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DTSTAMP:20260421T090517Z
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:Algorithms and Optimizations for Global Non-Linear Hybrid Flui
 d-Kinetic Finite Element Stellarator Simulations\n\nPredictive modeling of
  stellarator plasmas is crucial for advancing nuclear fusion energy, yet i
 t faces unique computational difficulties. A primary challenge is accurate
 ly simulating the dynamics of specific particle species not well captured 
 by fluid models, necessitating the use of hybrid fluid-k...\n\n\nLuca Vene
 rando Greco and Matthias Hoelzl (Max Planck Institute for Plasma Physics);
  Guido Huijsmans (CEA, IRFM); and Edoardo Carrà (Max Planck Institute for 
 Plasma Physics)\n---------------------\nMaintainable, Sustainable, and Gen
 eralisable Datalayouts and Vectorisation for Rigid Body Molecular Dynamics
 \n\nls1-MarDyn (ls1) is a Molecular Dynamics (MD) simulator designed for l
 arge-scale simulations of multi-site molecules and has been successfully u
 sed in a variety of scientific studies. It represents molecules as rigid b
 odies composed of multiple interaction sites that each exert forces on the
 ir neigh...\n\n\nSamuel James Newcome, Luis Gall, David Martin, Markus Müh
 lhäußer, and Hans-Joachim Bungartz (Technical University of Munich)\n-----
 ----------------\nAlgebraic Multi-Level Methods for Lattice Dirac Operator
 s in LQCD\n\nThe main computational challenge in Lattice QCD is the effici
 ent and scalable approximate solution of the Dirac equation Dz = b, where 
 D denotes the Dirac matrix on a four-dimensional space-time lattice. Moder
 n solvers for this case are based on Adaptive Multigrid. Among them, Domai
 n Decomposition A...\n\n\nPauline Schauerte (University of Bonn, Fraunhofe
 r SCAI) and Jaime Fabian Nieto Castellanos (Forschungszentrum Jülich, Univ
 ersity of Bonn)\n---------------------\nAn Integrated HPC Workflow for AI-
 Driven Immunogenic Peptide Prediction\n\nImmunogenic peptides play importa
 nt roles as drivers for the adaptive immune response - our bodies' ultimat
 e protection against infections and cancers. Parts of these peptides, call
 ed epitopes, are recognized by either major histocompatibility complexes o
 r antibodies, which then interact with T-cell...\n\n\nCathrine Bergh (KTH 
 Royal Institute of Technology), Leonardo Salicari (CINECA), Danai Kotzampa
 si and Victor Reys (Utrecht University), Narendra Kumar (National Institut
 e of Immunology), Archana Achalere and Sunitha Manjari Kasibhatla (Center 
 for the Development of Advanced Computing), Alessandra Villa (KTH Royal In
 stitute of Technology), Uddhavesh Sonavane (Center for the Development of 
 Advanced Computing), and Alexandre Bonvin (Utrecht University)\n----------
 -----------\nHypergraph Partitioning for Sparse Matrix Reordering\n\nFill-
 in during sparse matrix factorization remains a critical bottleneck in sci
 entific computing. We present an efficient hypergraph partitioning approac
 h for sparse matrix reordering based on the Clique-Node Hypergraph (CNH) r
 epresentation, building on prior work by Çatalyürek et al. and Selvitopi .
 ..\n\n\nRitvik Ranjan, Vincent Maillou, Alexandros Nikolaos Ziogas, and Ma
 thieu Luisier (ETH Zurich)\n---------------------\nBridging Python Flexibi
 lity and GPU Performance with Aithon:"Kernel-Level Optimization, Scaling, 
 and Extreme-Resolution MHD Turbulence Simulations"\n\nWe present Aithon, a
  GPU-accelerated incompressible flow solver for hydrodynamics and magnetoh
 ydrodynamics, designed for extreme-scale supercomputing. Optimized for AMD
  MI250X GPUs and deployed on the Frontier system, Aithon combines kernel-l
 evel GPU optimizations, CUDA/HIP-aware MPI, and Python int...\n\n\nManthan
  Verma (Indian Institute of Technology kanpur), Gina Sitaraman (AMD), and 
 Mahendra Verma (Indian Institute of Technology kanpur)\n------------------
 ---\nA High-Performance, GPGPU-Enabled Discontinuous Galërkin Solver Using
  OpenMP Offloading and MPI\n\nWe present a GPGPU-enabled modal Discontinuo
 us Galërkin solver that uses OpenMP+MPI. Device code is generated by offlo
 ading OpenMP pragmas, and inter-device/inter-node communication is enabled
  by MPI.\nOur test case implements a diffusion-advection solver with a Run
 ge-Kutta-Chebyshev time stepping sc...\n\n\nMarco Scarpelli, Paola Frances
 ca Antonietti, Carlo De Falco, and Luca Formaggia (Politecnico di Milano) 
 and Giovanni Viciconte (ENI S.p.A.)\n---------------------\nDeveloping and
  Evaluating Performance-Portable Physical Parametrization Codes\n\nWe pres
 ent results and ongoing work in the porting of physical parametrizations t
 o Python using the GridTools for Python (GT4Py) library. Our basis is the 
 Fortran code from the Integrated Forecasting System (IFS) which is run ope
 rationally at the European Centre for Medium-Range Weather Forecasts (E...
 \n\n\nGabriel Vollenweider and Stefano Ubbiali (ETH Zurich), Christian Küh
 nlein (ECMWF), and Heini Wernli (ETH Zurich)\n---------------------\nA Flu
 x-Form Semi-Lagrangian WENO Scheme on Triangular Meshes\n\nThe icosahedral
  model for weather and climate simulations utilises flux-form semi-Lagrang
 ian (FFSL) schemes for the transport of species. The motivation is the hig
 her Courant-Friedrich-Lewy (CFL) number compared to Eulerian approaches. T
 he schemes are implemented on the triangular mesh on a sphere w...\n\n\nAn
 dreas Jocksch (ETH Zurich / CSCS), Daniel Reinert (Deutscher Wetterdienst 
 (DWD)), Christoph Müller (MeteoSwiss), David Strassmann (ETH Zurich), and 
 Nina Burgdorfer (MeteoSwiss)\n---------------------\nAccelerating Lattice 
 QCD Dirac GCR Solvers with Multiple Right-Hand Sides (MRHS)\n\nLattice QCD
  simulations are often limited by the memory-bandwidth bottlenecks of solv
 ing the Dirac equation for numerous source vectors. We present an optimise
 d Multiple Right-Hand Side (MRHS) Generalised Conjugate Residual (GCR) sol
 ver in the openQxD framework that addresses these limitations. By t...\n\n
 \nJingJing Li and Roman Gruber (University of Bern) and Marina Krstic Mari
 nkovic (ETH Zurich)\n---------------------\nGraph Neural Network Potential
 s for Million-Atom Molecular Dynamics Simulations of Aluminum Solidificati
 on\n\nSolidification is ubiquitous in the fabrication of metal parts. Mole
 cular dynamics simulations can predict the microstructure and the correspo
 nding mechanical properties. However, both high accuracy of interatomic po
 tential energy and scalability to millions of atoms are required to captur
 e physical...\n\n\nIan Störmer and Julija Zavadlav (Technical University o
 f Munich)\n---------------------\nEvaluating Open-Source Infrastructure-As
 -Code Virtual Clusters against SuperMUC-NG Phase 1\n\nTraditional high-per
 formance computing (tHPC) infrastructure requires weeks to months for hard
 ware procurement, network configuration and software integration, which li
 mits agility for short-term projects and hampers reproducibility through n
 on-standardized configurations. Infrastructure-as-Code (Ia...\n\n\nPrasant
 h Babu Ganta, Elmira Birang, Plamen Dobrev, Birkan Emrem, Matteo Foglieni,
  and Ferdinand Jamitzky (Leibniz Supercomputing Centre)\n-----------------
 ----\nExploring Performance and Efficiency of State-Of-The-Art Deep Learni
 ng Protein Structure Prediction Frameworks on the Frontier Exascale Superc
 omputer\n\nAccurately predicting the structure of a protein has been a lon
 g standing and extremely challenging problem in biology. In recent years, 
 the rapid evolution and adoption of artificial intelligence have made the 
 prediction of protein structures leveraging deep learning frameworks with 
 accuracy rivali...\n\n\nVerónica G. Melesse Vergara, Elijah MacCarthy, Asi
 m YarKhan, John Holmen, Manesh Shah, Érica Teixeira Prates, and Dan Jacobs
 on (Oak Ridge National Laboratory)\n---------------------\nCorrelated Elec
 trons on Accelerated Architectures from Frequency-Dependent Response Funct
 ions\n\nUnderstanding, characterizing and engineering spectral properties 
 of correlated materials is crucial for next-generation technologies, inclu
 ding 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\nPaolo Settembri and Nicola Colonna (Paul Scherrer Ins
 titute); Anton Kozhevnikov (ETH Zurich / CSCS); and Nicola Marzari (EPFL, 
 Paul Scherrer Institute)\n---------------------\nOn the Nexus of Data, Mod
 els, and Supercomputing: Optimization and Uncertainty Quantification in HP
 C\n\nThe future of HPC will blend advanced simulation with model training,
  integrating multi-fidelity stochastic ensembles, computational steering, 
 active learning, and interactive visualization. As we move beyond single "
 hero" simulations, HPC must support dynamic workflows that allow scientifi
 c questio...\n\n\nAntigni Georgiadou (Oak Ridge National Laboratory)\n----
 -----------------\nCoupling km-Scale Earth System Model to Hierarchical Ou
 tput 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, heterogeneity, and the overhead of 
 ad-hoc workflows.\nWe introduce **Hiopy** (Hierarchical Output in Python),
  a lightweight in-situ output component,...\n\n\nNils-Arne Dreier (DKRZ) a
 nd Siddhant Tibrewal (Max Planck Institute for Meteorology)\n-------------
 --------\nGPU-Accelerated Methods for Numerically Stable Resampling in Flu
 id-Structure Interaction\n\nFluid-structure interaction simulations requir
 e accurate transfer of scalar fields between overlapping meshes with diffe
 rent topologies. We address the problem of transferring fields from unstru
 ctured tetrahedral to structured hexahedral meshes.\n\nThis problem is cha
 llenging because direct quadrature...\n\n\nSimone Riva (Università della S
 vizzera italiana) and Patrick Zulian (UniDistance Suisse, Università della
  Svizzera italiana)\n---------------------\nA Machine Learning Framework f
 or CFD Applications\n\nIn the present study, an automated framework is pre
 pared that contains two modules, Computational Fluid Dynamics (CFD) simula
 tions and surrogate modelling. CFD simulations are performed to model and 
 make thermal assessment of battery air cooling in different air stream con
 ditions (i.e. stream veloci...\n\n\nMasumeh Gholamisheeri, Harry Durnberge
 r, and Tim Powell (STFC)\n---------------------\nGeneralization of Long-Ra
 nge Machine Learning Potentials in Complex Chemical Spaces\n\nThe vastness
  of chemical space makes generalization a fundamental challenge for machin
 e learning interatomic potentials (MLIPs). Although MLIPs enable near–quan
 tum-accuracy atomistic simulations at greatly reduced computational cost, 
 their practical reliability is often limited by poor transfe...\n\n\nMicha
 ł Sanocki (Technical University of Munich)\n---------------------\nA Flexi
 ble Interface for Neural Network Potentials in GROMACS\n\nWe present a new
  interface for hybrid machine learning/molecular mechanics (ML/MM) simulat
 ions implemented in the molecular dynamics engine GROMACS. The interface e
 nables neural network potentials (NNPs) trained in the PyTorch framework t
 o contribute energies and forces during molecular dynamics (MD...\n\n\nLuk
 as Müllender and Berk Hess (KTH Royal Institute of Technology) and Erik Li
 ndahl (KTH Royal Institute of Technology, Stockholm University)\n---------
 ------------\nHybrid Block-Structured Grids for Coastal Ocean Domains\n\nA
 chieving high performance and performance portability is critical for next
 -generation climate and ocean modelling on heterogeneous computing systems
 . Ocean models face complex, fractal-like coastlines and rapidly varying b
 athymetry, making unstructured triangular meshes attractive for their flex
 ibi...\n\n\nJonathan Schmalfuß and Vadym Aizinger (University of Bayreuth)
 \n---------------------\nFPGA-Specific Optimizations for Multi-Device Shal
 low Water Simulations with SYCL\n\nThe shallow water equations are an esse
 ntial tool for modeling tides, tsunamis, and storm surges. At PASC 24, we 
 presented an implementation of the shallow water equations running on CPUs
 , GPUs and FPGAs. While the numerical code is shared across the different 
 architectures, the implementation uses ...\n\n\nChristoph Alt (Paderborn U
 niversity, Friedrich-Alexander-Universität Erlangen-Nürnberg); Markus Bütt
 ner (University of Bayreuth); Tobias Kenter (Paderborn University); Harald
  Köstler (Friedrich-Alexander-Universität Erlangen-Nürnberg); Christian Pl
 essl (Paderborn University); and Vadym Aizinger (University of Bayreuth)\n
 ---------------------\nDiscretization Error Quantification in Plane-Wave D
 ensity Functional Theory\n\nDensity functional theory (DFT) has become a w
 orkhorse of computational materials science. DFT computations in materials
  typically use a plane wave basis set, truncated at a so-called kinetic en
 ergy cutoff Ecut. Estimates for the truncation error of the basis set open
  opportunities for error balanci...\n\n\nBruno Ploumhans and Michael Herbs
 t (EPFL)\n---------------------\nParallel Tempering on Boundary Conditions
  with Normalizing Flows to Solve Topological Freezing\n\nIn particle physi
 cs, Lattice Quantum Chromodynamics (LQCD) studies the strong interaction, 
 responsible, for example, for the binding of atomic nuclei, through comput
 ational methods.\nAn essential part of LQCD consists on being able to samp
 le high-dimensional multi-modal distributions, for which direc...\n\n\nVic
 tor Granados (University of Bern)\n---------------------\nOptimizing the I
 CON Dynamical Core for GPUs Utilizing GT4Py and DaCe\n\nNumerical weather 
 predictions are based on a numerical model running on a large super comput
 er. Improving the performance of these models is an active field of resear
 ch which benefits society. The ICON model is a finite volume model running
  on an icosahedral mesh.\nFinite volume stencil computations ...\n\n\nChri
 stoph Müller (MeteoSwiss) and Magdalena Luz, Nicoletta Farabullini, Till E
 hrengruber, Chia Rui Ong, Daniel Hupp, Philip Müller, Edoardo Paone, Ioann
 is Magkanaris, Christos Kotsalos, Yilu Chen, Jacopo Canton, Hannes Vogt, E
 nrique González Paredes, Rico Häuselmann, Anurag Dipankar, Mauro Bianco, W
 illiam Sawyer, and Mikael Simberg (ETH Zurich / CSCS)\n-------------------
 --\nLarge-Scale Molecular Dynamics Simulations for Advances in Biomimetic 
 Carbon Capture Materials\n\nSustainable carbon capture and greenhouse gas 
 mitigation require solutions that are innovative, reproducible, and scalab
 le. Biomolecular catalysts are promising for low-energy CO2 capture, yet i
 ndustrial deployment is limited by reduced stability under harsh operating
  conditions and the difficulty o...\n\n\nMerve Fedai (North Carolina State
  University)\n---------------------\nEstimation of Global Surface Carbon F
 luxes at the Grid Scale Using Machine Learning Techniques\n\nMachine learn
 ing (ML) techniques have recently been applied in the field of geoscience 
 as in other fields, and has shown significant progress. One of the major a
 dvantages of ML is its remarkable effectiveness in overcoming the problem 
 of realistic computational costs from a computational science per...\n\n\n
 Ji-Sun Kang (Korea Institute of Science and Technology Information)\n-----
 ----------------\nCo-designing Regional High-Performance Computing Ecosyst
 ems in Africa: A Pilot Focus on Kenya and West Africa\n\nHigh-performance 
 computing is becoming more important for bioinformatics, genomics, and pub
 lic health research. However, in Africa, its growth and use remain uneven,
  scattered, and poorly documented. This study examines current HPC capacit
 y and access models in West, East, and Southern Africa, drawi...\n\n\nPaul
 ine Karega (University of Manchester, Bioinformatics Hub of Kenya initiati
 ve)\n---------------------\nAn Ensemble Machine Learning Model to Predict 
 2- and 10-Year Breast Cancer Recurrence Using Routine Hematological and Cl
 inical Data\n\nAccurate prediction of breast cancer recurrence remains dif
 ficult because prognosis varies across molecular subtypes, and genomic tes
 ts are often expensive or unavailable, leading to broad risk categories th
 at may cause overtreatment or undertreatment. We developed machine learnin
 g models integratin...\n\n\nPatricia Moreira (Inteli - Institute of Techno
 logy and Leadership)\n---------------------\nAdvancing The Data Assimilati
 on Research Testbed (DART) as an Early-Career Software Engineer\n\nThe Dat
 a Assimilation Research Testbed (DART) is an open-source software facility
  for ensemble data assimilation that combines information from numerical m
 odel predictions with measurements of the Earth system to enhance the valu
 e of both. It has supported a diverse community of users for over 20 ye...
 \n\n\nMarlena Smith (NSF National Center for Atmospheric Research)
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