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UID:submissions.pasc-conference.org_PASC23_sess116@linklings.com
SUMMARY:Poster Session and Reception
DESCRIPTION:Poster\n\nP43 - Multilevel and Domain-Decomposition Solution S
 trategies for Solving Large-Scale Phase-Field Fracture Problems\n\nThe pha
 se-field approach for fracture propagation is a state-of-the-art technique
  for simulating crack initiation, propagation, and coalescence. In this ap
 proach, a damage field, called the phase field, is introduced that charact
 erizes the material state from intact to fully broken. Even though the ...
 \n\n\nHardik Kothari (Università della Svizzera italiana); Alena Kopanicak
 ova (Brown University, Università della Svizzera italiana); Patrick Zulian
  (Università della Svizzera italiana, UniDistance Suisse); Maria Nestola (
 Università della Svizzera italiana); Edoardo Pezzulli and Thomas Driesner 
 (ETH Zurich); and Rolf Krause (Università della Svizzera italiana, UniDist
 ance Suisse)\n---------------------\nP05 - Ab Initio Modeling of Magnetite
  Surfaces for Plutonium Retention\n\nIn many countries, thick steel casks 
 are used for the containment of high-level radioactive waste in deep geolo
 gical repositories. In contact with pore-water, steel corrodes forming mix
 ed iron oxides, mainly magnetite at the surface. After tens of thousands o
 f years, casks may breach allowing leachi...\n\n\nAnita S. Katheras and Ko
 nstantinos Karalis (University of Bern); Matthias Krack (Paul Scherrer Ins
 titute); Andreas C. Scheinost (Helmholtz-Zentrum Dresden-Rossendorf); and 
 Sergey V. Churakov (Paul Scherrer Institute, University of Bern)\n--------
 -------------\nP38 - Loki v0.1.1: A Source-To-Source Translation Tool for 
 Numerical Weather Prediction Codes and More\n\nAll known or presumed candi
 dates for exascale supercomputers will feature novel computing hardware or
  heterogeneous architectures, with GPUs currently being a cornerstone of t
 his development. Using these machines efficiently with today's numerical w
 eather prediction (NWP) codes requires adapting lar...\n\n\nMichael Stanek
 er, Ahmad Nawab, Balthasar Reuter, and Michael Lange (ECMWF)\n------------
 ---------\nP56 - The P4est Software for Parallel AMR: A Shared Memory Work
 flow\n\nParallel adaptive mesh refinement (AMR) is a key technique when si
 mulations are required to capture time-dependent and/or multiscale feature
 s. A forest of octrees is a data structure to represent the recursive adap
 tive refinement of an initial, conforming coarse mesh of hexahedra. This p
 oster presen...\n\n\nMikhail Kirilin and Carsten Burstedde (University of 
 Bonn)\n---------------------\nP39 - Mapping a Coupled Earth-System Simulat
 or onto the Modular Supercomputer Architecture\n\nThe Modular Supercompute
 r Architecture concept, developed for the DEEP project series, describes a
  novel kind of heterogeneous computing platform comprising several differe
 nt “modules”, each of which is a separate compute cluster in its own right
 . The modules are connected with a federat...\n\n\nSamuel Hatfield, Olivie
 r Marsden, Kristian Mogensen, and Ioan Hadade (ECMWF)\n-------------------
 --\nP33 - ICON-GPU for Numerical Weather Prediction – A Status Report\n\nW
 eather prediction centers are always seeking ways to improve the computati
 onal performance of their numerical weather prediction (NWP) models while 
 staying within budget. The era of ever improving scalar CPU has come to an
  end but massively multiprocessing GPUs are advertised as the next step fo
 rwa...\n\n\nMarek Jacob (DWD); Dmitry Alexeev (NVIDIA Inc.); Daniel Hupp a
 nd Xavier Lapillonne (MeteoSwiss); and Florian Prill, Daniel Reinert, and 
 Günther Zängl (DWD)\n---------------------\nP49 - Simulating Aquaplanet Us
 ing ICON with a GT4Py DSL Dynamical Core\n\nWe present the results of our 
 efforts porting the dynamical core of the ICON climate and numerical weath
 er prediction (NWP) model to GT4Py. GT4Py is a Domain Specific Language (D
 SL) designed for weather and climate applications, which allows domain sci
 entists to write performance portable climate an...\n\n\nChristoph Müller 
 (MeteoSwiss); Abishek Gopal (ETH Zurich / CSCS); Nicoletta Farabullini (ET
 H Zurich); Till Ehrengruber (ETH Zurich / CSCS); Samuel Kellerhals, Peter 
 Kardos, and Magdalena Luz (ETH Zurich); Matthias Röthlin (MeteoSwiss); Enr
 ique G. Paredes (ETH Zurich / CSCS); David Leutwyler and Benjamin Weber (M
 eteoSwiss); Rico Häuselmann and Felix Thaler (ETH Zurich / CSCS); Jonas Ju
 cker (ETH Zurich); Linus Groner, Hannes Vogt, and Mauro Bianco (ETH Zurich
  / CSCS); Anurag Dipankar (ETH Zurich); and Carlos Osuna and Xavier Lapill
 onne (MeteoSwiss)\n---------------------\nP13 - Calculation of the Maximal
 ly Localized Wannier Functions in the SIRIUS Library\n\nElectronic propert
 ies of the materials are one of the major line of research for studying ex
 isting and discovering novel materials. DFT+U and Koopman spectral functio
 nals constitute a good approach for correcting the DFT band structure, whi
 ch is usually not good for the prediction of some of the pro...\n\n\nGiova
 nni Consalvo Cistaro (EPFL), Nicola Colonna (Paul Scherrer Institute), Iur
 ii Timrov (EPFL), Anton Kozhevnikov (ETH Zurich / CSCS), and Nicola Marzar
 i (EPFL)\n---------------------\nP23 - Evaluation of GPU Accelerated Machi
 ne Learning Algorithms for Energy Price Prediction\n\nThe Locational Margi
 nal Pricing (LMP) mechanism is a way to calculate the cost of providing el
 ectricity to a specific point in the grid. Accurate forecasting of LMP is 
 important for market participants such as power producers or financial ins
 titutions to optimize operations and bidding strategies. T...\n\n\nNaga Ve
 nkata Sai Jitin Jami (Università della Svizzera italiana, Friedrich-Alexan
 der-Universität Erlangen-Nürnberg); Juraj Kardos and Olaf Schenk (Universi
 tà della Svizzera italiana); and Harald Köstler (Friedrich-Alexander-Unive
 rsität Erlangen-Nürnberg)\n---------------------\nP46 - Parallel Second Or
 der Conservative Remapping on the Sphere\n\nWe present an MPI-parallel imp
 lementation and analysis of a conservative second-order interpolation meth
 od between arbitrary very high resolution spherical meshes supported by th
 e Atlas library of ECMWF. Hence, meshes are those used by ECMWF’s IFS mode
 l: structured grids such as the reduced Ga...\n\n\nSlavko Brdar, Willem De
 coninck, Pedro Maciel, and Michail Diamantakis (ECMWF)\n------------------
 ---\nP53 - Ultra-High Resolution Simulations of Planetary Collisions\n\nGi
 ant impacts (GI) form the last stage of planet formation and play a key ro
 le in determining many aspects like the final structure of planetary syste
 ms and the masses and compositions of its constituents. A common choice fo
 r numerically solving the equations of motion is the Smoothed Particle Hyd
 ro...\n\n\nThomas Meier, Christian Reinhardt, Douglas Potter, and Joachim 
 Stadel (University of Zurich)\n---------------------\nP50 - Towards a GPU-
 Enabled Linear-Response Algorithm in the SIRIUS Library\n\nElectronic-stru
 cture approaches have become integral in materials science, physics and ch
 emistry for studying existing and designing and discovering novel material
 s. Among the properties that can be studied, spectral properties of materi
 als provide a wealth of information, and can be obtained from K...\n\n\nGi
 annis D. Savva and Iurii Timrov (EPFL), Nicola Colonna (Paul Scherrer Inst
 itute), Anton Kozhevnikov (ETH Zurich / CSCS), and Nicola Marzari (EPFL)\n
 ---------------------\nP10 - Application of Deep Learning and Reinforcemen
 t Learning to Boundary Control Problems\n\nMany scientific problems, such 
 as fluid dynamics problems involving drag reduction, temperature control w
 ith some desired flow pattern, etc., rely on optimal boundary control algo
 rithms. These forward solves are performed for multiple simulation timeste
 ps, and hence, a method to solve the boundary c...\n\n\nZenin Easa Panthak
 kalakath and Juraj Kardoš (Università della Svizzera italiana) and Olaf Sc
 henk (Università della Svizzera italiana, ETH Zurich)\n-------------------
 --\nP55 - Novel Geometric Deep Learning Surrogate Framework for Non-Linear
  Finite Element Simulations\n\nConventional numerical methods are computat
 ionally expensive in simulating non-linear phenomena arising in mechanics.
  In this aspect, deep learning (DL) techniques are being increasingly used
  for accelerating simulations in mechanics. However, existing DL methods p
 erform inefficiently as the size an...\n\n\nSaurabh Deshpande (University 
 of Luxembourg); Jakub Lengiewicz (University of Luxembourg, IPPT PAN); and
  Stéphane Bordas (University of Luxembourg)\n---------------------\nP37 - 
 LIBRSB: Multicore Sparse Matrix Performance Across Languages and Architect
 ures\n\nLIBRSB (http://librsb.sf.net/) is a highly interoperable multicore
  CPU-oriented library for sparse matrix computations.<br />It serves as a 
 component in sparse linear solvers. LIBRSB builds upon its "RSB" hierarchi
 cal and coarse-grained sparse matrices storage. The RSB data structure and
  algorithms ...\n\n\nMichele Martone (Leibniz Supercomputing Centre)\n----
 -----------------\nP29 - GT4Py: A Python Framework for the Development of 
 High-Performance Weather and Climate Applications\n\nGT4Py is a Python fra
 mework for weather and climate applications simplifying the development an
 d maintenance of high-performance codes in prototyping and production envi
 ronments. GT4Py separates model development from hardware architecture dep
 endent optimizations, instead of intermixing both togethe...\n\n\nMauro Bi
 anco and Till Ehrengruber (ETH Zurich / CSCS); Nicoletta Farabullini and A
 bishek Gopal (ETH Zurich); Linus Groner and Rico Häuselmann (ETH Zurich / 
 CSCS); Peter Kardos, Samuel Kellerhals, and Magdalena Luz (ETH Zurich); Ch
 ristoph Müller (MeteoSwiss); Enrique G. Paredes (ETH Zurich / CSCS); Matth
 ias Roethlin (MeteoSwiss); Felix Thaler and Hannes Vogt (ETH Zurich / CSCS
 ); Benjamin Weber (MeteoSwiss); and Thomas C. Schulthess (ETH Zurich / CSC
 S)\n---------------------\nP08 - Analysis and Application of CNN to Improv
 e Deterministic Optical Flow Nowcasting at DWD\n\nOptical flow based nowca
 sting is essential for several operational productions at DWD, including t
 ime critical warnings. Precipitation and radar reflectivity nowcasts are p
 roduced every 5 minutes with a 5 minute stepping up to 2h lead time. The m
 ethod assumes stationarity of the input data. It is a ...\n\n\nUlrich Frie
 drich (DWD)\n---------------------\nP52 - Tunable and Portable Extreme-Sca
 le Drug Discovery Platform at Exascale: the LIGATE Approach\n\nToday digit
 al revolution is having a dramatic impact on the pharmaceutical industry a
 nd the entire healthcare system. The implementation of machine learning, e
 xtreme-scale computer simulations, and big data analytics in the drug desi
 gn and development process offers an excellent opportunity to lower...\n\n
 \nAndrea Beccari (Dompé farmaceutici), Silvano Coletti (Chelonia SA), Biag
 io Cosenza (Università di Salerno), Andrew Emerson (CINECA), Thomas Fahrin
 ger (University of Innsbruck), Daniele Gregori (E4 Engineering), Philipp G
 schwandtner (UIBK), Erik Lindahl (KTH Royal Institute of Technology), Jan 
 Martinovic (IT4Innovations National Supercomputing Center), Gianluca Paler
 mo (Politecnico di Milano), and Torsten Schwede (University of Basel)\n---
 ------------------\nP51 - Towards a Python-Based Performance-Portable Fini
 te-Volume Dynamical Core for Numerical Weather Prediction\n\nWe present re
 cent progress in the development of a high-performance Python implementati
 on of the FVM non-hydrostatic dynamical core at ECMWF and its member state
  partners. The FVM numerical formulation centred about 3D semi-implicit ti
 me integration of the fully compressible equations with finite-vo...\n\n\n
 Stefano Ubbiali (ETH Zurich), Till Ehrengruber (ETH Zurich / CSCS), Nicola
 i Krieger (ETH Zurich), Christian Kühnlein (ECMWF), and Lukas Papritz and 
 Heini Wernli (ETH Zurich)\n---------------------\nP12 - Building a Physics
 -Constrained, Fast and Stable Machine Learning-Based Radiation Emulator\n\
 nModeling the transfer of radiation through the atmosphere is a key compon
 ent of weather and climate models. The operational radiation scheme in the
  Icosahedral Nonhydrostatic Weather and Climate Model (ICON) is ecRad. The
  radiation scheme ecRad is accurate but computationally expensive. It is o
 perat...\n\n\nGuillaume Bertoli and Sebastian Schemm (ETH Zurich) and Fira
 t Ozdemir, Fernando Perez Cruz, and Eniko Szekely (Swiss data science cent
 er)\n---------------------\nP35 - Investigating the Mechanism of a Local W
 indstorm in the Swiss Alps Using Large-Eddy Simulations\n\nThe Laseyer win
 dstorm is a local and strong wind phenomenon in the narrow Schwende valley
  in northeastern Switzerland. The phenomenon has raised the interest of me
 teorologists as it has - in the past - led to derailments of the local tra
 in. It is characterised by easterly to southeasterly winds duri...\n\n\nNi
 colai Krieger (ETH Zurich), Christian Kühnlein (ECMWF), and Michael Spreng
 er and Heini Wernli (ETH Zurich)\n---------------------\nP44 - Numerical S
 imulation of Gradual Compaction of Granular Materials and the Uncertainty 
 Quantification of the Proposed Mathematical Model\n\nThe poster deals with
  mathematical modelling of granular materials and focuses on the process o
 f their gradual compaction called ratchetting. The model of hypoplasticity
  introduced by E. Bauer et al. is investigated and the problem of stress-c
 ontrolled hypoplasticity is considered. The behaviour of ...\n\n\nJudita R
 uncziková and Jan Chleboun (Czech Technical University in Prague)\n-------
 --------------\nP06 - Accurate Electronic Properties and Intercalation Vol
 tages of Li-Ion Cathode Materials from Extended Hubbard Functionals\n\nThe
  design of novel cathode materials for Li-ion batteries requires accurate 
 first-principles predictions of their properties. Density-functional theor
 y (DFT) with standard (semi-)local functionals fails due to the strong sel
 f-interaction errors of partially filled d shells of transition-metal (TM)
  ...\n\n\nIurii Timrov, Francesco Aquilante, and Michele Kotiuga (EPFL); M
 atteo Cococcioni (University of Pavia); and Nicola Marzari (EPFL)\n-------
 --------------\nP32 - High-Throughput Computational Screening of Fast Li-I
 on Conductors\n\nWe present a high-throughput computational screening to f
 ind fast Li-ion conductors to identify promising candidate materials for a
 pplication in solid-state electrolytes. Starting with ~30,000 experimental
  structures sourced from COD, ICSD and MPDS repositories, we performed hig
 hly automated calcula...\n\n\nTushar Thakur, Loris Ercole, and Nicola Marz
 ari (EPFL)\n---------------------\nP57 - Partial Charge Prediction and Pat
 tern Extraction from a AttentiveFP Graph Neural Network\n\nMolecular dynam
 ics (MD) simulations enable the time-resolved study of bio-molecular proce
 sses. The quality of MD simulations is, however, highly dependent on the s
 et of interaction parameters used, so-called force fields. The accurate pa
 rtial-charge assignment of all simulated atoms is hence a cruci...\n\n\nMa
 rc Thierry Lehner (ETH Zurich)\n---------------------\nP21 - Doppler-Boost
 ed Lasers: A New Path to Extreme QED Pair Plasmas in Light-Matter and Ligh
 t-Quantum Vacuum Interactions\n\nHow does light interact with matter or th
 e quantum vacuum at intensities where the physics is governed by Quantum E
 lectrodynamics (QED)? What are the properties of the QED electron-positron
  pair plasma produced in those interactions? Can the probing of this plasm
 a help address open problems in quant...\n\n\nHenri Vincenti, Luca Fedeli,
  Neil Zaim, Antonin Sainte-Marie, Pierre Bartoli, and Thomas Clark (CEA) a
 nd Jean-Luc Vay and Axel Huebl (Lawrence Berkeley National Laboratory)\n--
 -------------------\nP27 - GPU-Accelerated Modelling of Greenhouse Gases a
 nd Air Pollutants in ICON with OpenACC\n\nReleasing excess greenhouse gase
 s into the atmosphere is the major cause of its natural composition altern
 ation and climate change. Computational modelling of the atmospheric chemi
 stry and transport processes has played a vital role in enhancing our unde
 rstanding of such complex phenomena and helped...\n\n\nArash Hamzehloo and
  Dominik Brunner (Empa)\n---------------------\nP45 - Optimization of Non-
 Conventional Airfoils for Martian Rotorcraft with Direct Numerical Simulat
 ions Using High-Performance Computing\n\nDesign of rotorcraft for Mars is 
 challenging due to the very low density and low speed of sound compared to
  Earth. These conditions require Martian rotor blades to operate in a low-
 Reynolds-number (1,000 to 10,000 based on chord) compressible flow regime,
  atypical of conventional, terrestrial helico...\n\n\nLidia Caros, Oliver 
 Buxton, and Peter Vincent (Imperial College London)\n---------------------
 \nP04 - A Research Software Engineering Workflow for Computational Science
  and Engineering\n\nWe present a Research Software Engineering (RSE) workf
 low for developing research software in Computational Science and Engineer
 ing (CSE) in university research groups. Their members have backgrounds fr
 om different scientific disciplines and often lack education in RSE. Resea
 rch software development...\n\n\nMoritz Schwarzmeier, Tomislav Mari&#263;, Tobi
 as Tolle, Jan-Patrick Lehr, Ioannis Pappagianidis, Benjamin Lambie, Dieter
  Bothe, and Christian Bischof (TU Darmstadt)\n---------------------\nP42 -
  Multigrid in H(curl) on Hybrid Tetrahedral Grids\n\nIn many applications 
 large scale solvers for Maxwell's equations are an indispensable tool. Thi
 s work presents theory and algorithms that are relevant to the solution of
  Maxwell's equations as well as their implementation in HyTeG. We focus on
  multigrid methods for the curl-curl-problem which arises...\n\n\nDaniel B
 auer (Friedrich-Alexander-Universität Erlangen-Nürnberg)\n----------------
 -----\nP47 - Parallel Training of Deep Neural Networks\n\nDeep neural netw
 orks (DNNs) are used in a wide range of application areas and scientific f
 ields. The accuracy and the expressivity of the DNNs are tightly coupled t
 o the number of parameters of the network as well as the amount of data us
 ed for training. As a consequence, the networks and the amount...\n\n\nSam
 uel Cruz (Università della Svizzera italiana, UniDistance Suisse); Alena K
 opanicakova (Brown University, Università della Svizzera italiana); Hardik
  Kothari (Università della Svizzera italiana); and Rolf Krause (Università
  della Svizzera italiana, UniDistance Suisse)\n---------------------\nP24 
 - Geodynamo Simulations in a Full Sphere\n\nAlthough the geomagnetic field
  exists since about 4 Gyr, recent estimates for the formation of the Earth
 's inner core go back no further than 500 Myr to 1 Gyr. Here we run rapidl
 y rotating dynamos in a full sphere geometry, representative of the Earth'
 s dynamo before the nucleation of the inner core...\n\n\nFabian Burmann, J
 iawen Luo, Philippe David Marti, and Andrew Jackson (ETH Zurich)\n--------
 -------------\nP01 - A Language-Interoperable C++-Based Memory-Manager for
  the ICON Climate and Weather Prediction Model\n\nHPC machines now use acc
 elerators such as GPUs. In addition, CPUs themselves now feature many core
 s as well as special fast memory, like the Fujistu A64FX and Intel Sapphir
 e Rapids. These rapid changes create important challenges for simulation c
 odes to accommodate different parallel programming mod...\n\n\nClaudius Ho
 leksa (Karlsruhe Institute of Technology), Ralf Müller and Jörg Behrens (G
 erman Climate Computing Centre), Florian Prill (DWD), Christopher Bignamin
 i and Will Sawyer (ETH Zurich / CSCS), Xavier Lapillonne (MeteoSwiss), Ser
 gey Kosukhin and Daniel Klocke (Max Planck Institute for Meteorology), Ter
 ry Cojean and Yen-Chen Chen (Karlsruhe Institute of Technology), Hartwig A
 nzt (University of Tennessee), and Claudia Frauen (German Climate Computin
 g Centre)\n---------------------\nP14 - Closing the Gap: Aligning Develope
 rs’ Expectations and Users’ Practices in Cloud Computing Infrastructure\n\
 nThere are often discrepancies between the uses that infrastructure develo
 pers envision for their technology and the way they are implemented in rea
 lity. We report on this gap between expectation and practice based on our 
 ongoing study of the user-experience on a national cyberinfrastructure sys
 tem f...\n\n\nTamanna Motahar, Johanna Cohoon, Kazi Sinthia Kabir, and Jas
 on Wiese (University of Utah)\n---------------------\nP41 - MPI for Multi-
 Core, Multi Socket, and GPU Architectures: Optimised Shared Memory Allredu
 ce\n\nIn the literature the benefits of shared memory collectives especial
 ly allreduce have been shown. This intra-node communication is not only ne
 cessary for single node communications but it is also a key component of m
 ore complex inter-node communication algorithms [1]. In contrast to [2], o
 ur impleme...\n\n\nAndreas Jocksch and Jean-Guillaume Piccinali (ETH Zuric
 h / CSCS)\n---------------------\nP20 - Docker Container in DWD's Seamless
  INtegrated FOrecastiNg sYstem (SINFONY)\n\nAt Deutscher Wetterdienst (DWD
 ), the SINFONY project has been set up to develop a seamless ensemble pred
 iction system for convective-scale forecasting with forecast ranges of up 
 to 12 hours. It combines Nowcasting (NWC) techniques with numerical weathe
 r prediction (NWP) in a seamless way. So far NWC...\n\n\nMatthias Zacharuk
  (DWD)\n---------------------\nP16 - Denoising Electronic Signals from Par
 ticle Detectors Using a Flexible Deep Convolutional Autoencoder\n\nIn this
  work, we present the use of a deep convolutional autoencoder to denoise s
 ignals from particle detectors. The study of rare particle interactions is
  crucial in advancing our understanding of the Universe. However, the pres
 ence of electronic noise makes signal events difficult to distinguish f...
 \n\n\nMark Anderson, Noah Rowe, and Tianai Ye (Queen's University)\n------
 ---------------\nP02 - A Massively Parallel Approach to Forecasting Electr
 icity Prices\n\nWith the ongoing energy crisis in Europe, accurate forecas
 ting of electricity price levels and volatility is essential to planning g
 rid operations and protecting consumers from extreme prices. We present ho
 w massively parallel stochastic optimal power flow models can be deployed 
 on modern many-core ...\n\n\nTimothy Holt (Università della Svizzera itali
 ana, Oak Ridge National Laboratory)\n---------------------\nP28 - GPU-Opti
 mized Tridiagonal and Pentadiagonal System Solvers for Spectral Transforms
  in QuiCC\n\nQuiCC is a code under development designed to solve the equat
 ions of magnetohydrodynamics in a full sphere and other geometries. It use
 s a fully spectral approach to the problem, with the Jones-Worland polynom
 ials as a radial basis and Spherical Harmonics as a spherical basis. We pr
 esent an alternat...\n\n\nDmitrii Tolmachev, Philippe Marti, and Giacomo C
 astiglioni (ETH Zurich); Daniel Ganellari (ETH Zurich / CSCS); and Andrew 
 Jackson (ETH Zurich)\n---------------------\nP48 - ProtoX: A First Look\n\
 nStencil operation is a key component in the numerical solution of partial
  differential equations. Developers tend to use different libraries that p
 rovide these operations for them. One such library is Proto. It is a C++ b
 ased domain specific library designed to provide an intuitive interface th
 at op...\n\n\nHet Mankad and Sanil Rao (Carnegie Mellon University), Phil 
 Colella and Brian Van Straalen (Lawrence Berkeley National Laboratory), an
 d Franz Franchetti (Carnegie Mellon University)\n---------------------\nP2
 5 - Ginkgo — A High-Performance Portable Numerical Linear Algebra Software
 \n\nNumerical linear algebra building blocks are used in many modern scien
 tific applications codes. Ginkgo is an open-source numerical linear algebr
 a software designed around the principles of portability, flexibility, usa
 bility, and performance. The Ginkgo library is integrated into the deal.II
 , MFEM, ...\n\n\nTerry Cojean and Isha Aggarwal (Karlsruhe Institute of Te
 chnology); Natalie Beams and Hartwig Anzt (University of Tennessee); and Y
 en-Chen Chen, Thomas Grützmacher, Fritz Göbel, Marcel Koch, Gregor Olenik,
  Pratik Nayak, Tobias Ribizel, and Yu-Hsiang Tsai (Karlsruhe Institute of 
 Technology)\n---------------------\nP18 - Directive-Based, Fortran/C++ Int
 eroperable Approach to GPU Offloading of the High Performance Gyrokinetic 
 Turbulence Code GENE-X\n\nThe achievement of high plasma confinement is th
 e key to realize commercially attractive energy production by magnetic con
 finement fusion (MCF) devices. Turbulence plays a significant role in main
 taining the plasma confinement within MCF devices. The GENE-X code is base
 d on an Eulerian (continuum) a...\n\n\nJordy Trilaksono, Philipp Ulbl, and
  Andreas Stegmeir (Max Planck Institute for Plasma Physics) and Frank Jenk
 o (Max Planck Institute for Plasma Physics, University of Texas at Austin)
 \n---------------------\nP36 - Iterative Refinement With Hierarchical Low-
 Rank Preconditioners Using Mixed Precision\n\nIt has been shown that the s
 olution to a dense linear system can be accelerated by using mixed precisi
 on iterative refinement relying on approximate LU-factorization. While mos
 t recent work has focused on obtaining such a factorization at a reduced p
 recision, we investigate an alternative via low-ra...\n\n\nThomas Spendlho
 fer and Rio Yokota (Tokyo Institute of Technology)\n---------------------\
 nP34 - Interpretable Compression of Fluid Flows Using Graph Neural Network
 s\n\nNeural network (NN) based reduced-order models (ROMs) via autoencodin
 g have been shown to drastically accelerate traditional computational flui
 d dynamics (CFD) simulations for rapid design optimization and prediction 
 of fluid flows. However, many real-world applications (e.g. hypersonic pro
 pulsion, ...\n\n\nShivam Barwey (Argonne National Laboratory) and Romit Ma
 ulik (Argonne National Laboratory, University of Pennsylvania)\n----------
 -----------\nP30 - High Performance Computing Meets Approximate Bayesian I
 nference\n\nDespite the ongoing advancements in Bayesian computing, large-
 scale inference tasks continue to pose a computational challenge that ofte
 n requires a trade-off between accuracy and computation time. Combining so
 lution strategies from the field of high-performance computing with state-
 of-the-art stati...\n\n\nLisa Gaedke-Merzhäuser (Università della Svizzera
  italiana), Haavard Rue (King Abdullah University of Science and Technolog
 y), and Olaf Schenk (Università della Svizzera italiana)\n----------------
 -----\nP31 - High-Performance Computing by and for Patient Specific Mechan
 ical Properties\n\nModeling the mechanical behavior of human trabecular bo
 nes improves technical applications and the treatment of fractures and bon
 e or joint related diseases. However, this type of bone consists of a larg
 e number of struts and plates, resulting in a highly anisotropic and patie
 nt specific behavior. F...\n\n\nJohannes Gebert, Ralf Schneider, and Micha
 el Resch (High-Performance Computing Center Stuttgart, University of Stutt
 gart)\n---------------------\nP26 - Global Sensitivity Analysis of High-Di
 mensional Models with Correlated Inputs\n\nGlobal sensitivity analysis is 
 an important tool used in many domains of computational science to either 
 gain insight into the mathematical model and interaction of its parameters
  or study the uncertainty propagation through the input-output interaction
 s. This works introduces a comprehensive framew...\n\n\nJuraj Kardos and O
 laf Schenk (Università della Svizzera italiana) and Derek Groen and Diana 
 Suleimenova (Brunel University London)\n---------------------\nP17 - Detec
 ting Financial Fraud with Graph Neural Networks\n\nDetecting financial fra
 ud is a challenging classification problem that entails the discovery of s
 uspicious patterns in large-scale and time evolving data. Traditionally, f
 inancial institutions have been relying on rule-based methods to identify 
 suspicious accounts, with such approaches becoming inef...\n\n\nJulien Sch
 midt, Dimosthenis Pasadakis, and Olaf Schenk (Università della Svizzera it
 aliana)\n---------------------\nP40 - Modeling a Novel Laser-Driven Electr
 on Acceleration Scheme: Particle-In-Cell Simulations at the Exascale\n\nIn
 tense femtosecond lasers focused on low-density gas jets can accelerate ul
 tra-short electron bunches up to very high energies (from hundreds of MeV 
 to several GeV) over a few millimeters or a few centimeters. However, conv
 entional laser-driven electron acceleration schemes do not provide enough 
 ch...\n\n\nLuca Fedeli (CEA), Axel Huebl (Lawrence Berkeley National Labor
 atory), France Boillod-Cerneux and Thomas Clark (CEA), Kevin Gott (Lawrenc
 e Berkeley National Laboratory), Conrad Hillairet (Arm), Stephan Jaure (At
 os), Adrien Leblanc (ENSTA), Rémi Lehe and Andrew Myers (Lawrence Berkeley
  National Laboratory), Christelle Piechurski (GENCI), Mitsuhisa Sato (RIKE
 N), Neil Zaim (CEA), Weiqun Zhang and Jean-Luc Vay (Lawrence Berkeley Nati
 onal Laboratory), and Henri Vincenti (CEA)\n---------------------\nP22 - E
 fficient Data Managment in Fully Spectral Dynamo Simulations on Heterogene
 ous Nodes\n\nOur CFD framework QuICC, based on a fully spectral method, ha
 s been successfully used for various dynamo simulations in spherical and C
 artesian geometries. It runs efficiently on a few thousands of cores using
  a 2D data distribution based on a distributed memory paradigm (MPI). In o
 rder to better ha...\n\n\nGiacomo Castiglioni, Philippe Marti, and Dmitrii
  Tolmachev (ETH Zurich); Daniel Ganellari (ETH Zurich / CSCS); and Andy Ja
 ckson (ETH Zurich)\n---------------------\nP09 - Analyzing Physics-Informe
 d Neural Networks for Solving Classical Flow Problems\n\nThe application o
 f Neural Networks (NNs) has been extensively investigated for fluid dynami
 c problems. A specific form of NNs are Physics-Informed Neural Networks (P
 INNs), which incorporate physics-based embeddings to account for physical 
 laws. In this work, the performance of PINNs is compared to t...\n\n\nRish
 abh Puri (Forschungszentrum Jülich); Mario Rüttgers (Forschungszentrum Jül
 ich, RWTH Aachen University); and Rakesh Sarma and Andreas Lintermann (For
 schungszentrum Jülich)\n---------------------\nP19 - DNS of Strongly Turbu
 lent Thermal Convection in a Non-Rotating Full Sphere\n\nBody forces such 
 as gravity can drive convective motion in fluids. Convection due to therma
 l gradients and the resulting buoyancy force is called thermal convection 
 and occurs ubiquitously in nature. We present results on DNS of thermal co
 nvection in a non-rotating full sphere and with different bou...\n\n\nTobi
 as Sternberg, Philippe Marti, Giacomo Gastiglioni, and Andrew Jackson (ETH
  Zurich)\n---------------------\nP11 - Bridging the Language Gap: Classes 
 for C++/Fortran Interoperability\n\nFortran and C++ remain popular languag
 es for high-performance scientific computing. Interoperation of these two 
 languages is of great interest; be it to take advantage of a mature ecosys
 tem of libraries, or for coupling individual simulation codes into larger 
 multi-scale or multi-physics application...\n\n\nIvan Pribec (Leibniz Supe
 rcomputing Centre)\n---------------------\nP15 - Compressing Multidimensio
 nal Weather and Climate Data into Neural Networks\n\nWeather and climate s
 imulations produce petabytes of high-resolution data that are later analyz
 ed by researchers in order to understand climate change or severe weather.
  We propose a new method of compressing this multidimensional weather and 
 climate data: a coordinate-based neural network is traine...\n\n\nLangwen 
 Huang and Torsten Hoefler (ETH Zurich)\n---------------------\nP03 - A Nov
 el Stochastic Parameterization for Lagrangian Modeling of Atmospheric Aero
 sol Transport\n\nIn recent years, it has become clear that the behavior of
  atmospheric aerosols has a non-negligible effect on radiative forcing wit
 hin Earth's climate and the computational models that simulate it [Carslaw
 , et al., Nature, 2013]. Thus, we must obtain descriptive aerosol models t
 hat are also predicti...\n\n\nMichael Schmidt (Sandia National Laboratorie
 s)\n---------------------\nP59 - A Scalable Interior-Point Method for PDE-
 Constrained Inverse Problems Subject to Inequality Constraints\n\nWe prese
 nt a scalable computational method for large-scale inverse problems with P
 DE and inequality constraints. Such problems are used to learn spatially d
 istributed variables that respect bound constraints and parametrize PDE-ba
 sed models from unknown or uncertain data. We first briefly overview P...\
 n\n\nTucker Hatland and Cosmin Petra (Lawrence Livermore National Laborato
 ry), Noemi Petra (University of California Merced), and Jingyi Wang (Lawre
 nce Livermore National Laboratory)\n---------------------\nP07 - Addressin
 g Exascale Challenges for Numerical Algorithms of Strongly Correlated Latt
 ice Models\n\nStrongly Correlated Lattice Models play an important role fo
 r our understanding of Quantum Magnetism, High-Tc superconductors, and als
 o Quantum Simulators built from cold atoms, trapped ions, Rydberg atoms, o
 r superconducting qubits. Wave function based numerical algorithms, such a
 s Exact Diagonaliz...\n\n\nSamuel Gozel (Paul Scherrer Institute) and Andr
 eas M. Läuchli (Paul Scherrer Institute, EPFL)
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