arXiv subjects

Gregor Olenik

Publications and source records attributed to Gregor Olenik.

4 recordsLinked to original sources

arXiv2026

The First OpenFOAM HPC Challenge (OHC-1)

The first OpenFOAM HPC Challenge (OHC-1) was organised by the OpenFOAM HPC Technical Committee (HPCTC) to collect a snapshot of OpenFOAM's computational performance on contemporary production hardware and to compare hardware-constrained submissions with software-track optimisations. Participants ran a common incompressible steady-state RANS case, the open-closed cooling DrivAer (occDrivAer) configuration, on prescribed meshes, submitting either with the reference setup (hardware track) or with modified solvers, decomposition strategies, or accelerator offloading (software track). In total, 237 valid datapoints were submitted by 12 contributors: 175 in the hardware track and 62 in the software track. The hardware track covered 25 distinct CPU models across AMD, Intel, and ARM families, with runs spanning from single-node configurations up to 256 nodes (32768 CPU cores). Wall-clock times ranged from 7.8 minutes to 65.7 hours and reported energy-to-solution from 2.1 to 236.9 kWh. Analysis of the hardware track identified a Pareto front of optimal balance between time- and energy-to-solution, and revealed that on-package high-bandwidth memory (HBM) dominates single-node performance for next-generation CPUs. Software-track submissions achieved up to 28% lower energy per iteration, 17% higher maximum performance per node, and 72% shorter minimum time per iteration than the best hardware-track results, with full GPU ports and selective-memory optimisations leading the performance range. This manuscript describes the challenge organisation, the case setup and metrics, and presents the main findings from both tracks together with an outlook for future challenges.

cs.DC↗

arXiv2025

Investigating Matrix Repartitioning to Address the Over- and Undersubscription Challenge for a GPU-based CFD Solver

Modern high-performance computing (HPC) increasingly relies on GPUs, but integrating GPU acceleration into complex scientific frameworks like OpenFOAM remains a challenge. Existing approaches either fully refactor the codebase or use plugin-based GPU solvers, each facing trade-offs between performance and development effort. In this work, we address the limitations of plugin-based GPU acceleration in OpenFOAM by proposing a repartitioning strategy that better balances CPU matrix assembly and GPU-based linear solves. We present a detailed computational model, describe a novel matrix repartitioning and update procedure, and evaluate its performance on large-scale CFD simulations. Our results show that the proposed method significantly mitigates oversubscription issues, improving solver performance and resource utilization in heterogeneous CPU-GPU environments.

cs.DC↗

arXiv2025

pyGinkgo: A Sparse Linear Algebra Operator Framework for Python

Sparse linear algebra is a cornerstone of many scientific computing and machine learning applications. Python has become a popular choice for these applications due to its simplicity and ease of use. Yet high performance sparse kernels in Python remain limited in functionality, especially on modern CPU and GPU architectures. We present pyGinkgo, a lightweight and Pythonic interface to the Ginkgo library, offering high-performance sparse linear algebra support with platform portability across CUDA, HIP, and OpenMP backends. pyGinkgo bridges the gap between high-performance C++ backends and Python usability by exposing Ginkgo's capabilities via Pybind11 and a NumPy and PyTorch compatible interface. We benchmark pyGinkgo's performance against state-of-the-art Python libraries including SciPy, CuPy, PyTorch, and TensorFlow. Results across hardware from different vendors demonstrate that pyGinkgo consistently outperforms existing Python tools in both sparse matrix vector (SpMV) product and iterative solver performance, while maintaining performance parity with native Ginkgo C++ code. Our work positions pyGinkgo as a compelling backend for sparse machine learning models and scientific workflows.

cs.MS↗

arXiv2025

Software Development Aspects of Integrating Linear Algebra Libraries

Many scientific discoveries are made through, or aided by, the use of simulation software. These sophisticated software applications are not built from the ground up, instead they rely on smaller parts for specific use cases, usually from domains unfamiliar to the application scientists. The software library Ginkgo is one of these building blocks to handle sparse numerical linear algebra on different platforms. By using Ginkgo, applications are able to ease the transition to modern systems, and speed up their simulations through faster numerical linear algebra routines. This paper discusses the challenges and benefits for application software in adopting Ginkgo. It will present examples from different domains, such as CFD, power grid simulation, as well as electro-cardiophysiology. For these cases, the impact of the integrations on the application code is discussed from a software engineering standpoint, and in particular, the approaches taken by Ginkgo and the applications to enable sustainable software development are highlighted.

cs.SE↗