BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Europe/Stockholm
X-LIC-LOCATION:Europe/Stockholm
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20250822T115815Z
LOCATION:Campussaal - Plenary Room
DTSTART;TZID=Europe/Stockholm:20250617T190000
DTEND;TZID=Europe/Stockholm:20250617T210000
UID:submissions.pasc-conference.org_PASC25_sess151@linklings.com
SUMMARY:Poster Session and Reception
DESCRIPTION:P04 - Calculation of Spin Hole Qubit Eigenstates with GPU-Acce
 lerated Rayleigh–Chebyshev Subspace Iteration Method\n\nQuantum computers 
 leverage quantum mechanical effects to solve complex problems exponentiall
 y faster than classical computers. Their building blocks, or 'qubits', can
  be realized with different technologies. Silicon spin hole qubits are one
  of the most promising ones, thanks to their long coherence ...\n\n\nAlexa
 nder Maeder, Ilan Bouquet, Vincent Maillou, and Alexandros Nikolaos Ziogas
  (ETH Zurich); Chris Anderson (UCLA); and Mathieu Luisier (ETH Zurich)\n--
 -------------------\nP06 - Code-Generation of Highly Efficient Finite Elem
 ent Operations Using the MLIR Compiler Infrastructure\n\nThe immense finan
 cial and environmental cost of high performance computing (HPC) infrastruc
 ture demands highly efficient and hardware specific software. In modern ex
 ascale hardware, the development of efficient kernels requires addressing 
 both hardware heterogeneity and the memory bandwidth bottlene...\n\n\nEdwa
 rd Erasmie-Jones (King's College London), Giacomo Castiglioni (ETH Zurich 
 / CSCS), and David Moxey (King's College London)\n---------------------\nP
 09 - Efficient Execution of Multiphysics Simulation Assembly Using Kokkos:
 :Graph\n\nThe computation of elemental system matrices and right-hand-side
  vectors and their assembly into sparse linear algebra data structures is 
 a key component of many multiphysics simulation codes. When the assembly i
 nvolves multiple types of governing equations that might also change by su
 bdomain (heter...\n\n\nMaarten Arnst and Romin Tomasetti (University of Li
 ège)\n---------------------\nP15 - Fostering the Wider Adoption of High-Pe
 rformance Computing by UK-Based Arts and Humanities Researchers via Nation
 al Training and Community-Building Initiatives\n\nThe integration of compu
 ting innovations into Arts and Humanities (A&H) research is crucial. Howev
 er, high-performance computing (HPC) is not widely used in A&H, posing ris
 ks to interdisciplinary integration with sciences that use advanced comput
 ational methods. Efforts in the UK to address the digi...\n\n\nEamonn Bell
  (Durham University) and Karina Rodriguez Echavarria (University of Bright
 on)\n---------------------\nP37 - pyGinkgo: Python Bindings for Ginkgo\n\n
 Over the past decade, machine learning has achieved significant advancemen
 ts, with applications spanning diverse fields such as physics, medicine, e
 conomics or energy. A pressing challenge in contemporary machine learning 
 is optimizing models for time and energy efficiency. One effective approac
 h to...\n\n\nKeshvi Tuteja and Gregor Olenik (Karlsruhe Institute of Techn
 ology); Roman Mishchuk and Nicolas Venkovic (Technical University of Munic
 h); Markus Götz and Achim Streit (Karlsruhe Institute of Technology); Hart
 wig Anzt (Technical University of Munich, University of Tennessee); and Ch
 arlotte Debus (Karlsruhe Institute of Technology)\n---------------------\n
 P39 - Simulations of Giant Impacts with Material Strength in pkdgrav3\n\nG
 iant impacts form the last stage of planet formation and play a key role i
 n determining many aspects like the final structure of planetary systems a
 nd the masses and compositions of its constituents. A common choice for nu
 merically solving the equations of motion is the Smoothed Particle Hydrody
 nam...\n\n\nThomas Meier (University of Zurich); Christian Reinhardt (Univ
 ersity of Zurich, University of Bern); and Douglas Potter and Joachim Stad
 el (University of Zurich)\n---------------------\nP08 - Developing a Porta
 ble Implementation for the Next-Generation ECMWF Model\n\nWe present the d
 evelopment of a portable high-level Python implementation for the next-gen
 eration ECMWF global dynamical core designed to facilitate simulations at 
 extreme numerical resolutions. This new model framework, called the Portab
 le Model for Multi-Scale Atmospheric Prediction (PMAP), is an ...\n\n\nSar
 a Faghih-Naini (ECMWF); Till Ehrengruber (ETH Zurich / CSCS); Stefano Ubbi
 ali (ETH Zurich); Lukas Papritz (ETH Zurich, ECMWF); and Christian Kühnlei
 n (ECMWF)\n---------------------\nP43 - Towards Exascale Particle-Mesh Met
 hods: A Massively Parallel Performance Portable C++ Particle-in-Cell Frame
 work\n\nWe showcase the Independent Parallel Particle Layer (IPPL), a perf
 ormance portable C++ library for particle-in-cell methods. IPPL makes use 
 of Kokkos (a performance portability abstraction layer), HeFFTe (a library
  for large scale FFTs), and MPI (Message Passing Interface) to deliver a p
 ortable, mas...\n\n\nSonali Mayani (Paul Scherrer Institute, ETH Zurich); 
 Matthias Frey (University of St Andrews); Sriramkrishnan Muralikrishnan (F
 orschungszentrum Jülich); and Ryan Ammann and Andreas Adelmann (Paul Scher
 rer Institute, ETH Zurich)\n---------------------\nP38 - Scalable Genomic 
 Context Analysis with GCsnap2 on HPC Clusters\n\nGCsnap2 Cluster is a scal
 able, Python-based high performance solution for genomic context analysis,
  co-developed by computer and life scientists to overcome the scalability 
 limitations of its predecessor, GCsnap1 Desktop. Leveraging distributed co
 mputing with mpi4py.futures, GCsnap2 Cluster achieved...\n\n\nReto Krummen
 acher and Osman Seckin Simsek (University of Basel); Michèle Leemann, Leil
 a T. Alexander, and Torsten Schwede (University of Basel, Swiss Institute 
 of Bioinformatics); Florina M. Ciorba (University of Basel); and Joana Per
 eira (University of Basel, Swiss Institute of Bioinformatics)\n-----------
 ----------\nP35 - Performance Portability Across Different Mathematical Mo
 dels, Hardware, and Simulation Scenarios in Molecular Dynamics\n\nDue to t
 he importance of Molecular Dynamics simulations within fields such as ther
 modynamics, numerous methods have been developed to speedup the force calc
 ulations, which typically dominate the runtime. None of these methods are,
  however, optimal for every molecular model, on every hardware, and fo...\
 n\n\nSamuel James Newcome, Fabio Alexander Gratl, Manish Kumar Mishra, Mar
 kus Mühlhäußer, Jonas Schumacher, and Hans-Joachim Bungartz (Technical Uni
 versity of Munich)\n---------------------\nP33 - Optimizing Data Offload i
 n the IFS Using GPU-Aware Data Structures and Source-To-Source Translation
 \n\nThe adaptation of the ECMWF’s medium-range forecasting model, the Inte
 grated Forecasting System (IFS), to heterogeneous computing architectures 
 is an ongoing effort. The IFS consists of millions of lines of Fortran cod
 e that is highly optimized for modern CPUs. This poses significant challen
 ge...\n\n\nJohan Ericsson, Ahmad Nawab, and Balthasar Reuter (ECMWF); Phil
 ippe Marguinaud and Judicaël Grasset (Meteo-France); and Michael Lange (EC
 MWF)\n---------------------\nP29 - itwinai: Enabling Scalable AI Workflows
  on HPC for Digital Twins in Science\n\nThe interTwin project is advancing
  the integration of Digital Twins across scientific domains, focusing on p
 hysics and climate research. A key component of this project is itwinai, a
  Python library designed to streamline scalable AI workflows on High-Perfo
 rmance Computing (HPC) systems. With its uni...\n\n\nMatteo Bunino, Anna E
 lisa Lappe, and Jarl Sondre Sæther (CERN); Rakesh Sarma (FZ Jülich); Maria
  Girone (CERN); and Andreas Lintermann (Forschungszentrum Jülich)\n-------
 --------------\nP14 - Flux-Form Semi-Lagrangian (FFSL) Schemes on a Triang
 ular Mesh\n\nFlux-Form Semi-Lagrangian (FFSL) schemes for the solution of 
 hyperbolic partial differential equations are popular since they allow for
  high CFL numbers. There are applications in plasma physics and weather an
 d climate simulations. For the latter recently icosahedral meshes on a sph
 erical domain are...\n\n\nAndreas Jocksch (ETH Zurich / CSCS), Nina Burgdo
 rfer (MeteoSwiss), Daniel Reinert (DWD), Christoph Mueller (MeteoSwiss), a
 nd David Strassmann and Roger Käppeli (ETH Zurich)\n---------------------\
 nP12 - Enhancing Productivity and Performance Analysis on Euro HPC Systems
 \n\nLarge-scale EuroHPC High-Performance Computing (HPC) systems, such as 
 Leonardo and Lumi, present significant challenges for developers. A key di
 fficulty is adapting their software to new architectures, accelerators and
  different compiler options in order to fully leverage available resources
 . As a r...\n\n\nMichael Redenti and Nitin Shukla (CINECA)\n--------------
 -------\nP03 - Bit-IF: An Incremental Sparse Tensor Format for Maximizing 
 Efficiency in Tensor-Vector Multiplications\n\nThis poster presents **Bit-
 IF** (Incremental Sparse Fibers with Bit Encoding), a novel sparse tensor 
 format designed to reduce the storage requirements of large tensors and im
 prove the efficiency of tensor operations, particularly of tensor-vector m
 ultiplication (TVM). As datasets in many scientific...\n\n\nXiaohe Niu (mc
 s Software AG); Georg Meyer (Friedrich-Alexander-Universität Erlangen-Nürn
 berg, Università della Svizzera italiana); Dimosthenis Pasadakis (Universi
 tà della Svizzera italiana, Panua Technologies); Albert-Jan N. Yzelman (Hu
 awei Zurich Research Center); and Olaf Schenk (Università della Svizzera i
 taliana, Panua Technologies)\n---------------------\nACMP01 - Designing Bi
 omimetic Materials for Carbon Capture: Leveraging High-Performance Computi
 ng for Large-Scale Molecular Dynamics Simulations to Advance Sustainable S
 olutions\n\nSustainable carbon capture and greenhouse gas mitigation deman
 d innovative strategies that harness biomolecular functions and integrate 
 them into existing technologies. Enzyme-based systems offer a promising so
 lution for sustainable CO₂ capture, yet their industrial adoption is limit
 ed by limi...\n\n\nMerve Fedai and Yaroslava Yingling (North Carolina Stat
 e University)\n---------------------\nACMP02 - Development of a Predictive
  Model for the Prognosis of Patients with Breast Cancer\n\nBreast cancer i
 s one of the most prevalent malignancies among women, accounting for about
  a quarter of all new diagnoses worldwide. Treatment and prognosis vary ac
 cording to histological subtype and stage at diagnosis. Because of heterog
 eneity in treatment responses, biomarkers that predict clinical...\n\n\nPa
 tricia Honorato Moreira (Inteli - Institute of Technology and Leadership)\
 n---------------------\nP24 - How to Build an Energy Dataset for HPC\n\nQu
 antifying the energy consumption in HPC domain is becoming increasingly cr
 itical nowadays, driven by rising energy costs. To gain a comprehensive un
 derstanding of the energy footprint created by the significant power deman
 d of modern systems like Alps, which exceeds its predecessor Piz Daint in 
 en...\n\n\nMathilde Gianolli, Massimo Benini, Jean-Guillaume Piccinali, Gi
 anna Marano, and Dino Conciatore (ETH Zurich / CSCS)\n--------------------
 -\nP19 - A GPU-Accelerated Implementation of Spectrum Slicing for Plane-Wa
 ve Density Functional Theory in ABINIT\n\nWe consider the problem of accel
 erating the iterative diagonalization of Hamiltonian operators for electro
 nic structure calculations in plane-wave Density Functional Theory. The co
 mplexity bottleneck of existing subspace iteration schemes is that the Ray
 leigh-Ritz procedure for extracting eigenvecto...\n\n\nIoanna-Maria Lygats
 ika and Marc Torrent (CEA, Université Paris-Saclay)\n---------------------
 \nP07 - A Deep Dive into Deep Learning Frameworks for Protein Structure Pr
 ediction: Developing and Evaluating Classes of Biomolecular Complexes\n\nA
 ccurately predicting the structure of a protein has been a long standing a
 nd extremely challenging problem in biology. In recent years, the rapid ev
 olution and adoption of artificial intelligence (AI) in scientific domains
  including biology have made the prediction of protein structures leveragi
 ng ...\n\n\nVerónica G. Melesse Vergara, Érica Texeira Prates, Manesh Shah
 , and Dan Jacobson (Oak Ridge National Laboratory)\n---------------------\
 nP21 - Graph Abstraction for Efficient Scheduling of Asynchronous Workload
 s on GPU\n\nMany computational physics simulations need to efficiently exe
 cute asynchronous workloads (FEM assembly, linear algebra, etc) that can b
 e organised as a Direct Acyclic Graph (DAG). Ad hoc scheduling of these as
 ynchronous workloads is an additional burden to the code and might not ful
 ly exploit the a...\n\n\nRomin Tomasetti and Maarten Arnst (University of 
 Liège)\n---------------------\nP28 - Interactive Visualization of High-Ene
 rgy Physics Events via Nvidia Omniverse\n\nSimulations play a crucial role
  in high energy, nuclear, and accelerator physics, aiding in both data ana
 lysis and hardware development. Over the years, several advanced programs 
 have been created to generate detailed and precise simulated events, provi
 ding insights into complex physical processes. ...\n\n\nFelice Nenna (INFN
  Bari, University of Padova); Marcello Maggi (INFN Bari); Matteo Bunino (C
 ERN); Stewart Boogert (University of Manchester); and Siobhan Alden (Royal
  Holloway, University of London)\n---------------------\nP17 - GPU-Acceler
 ated DEM Simulations for Complex Particle Shapes: Optimizing Spheropolyhed
 ron Contact Detection\n\nThe Discrete Element Method (DEM) is an N-body nu
 merical method widely used to model granular materials with various partic
 le shapes, including complex geometries like spheropolyhedra. A major comp
 utational challenge in DEM lies in contact detection, particularly for suc
 h complex shapes, which invol...\n\n\nCarlo Elia Doncecchi (CEA)\n--------
 -------------\nP01 - Achieving Performance Portability on ECMWF’s Open-Sou
 rce Operational Wave Model ecWAM Using Source-To-Source Translation and GP
 U-Aware Data-Structures\n\nIt can be quite challenging to adapt production
  numerical weather prediction (NWP) codes for GPU execution. Those codes h
 ave typically been developed and optimised for multi-core CPUs and are con
 tinually being updated by domain scientists. Additional complexity arises 
 from the vast size of these cod...\n\n\nMichael Staneker and Ahmad Nawab (
 ECMWF)\n---------------------\nP36 - Pyccel: Automating Translation of Pyt
 hon Prototypes to C/Fortran Production Codes\n\nPython is a widely popular
  programming language, valued for its simplicity, ease of learning, and va
 st ecosystem of packages, making it ideal for scientific applications. How
 ever, its execution speed is a major limitation compared to low-level lang
 uages. We present Pyccel, an intuitive transpiler th...\n\n\nEmily Bourne 
 (EPFL), Mohamed Jalal Maaouni (UM6P), and Yaman Güçlü (Max Planck Institut
 e for Plasma Physics)\n---------------------\nP22 - GT4Py: A Python Framew
 ork for the Development of High-Performance Weather and Climate Applicatio
 ns\n\nGT4Py is a Python framework for weather and climate applications sim
 plifying the development and maintenance of high-performance codes in prot
 otyping and production environments. \nGT4Py separates model development f
 rom hardware-dependent optimizations, instead of intermixing them in sourc
 e code, as ...\n\n\nMauro Bianco (ETH Zurich / CSCS); Yilu Chen (ETH Zuric
 h); Till Ehrengruber (ETH Zurich / CSCS); Sara Faghih-Naini (ECMWF); Nicol
 etta Farabullini (ETH Zurich); Abishek Gopal (NCAR, ETH Zurich); Rico Häus
 elmann (ETH Zurich / CSCS); Samuel Kellerhals (ETH Zurich); Christos Kotsa
 los and Ioannis Magkanaris (ETH Zurich / CSCS); Magdalena Luz (ETH Zurich)
 ; Christoph Müller (MeteoSwiss); Philip Müller, Edoardo Paone, and Enrique
  González Paredes (ETH Zurich / CSCS); David Strassmann (ETH Zurich); and 
 Felix Thaler, Hannes Vogt, and Thomas Schulthess (ETH Zurich / CSCS)\n----
 -----------------\nP13 - Estimation of Calving Law Parameters from Satelli
 te Data\n\nCapturing the calving front motion is critical for simulations 
 of ice shelves and tidewater glaciers. Multiple physical processes, includ
 ing sliding, water pressure and failure need to be understood to accuratel
 y model the front. Calving is particularly challenging due to its disconti
 nuous nature an...\n\n\nDaniel Abele (German Aerospace Center, Technical U
 niversity of Munich); Achim Basermann (German Aerospace Center); Martin Bu
 rger (DESY, University of Hamburg); Hans-Joachim Bungartz (Technical Unive
 rsity of Munich); and Angelika Humbert (Alfred-Wegener-Institut, Helmholtz
 -Zentrum für Polar- und Meeresforschung; University of Bremen)\n----------
 -----------\nP26 - Improving Productivity of Threaded Scientific Applicati
 ons with Quo Vadis\n\nScientific discovery is increasingly enabled by hete
 rogeneous hardware that includes multiple processor types, deep memory hie
 rarchies, and heterogeneous memories. To effectively utilize this hardware
 , computational scientists must compose their applications using a combina
 tion of programming models...\n\n\nEdgar A. Leon (Lawrence Livermore Natio
 nal Laboratory); Samuel K. Gutierrez (Los Alamos National Laboratory); and
  Guillaume Mercier (Bordeaux-INP; Inria, CNRS, LaBRI UMR 5800)\n----------
 -----------\nP40 - Spectral Methods for the Clustering of Cyclic and Acycl
 ic Graphs\n\nTraditional spectral clustering methods are designed for undi
 rected graphs and fail to capture the directionality of the edges and of t
 he connections between the clusters. The aim of our work is centered aroun
 d developing novel spectral methods for the spectral clustering of directe
 d graphs with blo...\n\n\nJacopo Palumbo (Università della Svizzera italia
 na, Politecnico di Milano); Dimosthenis Pasadakis (Università della Svizze
 ra italiana); Albert-Jan Yzelman (Huawei); and Olaf Schenk (Università del
 la Svizzera italiana)\n---------------------\nP41 - SYCL and Block-Structu
 red Grids: Performance Impact on Simulations of Complex Costal Ocean Domai
 ns\n\nDeveloping the next generation of climate modelling tools to increas
 e throughput and ensure performance portability is crucial. The choice of 
 an underlying grid for ocean modelling, an important climate compartment, 
 is difficult. The almost fractal-like boundaries of ocean domains and quic
 kly changi...\n\n\nJonathan Schmalfuß (University of Bayreuth), Daniel Zin
 t (New York University), Sara Faghih-Naini (ECMWF), Julian Stahl (Friedric
 h-Alexander-Universität Erlangen-Nürnberg), Markus Büttner (University of 
 Bayreuth), Roberto Grosso (Friedrich-Alexander-Universität Erlangen-Nürnbe
 rg), and Vadym Aizinger (University of Bayreuth)\n---------------------\nP
 30 - The MENTOR Interpretation Agent: From Network Embeddings to Mechanist
 ic Narratives via Retrieval-Augmented LLMs\n\nDespite an increasing number
  of complex omics data sets, extracting comprehensive mechanistic insights
  from these data remains challenging. To address this, we developed a huma
 n-in-the-loop LLM-based agentic retrieval-augmented generation (RAG) pipel
 ine, the MENTOR Interpretation Agent (MENTOR-IA), ...\n\n\nAnna H.C. Vlot 
 (Oak Ridge National Laboratory); Matthew Lane (Oak Ridge National Laborato
 ry; Bredesen Center for Interdisciplinary Graduate Research and Education,
  University of Tennessee-Knoxville); Kyle A. Sullivan (Oak Ridge National 
 Laboratory); Peter Kruse (Oak Ridge National Laboratory; Bredesen Center f
 or Interdisciplinary Graduate Research and Education, University of Tennes
 see-Knoxville); John Dandy and Selin Kaplanoglu (Oak Ridge National Labora
 tory); Alice Townsend and Jean Merlet (Oak Ridge National Laboratory; Bred
 esen Center for Interdisciplinary Graduate Research and Education, Univers
 ity of Tennessee-Knoxville); and Daniel A. Jacobson (Oak Ridge National La
 boratory)\n---------------------\nP02 - Advances in HPC-Oriented Refactori
 ng Techniques with Coccinelle\n\nOur collaboration around the Coccinelle t
 ool aims at streamlining maintenance of large software projects in HPC.\nW
 e are developing techniques to modify large swathes of C/C++ codes and int
 roduce e.g.:\nGPU support, replace an API with another, introduce modern C
 ++ according to guidelines, change para...\n\n\nMichele Martone (Leibniz S
 upercomputing Centre) and Julia Lawall and Victor Gambier (INRIA)\n-------
 --------------\nP11 - Enabling Lattice QCD Normalizing Flows in HPC Infras
 tructures\n\nThe Horizon Europe project interTwin aims at developing a pro
 totype for a multidisciplinary Digital Twin Engine, applicable across a wh
 ole spectrum of scientific disciplines: High Energy Physics (HEP), Environ
 ment, Climate, etc. As part of this effort we explore the extent to which 
 Machine Learning ...\n\n\nMatteo Bunino (CERN), Isabel Campos Plasencia (I
 FCA/CSIC), Javad Komijani and Marina Marinkovic (ETH Zurich), Gaurav Sinha
  Ray (IFCA/CSIC), Rakesh Sarma (Forschungszentrum Jülich), and Jarl Sondre
  Saether (CERN)\n---------------------\nP16 - GPU Porting of ECMWF Physica
 l Parametrizations Using a High-Level Programming Model\n\nWe present rece
 nt developments in the GPU porting using the domain-specific library GridT
 ools for Python (GT4Py) of three physical parametrizations from the Integr
 ated Forecasting System (IFS) of the European Centre for Medium-Range Weat
 her Forecasts (ECMWF): the cloud microphysics packages CLOUDSC ...\n\n\nGa
 briel Vollenweider and Stefano Ubbiali (ETH Zurich), Christian Kühnlein (E
 CMWF), and Heini Wernli (ETH Zurich)\n---------------------\nACMP05 - A Pe
 rformance Portable Matrix-Free Finite Element Framework for Particle-Mesh 
 Methods\n\nComputing architectures are becoming increasingly complex and p
 otent, as we reach new computing capacities. Currently the first three mac
 hines in the TOP500 list are exascale systems. To be able to take full adv
 antage of these machines, and even run on such heterogeneous architectures
 , it has become...\n\n\nSonali Mayani (Paul Scherrer Institute, ETH Zurich
 )\n---------------------\nACMP03 - Distributed Computing for Spatio-Tempor
 al Bayesian Modeling Using the INLA Method\n\nBayesian inference on large-
 scale spatio-temporal models is limited by its computational feasibility, 
 a trend that is further exacerbated by the continuous increase in data ava
 ilability and model refinements. To address this issue, we present a doubl
 e-layer distributedmemory parallelization strategy...\n\n\nVincent Maillou
  and Alexandros Nikolaos Ziogas (ETH Zurich); Olaf Schenk (Università dell
 a Svizzera italiana); Mathieu Luisier (ETH Zurich); Håvard Rue (King Abdul
 lah University of Science and Technology); and Lisa Gaedke-merzhaeuser (Ki
 ng Abdullah University of Science and Technology, Università della Svizzer
 a italiana)\n---------------------\nP18 - GPU-Accelerated Fluid-Structure 
 Interaction Resampling in FEM, Including Application of 3-Dimensional 4th-
 Order WENO\n\nThis work addresses the HPC challenges of fluid structure it
 eration (FSI), focusing on the computational efficiency of mesh resampling
 . A major computational challenge arises from the fact that the method req
 uires gather and scatter memory access, which introduces a significant mem
 ory barrier, and t...\n\n\nSimone Riva (Università della Svizzera italiana
 , Euler institute) and Patrick Zulian (Università della Svizzera italiana,
  Euler institute; UniDistance Suisse)\n---------------------\nACMP04 - Mul
 ti-Team Software Collaboration within the Exascale Computing Project\n\nCo
 llaboration and team science are emerging areas of interest in research an
 d software production. Historically, multi-institutional research collabor
 ations are difficult to initiate and maintain, negatively impacting commun
 ication, negotiation, and dialogue between industry, government, and acade
 mi...\n\n\nHana Frluckaj (University of Texas at Austin)\n----------------
 -----\nP32 - Multi-Omic Single Cell Network Perturbation for Phenotypic Pr
 ediction\n\nDrug repurposing offers a cost-effective strategy to identify 
 new applications for existing medications, leveraging established safety p
 rofiles to accelerate therapeutic development. Advances in computational b
 iology and large-scale multi-omics data enable systematic identification o
 f novel therapeu...\n\n\nMatthew Lane (Oak Ridge National Laboratory, Univ
 ersity of Tennessee); Erica Prates (Oak Ridge National Laboratory); Alice 
 Townsend and Jean Merlet (Oak Ridge National Laboratory, University of Ten
 nessee); Christiane Alvarez and Alana Wells (Oak Ridge National Laboratory
 ); and Daniel Jacobson (Oak Ridge National Laboratory, University of Tenne
 ssee)\n---------------------\nP34 - Optimizing the ECsim Plasma Code for E
 xascale Architectures: GPU Acceleration, Portability, and Scalability\n\nT
 his work presents the adaptation of the plasma code ECsim for future exasc
 ale architectures. The code has three main blocks called particle movers, 
 moment gathering and field solver. The first two blocks are the most compu
 tationally challenging, thus we focused on optimizing them for GPU acceler
 ati...\n\n\nNitin Shukla (CINECA), Elisabetta Boella (E4 Computer Engineer
 ing), Filippo Spiga (NVIDIA Inc.), Michael Redenti (CINECA), Mozhgan Kabir
 i Chimeh (NVIDIA Inc.), and Maria Elena Innocenti (Ruhr University Bochum)
 \n---------------------\nP23 - Harnessing High Performance Computing for A
 dvanced Biomarker Discovery from Wearable Device Data: A Pathway to Optimi
 zed Therapeutic Outcomes\n\nThe integration of data from smartphones and w
 earable devices offers a groundbreaking opportunity to apply machine learn
 ing for advancements in digital health. This project presents a case study
  demonstrating the application of advanced machine learning techniques to 
 large-scale, heterogeneous datas...\n\n\nSilvano Coletti (Università degli
  Studi Guglielmo Marconi, CHELONIA SA) and Francesca Fallucchi (Università
  degli Studi Guglielmo Marconi)\n---------------------\nACMP06 - Understan
 ding HMM Performance for Enhanced HPC Portability\n\nHeterogeneous Memory 
 Management (HMM) simplifies programming for heterogeneous systems, making 
 High-Performance Computing (HPC) devices more accessible to domain scienti
 sts; however, it suffers from slow performance compared to other memory ma
 nagement approaches. HMM is an infrastructure provided by...\n\n\nNicholas
  Cassarino (University of North Carolina at Charlotte)\n------------------
 ---\nP27 - Integrating the ICON4Py Python-Based Dynamical Core into ICON\n
 \nThe integration of Python-based high-performance computing into legacy F
 ortran climate models offers new opportunities for flexibility and efficie
 ncy. This poster presents the integration, in the Fortran ICON implementat
 ion of the dynamical core implemented in Python, as part of ICON4py, a sti
 ll in-...\n\n\nMauro Bianco (ETH Zurich / CSCS), Magdalena Luz (ETH Zurich
 ), Christoph Muller and Daniel Hupp (MeteoSwiss), Anurag Dipankar (ETH Zur
 ich), Edoardo Paone (ETH Zurich / CSCS), Xavier Lapillonne (MeteoSwiss), N
 icoletta Farabullini (ETH Zurich), Enrique Gonzales Pareder and Hannes Vog
 t (ETH Zurich / CSCS), Ong Chia Rui (ETH Zurich), Till Ehrengruber (ETH Zu
 rich / CSCS), Yilu Chen (ETH Zurich), and Philip Muller and Christos Kotsa
 los (ETH Zurich / CSCS)\n---------------------\nP05 - The Chatbot Update S
 ystem (CUS): An Effective Interface to Train AI\n\nWith the rise of AI in 
 many disciplines and the proliferation of chatbots in many applications, v
 arious chatbots need training to properly respond to human users. In this 
 presentation, I report on a chatbot training interface that I developed na
 med CUS, the Chatbot Update System. CUS was developed f...\n\n\nStephen Fr
 ancis (Brigham Young University)\n---------------------\nP31 - Mixed Preci
 sion Customized for Discontinuous Galerkin Methods\n\nWe present an approa
 ch to enhance storage efficiency and reduce memory bandwidth utilization i
 n modal Discontinuous Galerkin (DG) methods by introducing a customized mi
 xed-precision\nrepresentation for the solution vector. Our approach levera
 ges variations in floating-point accuracy requirements amon...\n\n\nShivam
  Sundriyal, Markus Büttner, and Vadym Aizinger (University of Bayreuth)\n-
 --------------------\nP20 - GPU-Accelerated Matrix Decomposition and Selec
 ted Inversion for Banded Arrowhead Matrices\n\nMatrix inversion is a funda
 mental operation in linear algebra which arises in various scientific prob
 lems. Many applications are cast as sparse linear systems, however, when i
 nverted, they produce dense matrices. In some cases, only a subset of the 
 complete inverse—referred to as selected inve...\n\n\nCarla Lopez Zurita (
 ETH Zurich); Lisa Gaedke-Merzhäuser (King Abdullah University of Science a
 nd Technology, Università della Svizzera italiana); Vincent Maillou (ETH Z
 urich); and Olaf Schenk (Università della Svizzera italiana)\n------------
 ---------\nP10 - Electronic Structure Calculations Powered by DLA-Future\n
 \nDLA-future implements efficient multicore and GPU eigenvalue solvers, de
 signed around C++'s std::execution concurrency proposal (P2300) as impleme
 nted in pika. DLA-Future takes advantage of asynchronous task-based progra
 mming, and it is designed to exploit modern heterogeneous architectures. D
 LA-Fut...\n\n\nJohn Biddiscombe, Alberto Invernizzi, Rocco Meli, Auriane R
 everdell, Mikael Simberg, and Raffaele Solcà (ETH Zurich / CSCS)
END:VEVENT
END:VCALENDAR