Solving differential equations in parallel on GPUs
07-23, 14:00–17:00 (UTC), Green

Why to wait hours for computations to complete, when it could take only a few seconds? Tired of prototyping code in an interactive, high-level language and rewriting it in a lower-level language to get high-performance code? Or simply curious about parallel and GPU computing being game changers.


The workshop materials can be found here: https://github.com/luraess/parallel-gpu-workshop-JuliaCon21

This workshop covers trendy areas in modern numerical computing with examples from geoscientific applications. The physical processes governing natural systems' evolution are often mathematically described as systems of differential equations. Fast and accurate solutions require numerical implementations to leverage modern parallel hardware.

The goal of this workshop is to offer an interactive hands-on to solve systems of differential equations in parallel on GPUs using the ParallelStencil.jl and ImplicitGlobalGrid.jl Julia modules. ParallelStencil.jl permits to write architecture-agnostic parallel high-performance GPU and CPU code and ImplicitGlobalGrid.jl renders stencil-based distributed parallelisation almost trivial. The resulting codes are fast, short and readable. We will use these two Julia modules to design and implement a (multi-) GPU application that predicts ice flow dynamics over mountainous topography.

The workshop consists of 2 parts:
1. You will learn about parallel and distributed computing and iterative solvers.
2. You will implement a PDE solver to predict ice flow dynamics on real topography.

By the end of this workshop, you will:
- Have a GPU PDE solver that predicts ice-flow;
- Have a concise Julia code that achieves similar performance than legacy C, CUDA, MPI code;
- Be able to leverage the computing power of modern GPU accelerated servers and supercomputers.

We look forward to having you on board and will make sure to foster exchange of ideas and knowledge to provide an as inclusive as possible event.