs and hundreds of cluster nodes.