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DTSTART;TZID=Europe/Stockholm:20260630T173000
DTEND;TZID=Europe/Stockholm:20260630T194500
UID:submissions.pasc-conference.org_PASC26_sess135@linklings.com
SUMMARY:Poster Session and Reception
DESCRIPTION:ACMP02 - Data Augmentation to Improve the Performance of Deep 
 Learning-Based Seismic Inversion\n\nDeep Learning-based Seismic Inversion 
 (DLI) is a promising alternative to Full Waveform Inversion (FWI), but str
 uggles with data scarcity. This work evaluates data augmentation technique
 s adapted from computer vision to address this bottleneck, and demonstrate
 s that generating synthetic earth models...\n\n\nCarlos Gomes de Carvalho 
 Junior (Universidade Federal de São Carlos)\n---------------------\nP12 - 
 Evaluating Open-Source Infrastructure-As-Code Virtual Clusters against Sup
 erMUC-NG Phase 1\n\nTraditional high-performance computing (tHPC) infrastr
 ucture requires weeks to months for hardware procurement, network configur
 ation and software integration, which limits agility for short-term projec
 ts and hampers reproducibility through non-standardized configurations. In
 frastructure-as-Code (Ia...\n\n\nPrasanth Babu Ganta, Elmira Birang, Plame
 n Dobrev, Birkan Emrem, Matteo Foglieni, and Ferdinand Jamitzky (Leibniz S
 upercomputing Centre)\n---------------------\nP20 - A High-Performance, GP
 GPU-Enabled Discontinuous Galërkin Solver Using OpenMP Offloading and MPI\
 n\nWe present a GPGPU-enabled modal Discontinuous Galërkin solver that use
 s OpenMP+MPI. Device code is generated by offloading OpenMP pragmas, and i
 nter-device/inter-node communication is enabled by MPI.\nOur test case imp
 lements a diffusion-advection solver with a Runge-Kutta-Chebyshev time ste
 pping sc...\n\n\nMarco Scarpelli, Paola Francesca Antonietti, Carlo De Fal
 co, and Luca Formaggia (Politecnico di Milano) and Giovanni Viciconte (ENI
  S.p.A.)\n---------------------\nP35 - Shedding Light on the Solar Dynamo 
 Using Bayesian Data Science\n\nSolar magnetic activity exhibits an approxi
 mately 11-year cycle that is far from stable, showing strong long-term var
 iability and recurrent episodes of strongly reduced activity known as Gran
 d Minima. Understanding the origin of these features remains a fundamental
  challenge in solar physics and is ...\n\n\nSimone Ulzega (Zurich Universi
 ty of Applied Sciences, Institute of Computational Life Sciences) and Carl
 o Albert (Swiss Federal Institute of Aquatic Science and Technology (EAWAG
 ))\n---------------------\nP07 - Correlated Electrons on Accelerated Archi
 tectures from Frequency-Dependent Response Functions\n\nUnderstanding, cha
 racterizing 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 ex
 ternal stimuli, and while important in general, they are eve...\n\n\nPaolo
  Settembri and Nicola Colonna (Paul Scherrer Institute); Anton Kozhevnikov
  (ETH Zurich / CSCS); and Nicola Marzari (EPFL, Paul Scherrer Institute)\n
 ---------------------\nP18 - GPU-Accelerated Methods for Numerically Stabl
 e Resampling in Fluid-Structure Interaction\n\nFluid-structure interaction
  simulations require accurate transfer of scalar fields between overlappin
 g meshes with different topologies. We address the problem of transferring
  fields from unstructured tetrahedral to structured hexahedral meshes.\n\n
 This problem is challenging because direct quadrature...\n\n\nSimone Riva 
 (Università della Svizzera italiana) and Patrick Zulian (UniDistance Suiss
 e, Università della Svizzera italiana)\n---------------------\nP05 - Bridg
 ing Python Flexibility and GPU Performance with Aithon: Kernel-Level Optim
 ization, Scaling, and Extreme-Resolution MHD Turbulence Simulations\n\nWe 
 present Aithon, a GPU-accelerated incompressible flow solver for hydrodyna
 mics and magnetohydrodynamics, designed for extreme-scale supercomputing. 
 Optimized for AMD MI250X GPUs and deployed on the Frontier system, Aithon 
 combines kernel-level GPU optimizations, CUDA/HIP-aware MPI, and Python in
 t...\n\n\nManthan Verma (Indian Institute of Technology kanpur) and Gina S
 itaraman and Paul Mullowney (AMD)\n---------------------\nP24 - A Machine 
 Learning Framework for CFD Applications\n\nIn the present study, an automa
 ted framework is prepared that contains two modules, Computational Fluid D
 ynamics (CFD) simulations and surrogate modelling. CFD simulations are per
 formed to model and make thermal assessment of battery air cooling in diff
 erent air stream conditions (i.e. stream veloci...\n\n\nMasumeh Gholamishe
 eri, Harry Durnberger, Tim Powell, and Jony Castagna (STFC)\n-------------
 --------\nP21 - Hybrid Block-Structured Grids for Coastal Ocean Domains\n\
 nAchieving high performance and performance portability is critical for ne
 xt-generation climate and ocean modelling on heterogeneous computing syste
 ms. Ocean models face complex, fractal-like coastlines and rapidly varying
  bathymetry, making unstructured triangular meshes attractive for their fl
 exibi...\n\n\nJonathan Schmalfuß and Vadym Aizinger (University of Bayreut
 h)\n---------------------\nP01 - Accelerating Lattice QCD Dirac GCR Solver
 s with Multiple Right-Hand Sides (MRHS)\n\nLattice QCD simulations are oft
 en limited by the memory-bandwidth bottlenecks of solving the Dirac equati
 on for numerous source vectors. We present an optimised Multiple Right-Han
 d Side (MRHS) Generalised Conjugate Residual (GCR) solver in the openQxD f
 ramework that addresses these limitations. By t...\n\n\nJingJing Li and Ro
 man Gruber (University of Bern) and Marina Krstic Marinkovic (ETH Zurich)\
 n---------------------\nP37 - Structured Reinforcement Learning for Loop T
 ransformation in MLIR\n\nOptimizing polyhedral kernels for modern multicor
 e architectures is a high-dimensional, non-convex problem where small stru
 ctural changes often yield orders-of-magnitude runtime variation. While tr
 aditional compilers rely on rigid static heuristics and autotuners require
  prohibitive search times, th...\n\n\nAbrar Hossain (University of Toledo)
 \n---------------------\nP03 - Algebraic Multi-Level Methods for Lattice D
 irac Operators in LQCD\n\nThe main computational challenge in Lattice QCD 
 is the efficient and scalable approximate solution of the Dirac equation D
 z = b, where D denotes the Dirac matrix on a four-dimensional space-time l
 attice. Modern solvers for this case are based on Adaptive Multigrid. Amon
 g them, Domain Decomposition A...\n\n\nPauline Schauerte (University of Bo
 nn, Fraunhofer SCAI) and Jaime Fabian Nieto Castellanos (Forschungszentrum
  Jülich, University of Bonn)\n---------------------\nP08 - Coupling km-Sca
 le Earth System Model to Hierarchical Output for Analysis-Ready Dataset\n\
 nKilometer-scale Earth System Model (ESM) simulations produce petabyte-sca
 le outputs that are difficult to access, analyse, and share due to their s
 ize, heterogeneity, and the overhead of ad-hoc workflows.\nWe introduce **
 Hiopy** (Hierarchical Output in Python), a lightweight in-situ output comp
 onent,...\n\n\nNils-Arne Dreier (DKRZ) and Siddhant Tibrewal (Max Planck I
 nstitute for Meteorology)\n---------------------\nP23 - An Integrated HPC 
 Workflow for AI-Driven Immunogenic Peptide Prediction\n\nImmunogenic pepti
 des play important roles as drivers for the adaptive immune response - our
  bodies' ultimate protection against infections and cancers. Parts of thes
 e peptides, called epitopes, are recognized by either major histocompatibi
 lity complexes or antibodies, which then interact with T-cell...\n\n\nCath
 rine Bergh (KTH Royal Institute of Technology), Leonardo Salicari (CINECA)
 , Danai Kotzampasi and Victor Reys (Utrecht University), Narendra Kumar (N
 ational Institute of Immunology), Archana Achalere and Sunitha Manjari Kas
 ibhatla (Center for the Development of Advanced Computing), Alessandra Vil
 la (KTH Royal Institute of Technology), Uddhavesh Sonavane (Center for the
  Development of Advanced Computing), and Alexandre Bonvin (Utrecht Univers
 ity)\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 structur
 e of a protein has been a long standing and extremely challenging problem 
 in biology. In recent years, the rapid evolution and adoption of artificia
 l intelligence have made the prediction of protein structures leveraging d
 eep learning frameworks with accuracy rivali...\n\n\nVerónica G. Melesse V
 ergara, Elijah MacCarthy, Asim YarKhan, John Holmen, Manesh Shah, Érica Te
 ixeira Prates, and Dan Jacobson (Oak Ridge National Laboratory)\n---------
 ------------\nACMP05 - Hypergraph Partitioning for Sparse Matrix Reorderin
 g\n\nFill-in during sparse matrix factorization remains a critical bottlen
 eck in scientific computing. We present an efficient hypergraph partitioni
 ng approach for sparse matrix reordering based on the Clique-Node Hypergra
 ph (CNH) representation, building on prior work by Çatalyurek et al. and S
 elvitopi ...\n\n\nRitvik Ranjan (ETH Zurich)\n---------------------\nP26 -
  Optimizing the ICON Dynamical Core for GPUs Utilizing GT4Py and DaCe\n\nN
 umerical weather predictions are based on a numerical model running on a l
 arge super computer. Improving the performance of these models is an activ
 e field of research which benefits society. The ICON model is a finite vol
 ume model running on an icosahedral mesh.\nFinite volume stencil computati
 ons ...\n\n\nChristoph Müller (MeteoSwiss) and Magdalena Luz, Nicoletta Fa
 rabullini, Till Ehrengruber, Chia Rui Ong, Daniel Hupp, Philip Müller, Edo
 ardo Paone, Ioannis Magkanaris, Christos Kotsalos, Yilu Chen, Jacopo Canto
 n, Hannes Vogt, Enrique González Paredes, Rico Häuselmann, Anurag Dipankar
 , Mauro Bianco, William Sawyer, and Mikael Simberg (ETH Zurich / CSCS)\n--
 -------------------\nP17 - Generalization of Long-Range Machine Learning P
 otentials in Complex Chemical Spaces\n\nThe vastness of chemical space mak
 es generalization a fundamental challenge for machine learning interatomic
  potentials (MLIPs). Although MLIPs enable near–quantum-accuracy atomistic
  simulations at greatly reduced computational cost, their practical reliab
 ility is often limited by poor transfe...\n\n\nMichał Sanocki (Technical U
 niversity of Munich)\n---------------------\nACMP10 - Mixed Precision Acce
 leration of Light Matter Dynamics: Enabling HPC Co-Design for Quantum Mate
 rial Discovery\n\nLight-matter dynamics in topological quantum materials h
 olds the promise for a sustainable society with ubiquitous and power-hungr
 y artificial intelligence (AI) by enabling ultralow-power (attojoule) and 
 ultrafast (petahertz) computing and sensing devices. A challenge is simula
 ting multiple field an...\n\n\nTaufeq Mohammed Razakh (University of South
 ern California)\n---------------------\nP30 - The Portable Model for Multi
 -Scale Atmospheric Prediction (PMAP): Towards Sub-Kilometer Scale and Larg
 e-Eddy Simulation of Real Weather\n\nThe Portable Model for multi-scale At
 mospheric Prediction (PMAP) is an advanced high-resolution numerical model
 . Written entirely in Python, it leverages the GT4Py domain-specific langu
 age to achieve high performance and portability – running straightforwardl
 y on laptops and GPU-accelerated HP...\n\n\nLukas Papritz and Nicolai Krie
 ger (ETH Zurich); Christian Kühnlein (ECMWF); Till Ehrengruber (ETH Zurich
  / CSCS); Sara Faghih-Naini (ECMWF); and Stefano Ubbiali, Gabriel Vollenwe
 ider, Heini Wernli, and Jan Zibell (ETH Zurich)\n---------------------\nP2
 9 - Perspectives on Teamwork and AI in Scientific Computing\n\nThe develop
 ment and use of high-quality software—a primary mechanism for sustained co
 llaboration and progress in scientific computing—is undergoing profound ch
 ange, driven by increasing complexity in scientific drivers and computing 
 architectures and the rapid adoption of AI, placing dem...\n\n\nOlivia B. 
 Newton (University of Montana), Anshu Dubey (Argonne National Laboratory),
  Denice Ward Hood (University of Illinois Urbana-Champaign), Lois Curfman 
 McInnes (Argonne National Laboratory), and Santiago Ospina Tabares (Univer
 sity of Illinois Urbana-Champaign)\n---------------------\nP14 - A Flexibl
 e Interface for Neural Network Potentials in GROMACS\n\nWe present a new i
 nterface for hybrid machine learning/molecular mechanics (ML/MM) simulatio
 ns implemented in the molecular dynamics engine GROMACS. The interface ena
 bles 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 Lind
 ahl (KTH Royal Institute of Technology, Stockholm University)\n-----------
 ----------\nP22 - Hypergraph Partitioning for Sparse Matrix Reordering\n\n
 Fill-in during sparse matrix factorization remains a critical bottleneck i
 n scientific computing. We present an efficient hypergraph partitioning ap
 proach for sparse matrix reordering based on the Clique-Node Hypergraph (C
 NH) representation, building on prior work by Çatalyürek et al. and Selvit
 opi ...\n\n\nRitvik Ranjan, Vincent Maillou, Alexandros Nikolaos Ziogas, a
 nd Mathieu Luisier (ETH Zurich)\n---------------------\nP28 - Performance-
 Portable and Highly Scalable Spectral Transforms with ecTrans\n\nThe conti
 nued increase in the skill of weather forecasts observed over the past dec
 ades depends crucially on the efficient exploitation of the next generatio
 n of high-performance computers by Earth-system models. The European Centr
 e for Medium-Range Weather Forecast (ECMWF)'s model, the IFS, is one ...\n
 \n\nSam Hatfield (ECMWF)\n---------------------\nP10 - Discretization Erro
 r Quantification in Plane-Wave Density Functional Theory\n\nDensity functi
 onal theory (DFT) has become a workhorse of computational materials scienc
 e. DFT computations in materials typically use a plane wave basis set, tru
 ncated at a so-called kinetic energy cutoff Ecut. Estimates for the trunca
 tion error of the basis set open opportunities for error balanci...\n\n\nB
 runo Ploumhans and Michael Herbst (EPFL)\n---------------------\nP16 - FPG
 A-Specific Optimizations for Multi-Device Shallow Water Simulations with S
 YCL\n\nThe shallow water equations are an essential tool for modeling tide
 s, 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 implement
 ation uses ...\n\n\nChristoph Alt (Paderborn University, Friedrich-Alexand
 er-Universität Erlangen-Nürnberg); Markus Büttner (University of Bayreuth)
 ; Tobias Kenter (Paderborn University); Harald Köstler (Friedrich-Alexande
 r-Universität Erlangen-Nürnberg); Christian Plessl (Paderborn University);
  and Vadym Aizinger (University of Bayreuth)\n---------------------\nACMP0
 7 - Mapping the Productivity-To-Energy Trade-Off in Memory-Bound HPC via D
 VFS, Core Scaling, and C-State Control on Repurposed Hardware\n\nHigh-perf
 ormance computing (HPC) systems in resource-constrained environments, such
  as Africa, often rely on repurposed hardware, shifting the primary financ
 ial burden from capital expenditure to operational energy costs. This work
  aims to identify realistic and reproducible performance–energy...\n\n\nSu
 né Toerien (University of the Witwatersrand), Vele Nefale (UniverUniversit
 y of the Witwatersrand), and Ntandoyenkosi Memela and Mubeen Dewan (Univer
 sity of the Witwatersrand)\n---------------------\nP33 - RDQ: A Zero-Copy 
 Remote Data Queue for In-Situ Machine Learning in HPC\n\nThe integration o
 f machine learning into the computational sciences is increasingly pursued
  to reduce time-to-solution, alleviate I/O bottlenecks, and enable adaptiv
 e analysis during simulation. We present RDQ (Remote Data Queue), a librar
 y for coupling HPC simulations and machine learning training ...\n\n\nMaxi
 milian Sander (TU Dresden) and Jens Domke (RIKEN)\n---------------------\n
 P04 - Algorithms and Optimizations for Global Non-Linear Hybrid Fluid-Kine
 tic Finite Element Stellarator Simulations\n\nPredictive modeling of stell
 arator plasmas is crucial for advancing nuclear fusion energy, yet it face
 s unique computational difficulties. A primary challenge is accurately sim
 ulating the dynamics of specific particle species not well captured by flu
 id models, necessitating the use of hybrid fluid-k...\n\n\nLuca Venerando 
 Greco and Matthias Hoelzl (Max Planck Institute for Plasma Physics); Guido
  Huijsmans (CEA, IRFM); and Edoardo Carrà (Max Planck Institute for Plasma
  Physics)\n---------------------\nACMP08 - Matrix-Free vs. Matrix-Based Fi
 nite Element Solvers for 3D Advection–Diffusion–Reaction Equations\n\nHigh
 -order finite element methods are attractive for three-dimensional advecti
 on–diffusion–reaction (ADR) problems, but their efficiency on distributed-
 memory systems is often limited by memory traffic and communication in lar
 ge linear solves. This work compares matrix-based (MAT) and ma...\n\n\nZha
 ohui Song (Politecnico di Milano)\n---------------------\nACMP04 - Fine-Tu
 ning Large Language Models for HPO-Term Recognition\n\nLarge language mode
 ls (LLMs) offer a promising approach for extracting structured medical inf
 ormation from free-text clinical notes. This work investigates fine-tuning
  LLMs for Human Phenotype Ontology (HPO) term recognition, a core task in 
 clinical genetics that requires accurate identification and...\n\n\nSrinit
 hi Krishnamoorthy (Cornell University)\n---------------------\nACMP13 - So
 lver-Integrated Lossy and Lossless Compression for Scalable Flow Simulatio
 ns\n\nLarge-scale computational fluid dynamics (CFD) simulations on modern
  GPU-accelerated supercomputers generate terabytes of data per run, making
  checkpointing, storage, and post-hoc analysis increasingly I/O-bound. Thi
 s bottleneck limits data retention and hinders downstream workflows such a
 s visualiz...\n\n\nViral Sudip Shah (University of Illinois Urbana-Champai
 gn)\n---------------------\nP27 - Parallel Tempering on Boundary Condition
 s with Normalizing Flows to Solve Topological Freezing\n\nIn particle phys
 ics, Lattice Quantum Chromodynamics (LQCD) studies the strong interaction,
  responsible, for example, for the binding of atomic nuclei, through compu
 tational methods.\nAn essential part of LQCD consists on being able to sam
 ple high-dimensional multi-modal distributions, for which direc...\n\n\nVi
 ctor Granados (University of Bern)\n---------------------\nP39 - Tuning th
 e Performance of Three-Body Interactions in Molecular Dynamics\n\nMolecula
 r Dynamics (MD) simulations predict thermophysical properties, yet standar
 d pair potentials can lack the desired accuracy for certain applications. 
 Introducing three-body potentials, such as the Axilrod-Teller-Muto model, 
 improves results but poses significant computational challenges. This ...\
 n\n\nMarkus Mühlhäußer, Samuel James Newcome, Fabio Gratl-Gaßner, Manish K
 umar Mishra, and Hans-Joachim Bungartz (Technical University of Munich)\n-
 --------------------\nP32 - Profile-Guided-Optimisation of Lattice QCD Con
 tractions on CPU and GPU\n\nWe present a performance optimisation study of
  the 2+2 disconnected component bottleneck of Lattice QCD computation of t
 he hadronic light-by-light contribution to the muon's anomalous magnetic m
 oment. Optimization on CPU and GPU architectures were guided by popular pr
 ofiling tools perf, valgrind, ns...\n\n\nJingJing Li, Urs Wenger, and Roma
 n Gruber (University of Bern)\n---------------------\nACMP12 - Semantic-Aw
 are Implicit Neural Compression for Physics Simulations\n\nMachine learnin
 g surrogates and data-driven scientific discovery require efficient access
  to simulation data, yet physics simulations generate terabyte-scale datas
 ets. Traditional compression either achieves insufficient ratios or corrup
 ts physics-critical features like conservation laws. Implicit n...\n\n\nJe
 ssica Ezemba (Carnegie Mellon University)\n---------------------\nP36 - St
 encil Computation on Tenstorrent Wormhole\n\nThe rapid ascent of large lan
 guage models (LLMs) has prioritized domain-specific accelerators (DSAs) op
 timized for dense matrix-based deep learning. However, the suitability of 
 these architectures for traditional high-performance computing (HPC) kerne
 ls, like stencil-based partial differential equat...\n\n\nLorenzo Piarulli
  and Daniele De Sensi (Sapienza University)\n---------------------\nP38 - 
 Tracking Mechanistic Evolution Across Brain Tissues and Cell Types Using M
 ultiplex Networks\n\nTracking the evolution of biological function remains
  a major challenge in computational biology, as existing approaches are of
 ten limited to sequence conservation, gene presence, or predefined pathway
 s. These methods can fail to identify conserved functional mechanisms even
  though constituent genes...\n\n\nKenneth Smith (Oak Ridge National Labora
 tory); Matthew Lane (University of Tennessee); Alice Townsend and Jean Mer
 let (Oak Ridge National Laboratory, University of Tennessee); Anna Vlot an
 d Alana Wells (Oak Ridge National Laboratory); and Daniel Jacobson (Oak Ri
 dge National Laboratory, University of Tennessee)\n---------------------\n
 P11 - Estimation of Global Surface Carbon Fluxes at the Grid Scale Using M
 achine Learning Techniques\n\nMachine learning (ML) techniques have recent
 ly been applied in the field of geoscience as in other fields, and has sho
 wn significant progress. One of the major advantages of ML is its remarkab
 le effectiveness in overcoming the problem of realistic computational cost
 s from a computational science per...\n\n\nJi-Sun Kang (Korea Institute of
  Science and Technology Information)\n---------------------\nP34 - Scaling
  Linear Algebra: Eigenvalue Solvers and Performance Trends on Contemporary
  HPC Systems\n\nLarge-scale eigenvalue problems are a fundamental componen
 t of modern scientific simulations in fields such as materials science, co
 mputational chemistry, and theoretical physics, where they often represent
  a dominant computational bottleneck. The efficiency and scalability of li
 near algebra and eig...\n\n\nMaria Montagna, Sergio Orlandini, and Fabio A
 ffinito (CINECA)\n---------------------\nP06 - Co-designing Regional High-
 Performance Computing Ecosystems in Africa: A Pilot Focus on Kenya and Wes
 t Africa\n\nHigh-performance computing is becoming more important for bioi
 nformatics, genomics, and public health research. However, in Africa, its 
 growth and use remain uneven, scattered, and poorly documented. This study
  examines current HPC capacity and access models in West, East, and Southe
 rn Africa, drawi...\n\n\nPauline Karega (University of Manchester, Bioinfo
 rmatics Hub of Kenya initiative)\n---------------------\nP25 - Maintainabl
 e, Sustainable, and Generalizable Data Layouts and Vectorization for Rigid
 -Body Molecular Dynamics\n\nls1-MarDyn (ls1) is an MD simulator designed f
 or large-scale simulations of multi-site molecules and has been successful
 ly used in a variety of scientific studies. It represents molecules as rig
 id bodies composed of multiple interaction sites that each exert forces on
  their neighbours, which are det...\n\n\nSamuel James Newcome, Luis Gall, 
 David Martin, Markus Mühlhäußer, and Hans-Joachim Bungartz (Technical Univ
 ersity of Munich)\n---------------------\nP40 - Uncertainty Quantification
  for Energy Efficiency Analysis of Scientific Applications at Exascale\n\n
 Energy efficiency has become a critical constraint in high-performance com
 puting (HPC) as systems scale toward larger node counts. In modern HPC pla
 tforms, energy consumption is influenced by complex interactions among har
 dware and software parameters, including operating frequencies, concurrenc
 y le...\n\n\nMatheus Machado, Mariana Costa, Matheus Costa, Philippe Navau
 x, and Arthur Lorenzon (UFRGS) and Antigoni Georgiadou and Bronson Messer 
 (Oak Ridge National Laboratory)\n---------------------\nACMP11 - Mosaic: A
 utomatic Categorization of I/O Patterns from Scientific Applications\n\nWh
 ile newly deployed High-Performance Computing (HPC) systems embeds more co
 mpute capabilities than the generations before, parallel file systems (PFS
 ) are struggling to keep up with this same trend, increasing the gap betwe
 en computing power and I/O performance. If new paradigms and technologies 
 ar...\n\n\nThéo Jolivel (INRIA, Universite de Rennes)\n-------------------
 --\nACMP03 - Deep Reinforcement Learning for Algorithm Selection in Molecu
 lar Dynamics Simulations\n\nShort-range particle simulations are crucial i
 n physics and chemistry, requiring efficient neighbor search and interacti
 on algorithms. The C++ library AutoPas supports more than 100 algorithms, 
 but no single algorithm is universally optimal. Traditional manual tuning 
 is impractical due to dynamic si...\n\n\nPatrick Metscher (Technische Univ
 ersität München)\n---------------------\nP19 - Graph Neural Network Potent
 ials for Million-Atom Molecular Dynamics Simulations of Aluminum Solidific
 ation\n\nSolidification is ubiquitous in the fabrication of metal parts. M
 olecular dynamics simulations can predict the microstructure and the corre
 sponding mechanical properties. However, both high accuracy of interatomic
  potential energy and scalability to millions of atoms are required to cap
 ture physical...\n\n\nIan Störmer and Julija Zavadlav (Technical Universit
 y of Munich)\n---------------------\nACMP01 - Adaptive Multidimensional Qu
 adrature on Multi-GPU Systems\n\nThe present work introduces a distributed
  adaptive deterministic quadrature framework for high-dimensional integrat
 ion on multi-GPU systems, enabling accurate evaluation of integrals arisin
 g in applications such as radiative transfer and probabilistic design. The
  method is formulated as a hierarchic...\n\n\nMelanie Tonarelli (Universit
 à della Svizzera Italiana)\n---------------------\nP31 - Predictive Alerts
  and Atmospheric Data for Airport Windshear by CPAS 200m Weather Model\n\n
 Low-level wind shear (LLWS) at Hong Kong International Airport (HKIA) pose
 s significant aviation risks, primarily driven by terrain-induced turbulen
 ce from Lantau Island and sea-breeze interactions. While current mitigatio
 n relies on real-time detection and short-term forecasting, for operationa
 l pu...\n\n\nSai Lun Tin, Chi Chiu Cheung, Ka Ki Ng, and Wai Pang Sze (Clu
 sterTech Limited)\n---------------------\nACMP09 - Medulla: Cluster- and A
 pplication-level I/O Performance Diagnosis with LLMs\n\nModern High-Perfor
 mance Computing (HPC) I/O systems offer immense bandwidth but are notoriou
 sly difficult to utilize effectively.\nIdentifying critical bottlenecks is
  a challenge that persists for both\ndomain scientists and cluster adminis
 trators. Existing analysis tools\ncan identify common bottlenec...\n\n\nAn
 ish Sathyanarayanan (BITS Pilani K K Birla Goa Campus)\n------------------
 ---\nP02 - Advancing The Data Assimilation Research Testbed (DART) as an E
 arly-Career Software Engineer\n\nThe Data Assimilation Research Testbed (D
 ART) is an open-source software facility for ensemble data assimilation th
 at combines information from numerical model predictions with measurements
  of the Earth system to enhance the value of both. It has supported a dive
 rse community of users for over 20 ye...\n\n\nMarlena Smith (NSF National 
 Center for Atmospheric Research)\n---------------------\nP41 - Using Gener
 ative Machine Learning to Produce High-Resolution Weather Data\n\nGenerati
 ve  machine learning techniques show promise for performing atmospheric do
 wnscaling (super-resolution for meteorological data) to produce high-resol
 ution weather and climate simulations. Previous work has evaluated the qua
 lity of these models using standard error scores such as RMSE, absolut...\
 n\n\nPetar Stamenkovic and Mary McGlohon (MeteoSwiss, ETH Zurich); David L
 eutwyler and Xavier Lapillonne (MeteoSwiss); Fabian Bösch, Lukas Drescher,
  and Henrique Mendonça (ETH Zurich / CSCS); Sebastian Schemm (University o
 f Cambridge); Siddhartha Mishra (ETH Zurich); and Oliver Fuhrer (MeteoSwis
 s)\n---------------------\nACMP06 - Improving Deep Learning Based Seismic 
 Inversion with Online Augmentation\n\nThis study investigates the applicat
 ion of data augmentation techniques to optimize Deep Learning-based Seismi
 c Inversion (DLI), aiming to overcome the scarcity of labeled datasets in 
 the industry. Using the OpenFWI benchmark, the study evaluates four increm
 ental strategies: horizontal flipping, syn...\n\n\nLucas Souza (Federal Un
 iversity of São Carlos)\n---------------------\nP09 - Developing and Evalu
 ating Performance-Portable Physical Parametrization Codes\n\nWe present re
 sults and ongoing work in the porting of physical parametrizations to Pyth
 on using the GridTools for Python (GT4Py) library. Our basis is the Fortra
 n code from the Integrated Forecasting System (IFS) which is run operation
 ally at the European Centre for Medium-Range Weather Forecasts (E...\n\n\n
 Gabriel Vollenweider and Stefano Ubbiali (ETH Zurich), Christian Kühnlein 
 (ECMWF), and Heini Wernli (ETH Zurich)\n---------------------\nACMP14 - St
 ability and Accuracy of the r²SCAN Functional for Group-IV Elemental Solid
 s in a Cost-Aware Workflow Perspective\n\nDensity-functional theory (DFT) 
 is a central tool in computational materials science, and its predictive p
 ower depends critically on the choice of exchange-correlation functional. 
 Here we benchmark the meta-GGA r²SCAN in a study of the group-IV elemental
  solids C, Si, Ge, and Sn, focusing on stabili...\n\n\nAdonis Haxhijaj (EP
 FL, ETH Zurich)\n---------------------\nP15 - A Flux-Form Semi-Lagrangian 
 WENO Scheme on Triangular Meshes\n\nThe icosahedral model for weather and 
 climate simulations utilises flux-form semi-Lagrangian (FFSL) schemes for 
 the transport of species. The motivation is the higher Courant-Friedrich-L
 ewy (CFL) number compared to Eulerian approaches. The schemes are implemen
 ted on the triangular mesh on a sphere w...\n\n\nAndreas Jocksch (ETH Zuri
 ch / CSCS); Daniel Reinert (Deutscher Wetterdienst (DWD)); Christoph Mülle
 r (MeteoSwiss); David Strassmann (ETH Zurich); Nina Burgdorfer (MeteoSwiss
 ); Anurag Dipankar (ETH Zurich); Mauro Bianco (ETH Zurich / CSCS); and Tho
 mas Schulthess (ETH Zurich, ETH Zurich / CSCS)
END:VEVENT
END:VCALENDAR