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DTSTAMP:20250822T115805Z
LOCATION:Room 5.0B15 & 16
DTSTART;TZID=Europe/Stockholm:20250617T163000
DTEND;TZID=Europe/Stockholm:20250617T170000
UID:submissions.pasc-conference.org_PASC25_sess135_msa119@linklings.com
SUMMARY:Parallelizing GaPSE.jl with KernelAbstraction.jl: A Real-World Exa
 mple of Reproducibility in Julia
DESCRIPTION:Matteo Foglieni (Leibniz Supercomputing Centre)\n\nJulia is ga
 ining traction in scientific computing, and at the Leibniz Supercomputing 
 Centre (LRZ), we are exploring its potential on our high-performance compu
 ting (HPC) system, particularly on our Intel Ponte Vecchio GPUs of the Sup
 erMUC-NG Phase 2 supercomputer. The Julia package KernelAbstraction.jl ena
 bles vendor-agnostic parallelization, allowing developers to write kernels
  that run efficiently on both multi-threaded CPUs and various GPU architec
 tures with minimal modifications. This ability to write a single single-so
 urce, hardware-agnostic kernel bridges the gap between different hardware 
 backends and enhances the reproducibility of both results and performance 
 across diverse computing environments. To evaluate its real-world impact, 
 we apply KernelAbstraction.jl to GaPSE.jl, a cosmology program that comput
 es Two-Point Correlation Functions of galaxies including General Relativis
 tic effects. GaPSE.jl needs to perform numerous nested integrals, which ca
 n be computationally expensive. By leveraging parallel execution on CPUs a
 nd GPUs, we aim to significantly accelerate these calculations, improving 
 efficiency and scalability. In this talk, we share our experience developi
 ng and optimizing kernels with KernelAbstraction.jl, we benchmark Julia’s 
 performance on HPC, and we show how reproducibility is ensured in a real-w
 orld application.\n\nDomain: Chemistry and Materials, Engineering, Life Sc
 iences, Physics, Computational Methods and Applied Mathematics\n\nSession 
 Chair: Samuel Omlin (ETH Zurich / CSCS)\n\n
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