BEGIN:VCALENDAR VERSION:2.0 PRODID:Linklings LLC BEGIN:VTIMEZONE TZID:Europe/Stockholm X-LIC-LOCATION:Europe/Stockholm BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:19700308T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:19701101T020000 RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260605T154541Z LOCATION:Plenary Room (Bldg. 6 - 001) DTSTART;TZID=Europe/Stockholm:20260629T192000 DTEND;TZID=Europe/Stockholm:20260629T195000 UID:submissions.pasc-conference.org_PASC26_sess124@linklings.com SUMMARY:Flash Poster Session - Part I DESCRIPTION:P21 - Hybrid Block-Structured Grids for Coastal Ocean Domains\ n\nAchieving high performance and performance portability is critical for next-generation climate and ocean modelling on heterogeneous computing sys tems. Ocean models face complex, fractal-like coastlines and rapidly varyi ng bathymetry, making unstructured triangular meshes attractive for their flexibi...\n\n\nJonathan Schmalfuß and Vadym Aizinger (University of Bayre uth)\n---------------------\nP05 - Bridging Python Flexibility and GPU Per formance with Aithon:"Kernel-Level Optimization, Scaling, and Extreme-Reso lution MHD Turbulence Simulations"\n\nWe present Aithon, a GPU-accelerated incompressible flow solver for hydrodynamics and magnetohydrodynamics, de signed for extreme-scale supercomputing. Optimized for AMD MI250X GPUs and deployed on the Frontier system, Aithon combines kernel-level GPU optimiz ations, CUDA/HIP-aware MPI, and Python int...\n\n\nManthan Verma (Indian I nstitute of Technology kanpur) and Gina Sitaraman and Paul Mullowney (AMD) \n---------------------\nP09 - Developing and Evaluating Performance-Porta ble Physical Parametrization Codes\n\nWe present results and ongoing work in the porting of physical parametrizations to Python using the GridTools for Python (GT4Py) library. Our basis is the Fortran code from the Integra ted Forecasting System (IFS) which is run operationally at the European Ce ntre for Medium-Range Weather Forecasts (E...\n\n\nGabriel Vollenweider an d Stefano Ubbiali (ETH Zurich), Christian Kühnlein (ECMWF), and Heini Wern li (ETH Zurich)\n---------------------\nP02 - Advancing The Data Assimilat ion Research Testbed (DART) as an Early-Career Software Engineer\n\nThe Da ta Assimilation Research Testbed (DART) is an open-source software facilit y for ensemble data assimilation that combines information from numerical model predictions with measurements of the Earth system to enhance the val ue of both. It has supported a diverse community of users for over 20 ye.. .\n\n\nMarlena Smith (NSF National Center for Atmospheric Research)\n----- ----------------\nP11 - Estimation of Global Surface Carbon Fluxes at the Grid Scale Using Machine Learning Techniques\n\nMachine learning (ML) tech niques have recently been applied in the field of geoscience as in other f ields, and has shown significant progress. One of the major advantages of ML is its remarkable effectiveness in overcoming the problem of realistic computational costs from a computational science per...\n\n\nJi-Sun Kang ( Korea Institute of Science and Technology Information)\n------------------ ---\nP15 - A Flux-Form Semi-Lagrangian WENO Scheme on Triangular Meshes\n\ nThe icosahedral model for weather and climate simulations utilises flux-f orm semi-Lagrangian (FFSL) schemes for the transport of species. The motiv ation is the higher Courant-Friedrich-Lewy (CFL) number compared to Euleri an approaches. The schemes are implemented on the triangular mesh on a sph ere w...\n\n\nAndreas Jocksch (ETH Zurich / CSCS); Daniel Reinert (Deutsch er Wetterdienst (DWD)); Christoph Müller (MeteoSwiss); David Strassmann (E TH Zurich); Nina Burgdorfer (MeteoSwiss); Anurag Dipankar (ETH Zurich); Ma uro Bianco (ETH Zurich / CSCS); and Thomas Schulthess (ETH Zurich, ETH Zur ich / CSCS)\n---------------------\nP16 - FPGA-Specific Optimizations for Multi-Device Shallow Water Simulations with SYCL\n\nThe shallow water equa tions are an essential tool for modeling tides, tsunamis, and storm surges . At PASC 24, we presented an implementation of the shallow water equation s running on CPUs, GPUs and FPGAs. While the numerical code is shared acro ss the different architectures, the implementation uses ...\n\n\nChristoph Alt (Paderborn University, Friedrich-Alexander-Universität Erlangen-Nürnb erg); Markus Büttner (University of Bayreuth); Tobias Kenter (Paderborn Un iversity); Harald Köstler (Friedrich-Alexander-Universität Erlangen-Nürnbe rg); Christian Plessl (Paderborn University); and Vadym Aizinger (Universi ty of Bayreuth)\n---------------------\nP14 - A Flexible Interface for Neu ral Network Potentials in GROMACS\n\nWe present a new interface for hybrid machine learning/molecular mechanics (ML/MM) simulations implemented in t he molecular dynamics engine GROMACS. The interface enables neural network potentials (NNPs) trained in the PyTorch framework to contribute energies and forces during molecular dynamics (MD...\n\n\nLukas Müllender and Berk Hess (KTH Royal Institute of Technology) and Erik Lindahl (KTH Royal Inst itute of Technology, Stockholm University)\n---------------------\nP03 - A lgebraic Multi-Level Methods for Lattice Dirac Operators in LQCD\n\nThe ma in computational challenge in Lattice QCD is the efficient and scalable ap proximate solution of the Dirac equation Dz = b, where D denotes the Dirac matrix on a four-dimensional space-time lattice. Modern solvers for this case are based on Adaptive Multigrid. Among them, Domain Decomposition A.. .\n\n\nPauline Schauerte (University of Bonn, Fraunhofer SCAI) and Jaime F abian Nieto Castellanos (Forschungszentrum Jülich, University of Bonn)\n-- -------------------\nP13 - Exploring Performance and Efficiency of State-o f-the-Art Deep Learning Protein Structure Prediction Frameworks on the Fro ntier Exascale Supercomputer\n\nAccurately predicting the structure of a p rotein has been a long standing and extremely challenging problem in biolo gy. In recent years, the rapid evolution and adoption of artificial intell igence have made the prediction of protein structures leveraging deep lear ning frameworks with accuracy rivali...\n\n\nVerónica G. Melesse Vergara, Elijah MacCarthy, Asim YarKhan, John Holmen, Manesh Shah, Érica Teixeira P rates, and Dan Jacobson (Oak Ridge National Laboratory)\n----------------- ----\nP17 - Generalization of Long-Range Machine Learning Potentials in Co mplex Chemical Spaces\n\nThe vastness of chemical space makes generalizati on a fundamental challenge for machine learning interatomic potentials (ML IPs). Although MLIPs enable near–quantum-accuracy atomistic simulations at greatly reduced computational cost, their practical reliability is often limited by poor transfe...\n\n\nMichał Sanocki (Technical University of Mu nich)\n---------------------\nP07 - Correlated Electrons on Accelerated Ar chitectures from Frequency-Dependent Response Functions\n\nUnderstanding, characterizing and engineering spectral properties of correlated materials is crucial for next-generation technologies, including energy harvesting and quantum technologies. These properties encode a material's response to external stimuli, and while important in general, they are eve...\n\n\nPa olo Settembri and Nicola Colonna (Paul Scherrer Institute); Anton Kozhevni kov (ETH Zurich / CSCS); and Nicola Marzari (EPFL, Paul Scherrer Institute )\n---------------------\nP06 - Co-designing Regional High-Performance Com puting Ecosystems in Africa: A Pilot Focus on Kenya and West Africa\n\nHig h-performance computing is becoming more important for bioinformatics, gen omics, and public health research. However, in Africa, its growth and use remain uneven, scattered, and poorly documented. This study examines curre nt HPC capacity and access models in West, East, and Southern Africa, draw i...\n\n\nPauline Karega (University of Manchester, Bioinformatics Hub of Kenya initiative)\n---------------------\nP08 - Coupling km-Scale Earth Sy stem Model to Hierarchical Output for Analysis-Ready Dataset\n\nKilometer- scale Earth System Model (ESM) simulations produce petabyte-scale outputs that are difficult to access, analyse, and share due to their size, hetero geneity, and the overhead of ad-hoc workflows.\nWe introduce **Hiopy** (Hi erarchical Output in Python), a lightweight in-situ output component,...\n \n\nNils-Arne Dreier (DKRZ) and Siddhant Tibrewal (Max Planck Institute fo r Meteorology)\n---------------------\nP22 - Hypergraph Partitioning for S parse Matrix Reordering\n\nFill-in during sparse matrix factorization rema ins a critical bottleneck in scientific computing. We present an efficient hypergraph partitioning approach for sparse matrix reordering based on th e Clique-Node Hypergraph (CNH) representation, building on prior work by Ç atalyürek et al. and Selvitopi ...\n\n\nRitvik Ranjan, Vincent Maillou, Al exandros Nikolaos Ziogas, and Mathieu Luisier (ETH Zurich)\n-------------- -------\nP19 - Graph Neural Network Potentials for Million-Atom Molecular Dynamics Simulations of Aluminum Solidification\n\nSolidification is ubiqu itous in the fabrication of metal parts. Molecular dynamics simulations ca n predict the microstructure and the corresponding mechanical properties. However, both high accuracy of interatomic potential energy and scalabilit y to millions of atoms are required to capture physical...\n\n\nIan Störme r and Julija Zavadlav (Technical University of Munich)\n------------------ ---\nP12 - Evaluating Open-Source Infrastructure-As-Code Virtual Clusters against SuperMUC-NG Phase 1\n\nTraditional high-performance computing (tHP C) infrastructure requires weeks to months for hardware procurement, netwo rk configuration and software integration, which limits agility for short- term projects and hampers reproducibility through non-standardized configu rations. Infrastructure-as-Code (Ia...\n\n\nPrasanth Babu Ganta, Elmira Bi rang, Plamen Dobrev, Birkan Emrem, Matteo Foglieni, and Ferdinand Jamitzky (Leibniz Supercomputing Centre)\n---------------------\nP10 - Discretizat ion Error Quantification in Plane-Wave Density Functional Theory\n\nDensit y functional theory (DFT) has become a workhorse of computational material s science. DFT computations in materials typically use a plane wave basis set, truncated at a so-called kinetic energy cutoff Ecut. Estimates for th e truncation error of the basis set open opportunities for error balanci.. .\n\n\nBruno Ploumhans and Michael Herbst (EPFL)\n---------------------\nP 23 - An Integrated HPC Workflow for AI-Driven Immunogenic Peptide Predicti on\n\nImmunogenic peptides play important roles as drivers for the adaptiv e immune response - our bodies' ultimate protection against infections and cancers. Parts of these peptides, called epitopes, are recognized by eith er major histocompatibility complexes or antibodies, which then interact w ith T-cell...\n\n\nCathrine Bergh (KTH Royal Institute of Technology), Leo nardo Salicari (CINECA), Danai Kotzampasi and Victor Reys (Utrecht Univers ity), Narendra Kumar (National Institute of Immunology), Archana Achalere and Sunitha Manjari Kasibhatla (Center for the Development of Advanced Com puting), Alessandra Villa (KTH Royal Institute of Technology), Uddhavesh S onavane (Center for the Development of Advanced Computing), and Alexandre Bonvin (Utrecht University)\n---------------------\nP04 - Algorithms and O ptimizations for Global Non-Linear Hybrid Fluid-Kinetic Finite Element Ste llarator Simulations\n\nPredictive modeling of stellarator plasmas is cruc ial for advancing nuclear fusion energy, yet it faces unique computational difficulties. A primary challenge is accurately simulating the dynamics o f specific particle species not well captured by fluid models, necessitati ng the use of hybrid fluid-k...\n\n\nLuca Venerando Greco and Matthias Hoe lzl (Max Planck Institute for Plasma Physics); Guido Huijsmans (CEA, IRFM) ; and Edoardo Carrà (Max Planck Institute for Plasma Physics)\n----------- ----------\nP27 - Parallel Tempering on Boundary Conditions with Normalizi ng Flows to Solve Topological Freezing\n\nIn particle physics, Lattice Qua ntum Chromodynamics (LQCD) studies the strong interaction, responsible, fo r example, for the binding of atomic nuclei, through computational methods .\nAn essential part of LQCD consists on being able to sample high-dimensi onal multi-modal distributions, for which direc...\n\n\nVictor Granados (U niversity of Bern)\n---------------------\nP26 - Optimizing the ICON Dynam ical Core for GPUs Utilizing GT4Py and DaCe\n\nNumerical weather predictio ns are based on a numerical model running on a large super computer. Impro ving the performance of these models is an active field of research which benefits society. The ICON model is a finite volume model running on an ic osahedral mesh.\nFinite volume stencil computations ...\n\n\nChristoph Mül ler (MeteoSwiss) and Magdalena Luz, Nicoletta Farabullini, Till Ehrengrube r, Chia Rui Ong, Daniel Hupp, Philip Müller, Edoardo Paone, Ioannis Magkan aris, Christos Kotsalos, Yilu Chen, Jacopo Canton, Hannes Vogt, Enrique Go nzález Paredes, Rico Häuselmann, Anurag Dipankar, Mauro Bianco, William Sa wyer, and Mikael Simberg (ETH Zurich / CSCS)\n---------------------\nP25 - Maintainable, Sustainable, and Generalizable Data Layouts and Vectorizati on for Rigid-Body Molecular Dynamics\n\nls1-MarDyn (ls1) is an MD simulato r designed for large-scale simulations of multi-site molecules and has bee n successfully used in a variety of scientific studies. It represents mole cules as rigid bodies composed of multiple interaction sites that each exe rt forces on their neighbours, which are det...\n\n\nSamuel James Newcome, Luis Gall, David Martin, Markus Mühlhäußer, and Hans-Joachim Bungartz (Te chnical University of Munich)\n---------------------\nP18 - GPU-Accelerate d Methods for Numerically Stable Resampling in Fluid-Structure Interaction \n\nFluid-structure interaction simulations require accurate transfer of s calar fields between overlapping meshes with different topologies. We addr ess the problem of transferring fields from unstructured tetrahedral to st ructured hexahedral meshes.\n\nThis problem is challenging because direct quadrature...\n\n\nSimone Riva (Università della Svizzera italiana) and Pa trick Zulian (UniDistance Suisse, Università della Svizzera italiana)\n--- ------------------\nP24 - A Machine Learning Framework for CFD Application s\n\nIn the present study, an automated framework is prepared that contain s two modules, Computational Fluid Dynamics (CFD) simulations and surrogat e modelling. CFD simulations are performed to model and make thermal asses sment of battery air cooling in different air stream conditions (i.e. stre am veloci...\n\n\nMasumeh Gholamisheeri, Harry Durnberger, Tim Powell, and Jony Castagna (STFC)\n---------------------\nP01 - Accelerating Lattice Q CD Dirac GCR Solvers with Multiple Right-Hand Sides (MRHS)\n\nLattice QCD simulations are often limited by the memory-bandwidth bottlenecks of solvi ng the Dirac equation for numerous source vectors. We present an optimised Multiple Right-Hand Side (MRHS) Generalised Conjugate Residual (GCR) solv er in the openQxD framework that addresses these limitations. By t...\n\n\ nJingJing Li and Roman Gruber (University of Bern) and Marina Krstic Marin kovic (ETH Zurich)\n---------------------\nP20 - A High-Performance, GPGPU -Enabled Discontinuous Galërkin Solver Using OpenMP Offloading and MPI\n\n We present a GPGPU-enabled modal Discontinuous Galërkin solver that uses O penMP+MPI. Device code is generated by offloading OpenMP pragmas, and inte r-device/inter-node communication is enabled by MPI.\nOur test case implem ents a diffusion-advection solver with a Runge-Kutta-Chebyshev time steppi ng sc...\n\n\nMarco Scarpelli, Paola Francesca Antonietti, Carlo De Falco, and Luca Formaggia (Politecnico di Milano) and Giovanni Viciconte (ENI S. p.A.)\n\nSession Chair: Miroslava Nedyalkova (University of Fribourg) END:VEVENT END:VCALENDAR