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DTSTAMP:20260724T151408Z
LOCATION:Bldg. 6 - 001 - Plenary Room
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:Access the recording\n\nP01 - Accelerating Lattice QCD Dirac G
 CR Solvers with Multiple Right-Hand Sides (MRHS)\n\nLattice QCD simulation
 s are often limited by the memory-bandwidth bottlenecks of solving the Dir
 ac equation for numerous source vectors. We present an optimised Multiple 
 Right-Hand Side (MRHS) Generalised Conjugate Residual (GCR) solver in the 
 openQxD framework that addresses these limitations. By t...\n\n\nJingJing 
 Li and Roman Gruber (University of Bern) and Marina Krstic Marinkovic (ETH
  Zurich)\n---------------------\nP02 - Advancing The Data Assimilation Res
 earch Testbed (DART) as an Early-Career Software Engineer\n\nThe Data Assi
 milation Research Testbed (DART) is an open-source software facility for e
 nsemble data assimilation that combines information from numerical model p
 redictions with measurements of the Earth system to enhance the value of b
 oth. It has supported a diverse community of users for over 20 ye...\n\n\n
 Marlena Smith (NSF National Center for Atmospheric Research)\n------------
 ---------\nP03 - Algebraic Multi-Level Methods for Lattice Dirac Operators
  in LQCD\n\nThe main computational challenge in Lattice QCD is the efficie
 nt and scalable approximate 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 Fabian Nieto Castellanos (Forschungszentrum Jülich, Unive
 rsity of Bonn)\n---------------------\nP04 - Algorithms and Optimizations 
 for Global Non-Linear Hybrid Fluid-Kinetic Finite Element Stellarator Simu
 lations\n\nPredictive modeling of stellarator plasmas is crucial for advan
 cing nuclear fusion energy, yet it faces unique computational difficulties
 . A primary challenge is accurately simulating the dynamics of specific pa
 rticle species not well captured by fluid models, necessitating the use of
  hybrid fluid-k...\n\n\nLuca Venerando Greco and Matthias Hoelzl (Max Plan
 ck Institute for Plasma Physics); Guido Huijsmans (CEA, IRFM); and Edoardo
  Carrà (Max Planck Institute for Plasma Physics)\n---------------------\nP
 05 - Bridging Python Flexibility and GPU Performance with Aithon: Kernel-L
 evel Optimization, Scaling, and Extreme-Resolution MHD Turbulence Simulati
 ons\n\nWe present Aithon, a GPU-accelerated incompressible flow solver for
  hydrodynamics and magnetohydrodynamics, designed for extreme-scale superc
 omputing. Optimized for AMD MI250X GPUs and deployed on the Frontier syste
 m, Aithon combines kernel-level GPU optimizations, CUDA/HIP-aware MPI, and
  Python int...\n\n\nManthan Verma (Indian Institute of Technology kanpur) 
 and Gina Sitaraman and Paul Mullowney (AMD)\n---------------------\nP07 - 
 Correlated Electrons on Accelerated Architectures from Frequency-Dependent
  Response Functions\n\nUnderstanding, characterizing and engineering spect
 ral properties of correlated materials is crucial for next-generation tech
 nologies, including energy harvesting and quantum technologies. These prop
 erties encode a material's response to external stimuli, and while importa
 nt in general, they are eve...\n\n\nPaolo Settembri and Nicola Colonna (Pa
 ul Scherrer Institute); Anton Kozhevnikov (ETH Zurich / CSCS); and Nicola 
 Marzari (EPFL, Paul Scherrer Institute)\n---------------------\nP08 - Coup
 ling km-Scale Earth System Model to Hierarchical Output for Analysis-Ready
  Dataset\n\nKilometer-scale Earth System Model (ESM) simulations produce p
 etabyte-scale outputs that are difficult to access, analyse, and share due
  to their size, heterogeneity, and the overhead of ad-hoc workflows.\nWe i
 ntroduce **Hiopy** (Hierarchical Output in Python), a lightweight in-situ 
 output component,...\n\n\nNils-Arne Dreier (DKRZ) and Siddhant Tibrewal (M
 ax Planck Institute for Meteorology)\n---------------------\nP10 - Discret
 ization Error Quantification in Plane-Wave Density Functional Theory\n\nDe
 nsity functional theory (DFT) has become a workhorse of computational mate
 rials science. DFT computations in materials typically use a plane wave ba
 sis set, truncated at a so-called kinetic energy cutoff Ecut. Estimates fo
 r the truncation error of the basis set open opportunities for error balan
 ci...\n\n\nBruno Ploumhans and Michael Herbst (EPFL)\n--------------------
 -\nP11 - Estimation of Global Surface Carbon Fluxes at the Grid Scale Usin
 g Machine Learning Techniques\n\nMachine learning (ML) techniques have rec
 ently been applied in the field of geoscience as in other fields, and has 
 shown significant progress. One of the major advantages of ML is its remar
 kable effectiveness in overcoming the problem of realistic computational c
 osts from a computational science per...\n\n\nJi-Sun Kang (Korea Institute
  of Science and Technology Information)\n---------------------\nP12 - Eval
 uating Open-Source Infrastructure-As-Code Virtual Clusters against SuperMU
 C-NG Phase 1\n\nTraditional high-performance computing (tHPC) infrastructu
 re requires weeks to months for hardware procurement, network configuratio
 n and software integration, which limits agility for short-term projects a
 nd hampers reproducibility through non-standardized configurations. Infras
 tructure-as-Code (Ia...\n\n\nPrasanth Babu Ganta, Elmira Birang, Plamen Do
 brev, Birkan Emrem, Matteo Foglieni, and Ferdinand Jamitzky (Leibniz Super
 computing Centre)\n---------------------\nP13 - Exploring Performance and 
 Efficiency of State-of-the-Art Deep Learning Protein Structure Prediction 
 Frameworks on the Frontier Exascale Supercomputer\n\nAccurately predicting
  the structure of a protein has been a long standing and extremely challen
 ging 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, Asim YarKhan, John Holmen, Manesh S
 hah, Érica Teixeira Prates, and Dan Jacobson (Oak Ridge National Laborator
 y)\n---------------------\nP14 - A Flexible Interface for Neural Network P
 otentials in GROMACS\n\nWe present a new interface for hybrid machine lear
 ning/molecular mechanics (ML/MM) simulations implemented in the molecular 
 dynamics engine GROMACS. The interface enables neural network potentials (
 NNPs) trained in the PyTorch framework to contribute energies and forces d
 uring molecular dynamics (MD...\n\n\nLukas Müllender and Berk Hess (KTH Ro
 yal Institute of Technology) and Erik Lindahl (KTH Royal Institute of Tech
 nology, Stockholm University)\n---------------------\nP15 - A Flux-Form Se
 mi-Lagrangian WENO Scheme on Triangular Meshes\n\nThe icosahedral model fo
 r weather and climate simulations utilises flux-form semi-Lagrangian (FFSL
 ) schemes for the transport of species. The motivation is the higher Coura
 nt-Friedrich-Lewy (CFL) number compared to Eulerian approaches. The scheme
 s are implemented on the triangular mesh on a sphere w...\n\n\nAndreas Joc
 ksch (ETH Zurich / CSCS); Daniel Reinert (Deutscher Wetterdienst (DWD)); C
 hristoph Müller (MeteoSwiss); David Strassmann (ETH Zurich); Nina Burgdorf
 er (MeteoSwiss); Anurag Dipankar (ETH Zurich); Mauro Bianco (ETH Zurich / 
 CSCS); and Thomas Schulthess (ETH Zurich, ETH Zurich / CSCS)\n------------
 ---------\nP16 - FPGA-Specific Optimizations for Multi-Device Shallow Wate
 r Simulations with SYCL\n\nThe shallow water equations are an essential to
 ol for modeling tides, tsunamis, and storm surges. At PASC 24, we presente
 d an implementation of the shallow water equations running on CPUs, GPUs a
 nd FPGAs. While the numerical code is shared across the different architec
 tures, the implementation uses ...\n\n\nChristoph Alt (Paderborn Universit
 y, Friedrich-Alexander-Universität Erlangen-Nürnberg); Markus Büttner (Uni
 versity of Bayreuth); Tobias Kenter (Paderborn University); Harald Köstler
  (Friedrich-Alexander-Universität Erlangen-Nürnberg); Christian Plessl (Pa
 derborn University); and Vadym Aizinger (University of Bayreuth)\n--------
 -------------\nP17 - Generalization of Long-Range Machine Learning Potenti
 als in Complex Chemical Spaces\n\nThe vastness of chemical space makes gen
 eralization a fundamental challenge for machine learning interatomic poten
 tials (MLIPs). Although MLIPs enable near–quantum-accuracy atomistic simul
 ations at greatly reduced computational cost, their practical reliability 
 is often limited by poor transfe...\n\n\nMichał Sanocki (Technical Univers
 ity of Munich)\n---------------------\nP18 - GPU-Accelerated Methods for N
 umerically Stable Resampling in Fluid-Structure Interaction\n\nFluid-struc
 ture interaction simulations require accurate transfer of scalar fields be
 tween overlapping meshes with different topologies. We address the problem
  of transferring fields from unstructured tetrahedral to structured hexahe
 dral meshes.\n\nThis problem is challenging because direct quadrature...\n
 \n\nSimone Riva (Università della Svizzera italiana) and Patrick Zulian (U
 niDistance Suisse, Università della Svizzera italiana)\n------------------
 ---\nP19 - Graph Neural Network Potentials for Million-Atom Molecular Dyna
 mics Simulations of Aluminum Solidification\n\nSolidification is ubiquitou
 s in the fabrication of metal parts. Molecular dynamics simulations can pr
 edict the microstructure and the corresponding mechanical properties. Howe
 ver, both high accuracy of interatomic potential energy and scalability to
  millions of atoms are required to capture physical...\n\n\nIan Störmer an
 d Julija Zavadlav (Technical University of Munich)\n---------------------\
 nP20 - A High-Performance, GPGPU-Enabled Discontinuous Galërkin Solver Usi
 ng OpenMP Offloading and MPI\n\nWe present a GPGPU-enabled modal Discontin
 uous Galërkin solver that uses OpenMP+MPI. Device code is generated by off
 loading OpenMP pragmas, and inter-device/inter-node communication is enabl
 ed by MPI.\nOur test case implements a diffusion-advection solver with a R
 unge-Kutta-Chebyshev time stepping sc...\n\n\nMarco Scarpelli, Paola Franc
 esca Antonietti, Carlo De Falco, and Luca Formaggia (Politecnico di Milano
 ) and Giovanni Viciconte (ENI S.p.A.)\n---------------------\nP21 - Hybrid
  Block-Structured Grids for Coastal Ocean Domains\n\nAchieving high perfor
 mance 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 bathymetry, making un
 structured triangular meshes attractive for their flexibi...\n\n\nJonathan
  Schmalfuß and Vadym Aizinger (University of Bayreuth)\n------------------
 ---\nP22 - Hypergraph Partitioning for Sparse Matrix Reordering\n\nFill-in
  during sparse matrix factorization remains a critical bottleneck in scien
 tific computing. We present an efficient hypergraph partitioning approach 
 for sparse matrix reordering based on the Clique-Node Hypergraph (CNH) rep
 resentation, building on prior work by Çatalyürek et al. and Selvitopi ...
 \n\n\nRitvik Ranjan, Vincent Maillou, Alexandros Nikolaos Ziogas, and Math
 ieu Luisier (ETH Zurich)\n---------------------\nP23 - An Integrated HPC W
 orkflow for AI-Driven Immunogenic Peptide Prediction\n\nImmunogenic peptid
 es play important roles as drivers for the adaptive immune response - our 
 bodies' ultimate protection against infections and cancers. Parts of these
  peptides, called epitopes, are recognized by either major histocompatibil
 ity complexes or antibodies, which then interact with T-cell...\n\n\nCathr
 ine Bergh (KTH Royal Institute of Technology), Leonardo Salicari (CINECA),
  Danai Kotzampasi and Victor Reys (Utrecht University), Narendra Kumar (Na
 tional Institute of Immunology), Archana Achalere and Sunitha Manjari Kasi
 bhatla (Center for the Development of Advanced Computing), Alessandra Vill
 a (KTH Royal Institute of Technology), Uddhavesh Sonavane (Center for the 
 Development of Advanced Computing), and Alexandre Bonvin (Utrecht Universi
 ty)\n---------------------\nP24 - A Machine Learning Framework for CFD App
 lications\n\nIn the present study, an automated framework is prepared that
  contains two modules, Computational Fluid Dynamics (CFD) simulations and 
 surrogate modelling. CFD simulations are performed to model and make therm
 al assessment of battery air cooling in different air stream conditions (i
 .e. stream veloci...\n\n\nMasumeh Gholamisheeri, Harry Durnberger, Tim Pow
 ell, and Jony Castagna (STFC)\n---------------------\nP25 - Maintainable, 
 Sustainable, and Generalizable Data Layouts and Vectorization for Rigid-Bo
 dy Molecular Dynamics\n\nls1-MarDyn (ls1) is an MD simulator designed for 
 large-scale simulations of multi-site molecules and has been successfully 
 used in a variety of scientific studies. It represents molecules as rigid 
 bodies composed of multiple interaction sites that each exert forces on th
 eir neighbours, which are det...\n\n\nSamuel James Newcome, Luis Gall, Dav
 id Martin, Markus Mühlhäußer, and Hans-Joachim Bungartz (Technical Univers
 ity of Munich)\n---------------------\nP26 - Optimizing the ICON Dynamical
  Core for GPUs Utilizing GT4Py and DaCe\n\nNumerical weather predictions a
 re based on a numerical model running on a large super computer. Improving
  the performance of these models is an active field of research which bene
 fits society. The ICON model is a finite volume model running on an icosah
 edral mesh.\nFinite volume stencil computations ...\n\n\nChristoph Müller 
 (MeteoSwiss) and Magdalena Luz, Nicoletta Farabullini, Till Ehrengruber, C
 hia Rui Ong, Daniel Hupp, Philip Müller, Edoardo Paone, Ioannis Magkanaris
 , Christos Kotsalos, Yilu Chen, Jacopo Canton, Hannes Vogt, Enrique Gonzál
 ez Paredes, Rico Häuselmann, Anurag Dipankar, Mauro Bianco, William Sawyer
 , and Mikael Simberg (ETH Zurich / CSCS)\n---------------------\nP27 - Par
 allel Tempering on Boundary Conditions with Normalizing Flows to Solve Top
 ological Freezing\n\nIn particle physics, Lattice Quantum Chromodynamics (
 LQCD) studies the strong interaction, responsible, for example, for the bi
 nding of atomic nuclei, through computational methods.\nAn essential part 
 of LQCD consists on being able to sample high-dimensional multi-modal dist
 ributions, for which direc...\n\n\nVictor Granados (University of Bern)\n\
 nSession Chair: Miroslava Nedyalkova (University of Fribourg)
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