Ludovic Räss

Geoscientist with strong interests in Julia, HPC, GPUs, and supercomputing. Applications to resolve multi-physics processes in ice dynamics and geodynamics across scales.

Talks:

13:00 UTC

Teaching GPU computing, experiences from our Master-level course

07/27/2022, 1:00 PM1:30 PM UTC
Blue

In the Fall Semester 2021 at ETH Zurich, we designed and taught a new course: Solving PDEs in parallel on GPUs with Julia. We present technical and teaching experiences we gained: we look at our tech-stack CUDA.jl, ParallelStencils.jl and ImplictGlobalGrid.jl for GPU-computing; and Franklin.jl, Literate.jl, IJulia.jl/Jupyter for web, slides, and exercises. We look into the crash-course in Julia, teaching software-engineering (git, CI) and project-based student evaluations.

13:40 UTC

GPU4GEO - Frontier GPU multi-physics solvers in Julia

07/27/2022, 1:40 PM1:50 PM UTC
Blue

The accelerating outflow of ice in Antarctica or Greenland due to a warming climate or the geodynamic processes shaping the Earth share common computational challenges: extreme-scale high-performance computing (HPC) which requires the next-generation of numerical models, parallel solvers and supercomputers. We here present a fresh approach to modern HPC and share our experience running Julia on thousands of graphical processing units (GPUs).

15:20 UTC

High-performance xPU Stencil Computations in Julia

07/27/2022, 3:20 PM3:30 PM UTC
Purple

We present an efficient approach for writing architecture-agnostic parallel high-performance stencil computations in Julia. Powerful metaprogramming, costless abstractions and multiple dispatch enable writing a single code that is usable for both productive prototyping on a single CPU and for production runs on GPU or CPU workstations or supercomputers. Performance similar to CUDA C is achievable, which is typically a large improvement over reachable performance with CUDA.jl Array programming.

15:30 UTC

Distributed Parallelization of xPU Stencil Computations in Julia

07/27/2022, 3:30 PM3:40 PM UTC
Purple

We present a straightforward approach for distributed parallelization of stencil-based Julia applications on a regular staggered grid using GPUs and CPUs. The approach allows to leverage remote direct memory access and was shown to enable close to ideal weak scaling of real-world applications on thousands of GPUs. The communication performed can be easily hidden behind computation.

Platinum sponsors

Julia ComputingRelational AIJulius Technology

Gold sponsors

IntelAWS

Silver sponsors

Invenia LabsBeacon BiosignalsMetalenzASMLG-ResearchConningPumas AIQuEra Computing Inc.Jeffrey Sarnoff

Media partners

Packt PublicationGather TownVercel

Community partners

Data UmbrellaWiMLDS

Fiscal Sponsor

NumFOCUS