Minisymposium Presentation
Enhancing GPU-Accelerated Scientific Computing in Julia with Ginkgo.jl
Presenter
Computational Science and Engineering (CSE) student and team member of the student HPC team RACKlette at ETH Zürich.
Description
Solving sparse linear systems on GPU-accelerated systems efficiently is a highly specialized and demanding task. The implementation of efficient solvers incorporates not only deep insights into the problem but also an extensive understanding of the underlying hardware and the respective platform-specific languages. This interdisciplinary orchestration poses significant challenges in scientific software development.
This talk presents recent developments on Ginkgo.jl, a Julia wrapper package of the modern C++-based sparse linear algebra library Ginkgo. We demonstrate its performance through a series of benchmarks and showcase an example where we solve the sparse linear system assembled from the finite element toolbox package, Ferrite.jl, using a preconditioned iterative solver. We highlight its interoperability with existing packages in the Julia package ecosystem.