Kajol Kulkarni | Searcharxiv

arXiv2025

Harvesting energy consumption on European HPC systems: Sharing Experience from the CEEC project

Energy efficiency has emerged as a central challenge for modern high-performance computing (HPC) systems, where escalating computational demands and architectural complexity have led to significant energy footprints. This paper presents the collective experience of the EuroHPC JU Center of Excellence in Exascale CFD (CEEC) in measuring, analyzing, and optimizing energy consumption across major European HPC systems. We briefly review key methodologies and tools for energy measurement as well as define metrics for reporting results. Through case studies using representative CFD applications (waLBerla, FLEXI/GALÆXI, Neko, and NekRS), we evaluate energy-to-solution and time-to-solution metrics on diverse architectures, including CPU- and GPU-based partitions of LUMI, MareNostrum5, MeluXina, and JUWELS Booster. Our results highlight the advantages of accelerators and mixed-precision techniques for reducing energy consumption while maintaining computational accuracy. Finally, we advocate the need to facilitate energy measurements on HPC systems in order to raise awareness, teach the community, and take actions toward more sustainable exascale computing.

cs.DC↗

arXiv2025

ChannelFlow-Tools: A Configuration-Driven Pipeline for Generating Machine-Learning-Ready Datasets of 3D Obstructed Channel Flows

Data-driven surrogate models are increasingly used in computational fluid dynamics, and their reliability depends on the quality of the training data. These models are typically trained on fixed, pre-generated datasets. Systematic surrogate studies require controlled data generation, in which datasets can be regenerated, adapted, or extended to match specific research requirements. We introduce ChannelFlow-Tools, an open-source, configuration-driven pipeline for generating ML-ready datasets of three-dimensional obstructed channel flows. The pipeline integrates procedural obstacle geometry generation across six shape families, signed-distance-field (SDF) voxelisation, lattice-Boltzmann simulation, and packaging into ML-ready tensors. The workflow is driven by configuration files, with byte-identical reproducibility verified for the geometry-generation stage. The pipeline is evaluated through a full-corpus mesh-integrity audit, analytical and corpus-level validation of the SDF representation, canonical sphere-flow benchmarks for the solver, and a per-scene data-integrity audit. To demonstrate that the pipeline produces physically consistent and directly usable training data, three surrogate models (3D U-Net, FNO, and U-FNO) are trained on a sample dataset of 450 simulations spanning $Re_c \approx 1000$-$10{,}000$, generated entirely through the pipeline. The models learn the geometry-to-flow mapping and show physically interpretable behaviour on shape-family and Reynolds-number out-of-distribution splits, confirming direct downstream usability. ChannelFlow-Tools thus provides shared, auditable infrastructure for controlled benchmarking of geometry-aware CFD surrogates.

cs.GR↗