001     1041786
005     20250512202214.0
024 7 _ |a 10.34734/FZJ-2025-02426
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037 _ _ |a FZJ-2025-02426
041 _ _ |a English
100 1 _ |a Di Napoli, Edoardo
|0 P:(DE-Juel1)144723
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|u fzj
111 2 _ |a ISC High Performance 2024
|g ISC24
|c Hamburg
|d 2024-05-12 - 2024-05-16
|w Germany
245 _ _ |a Learning Materials at eXascale
260 _ _ |c 2024
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
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336 7 _ |a conferenceObject
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336 7 _ |a CONFERENCE_POSTER
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336 7 _ |a Output Types/Conference Poster
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336 7 _ |a Poster
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520 _ _ |a Solving large and sparse numerical linear systems in materials science on massively parallel supercomputers is a complex endeavour that requires a delicate balance between accuracy and computational efficiency. Challenges include managing the scale and complexity of these systems, optimising scalability on parallel architectures, and addressing real-world material complexities. The LimitX project represents a ground-breaking step in the field of computational Materials Science and aims to develop an innovative recommender system. This system aims to revolutionise the solution of large-scale sparse linear systems by accelerating and scaling the solutions of linear systems so that materials science research can be routinely performed on exascale clusters. At its core, this system relies on a two-pronged approach: first, the development of a spectral predictor system and, second, the use of an extensive database of matrices that encapsulate the essence of surrogate space in the field of materials science. The spectral predictor system is the heart of the recommendation system. It utilises deep learning techniques to predict spectral properties that are crucial for the efficient solution of linear systems. The extensive matrix dataset captures the diversity of spectral patterns that occur in material science calculations. The application of this recommender system promises to be transformative, as it enables simulations with hundreds of thousands of atoms, a feat previously unrealisable on current pre-exascale clusters. The linear scaling of DFT (density functional theory) codes such as BigDFT will enable researchers to simulate and analyse complex material systems with unprecedented accuracy and computational efficiency, opening up new horizons for scientific exploration in this field.Item Type:
536 _ _ |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)
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536 _ _ |a Simulation and Data Laboratory Quantum Materials (SDLQM) (SDLQM)
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|f Simulation and Data Laboratory Quantum Materials (SDLQM)
|x 1
536 _ _ |a Inno4Scale - Innovative Algorithms for Applications on European Exascale Supercomputers (101118139)
|0 G:(EU-Grant)101118139
|c 101118139
|f HORIZON-EUROHPC-JU-2022-ALG-02
|x 2
700 1 _ |a Davidovic, Davor
|0 P:(DE-HGF)0
|b 1
|e Corresponding author
700 1 _ |a Genovese, Luigi
|0 P:(DE-HGF)0
|b 2
856 4 _ |u http://fulir.irb.hr/id/eprint/8922
856 4 _ |u https://juser.fz-juelich.de/record/1041786/files/Limit%20x%20poster%20V3%20Final.pdf
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909 C O |o oai:juser.fz-juelich.de:1041786
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910 1 _ |a Forschungszentrum Jülich
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|6 P:(DE-Juel1)144723
913 1 _ |a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
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|v Enabling Computational- & Data-Intensive Science and Engineering
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915 _ _ |a OpenAccess
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920 1 _ |0 I:(DE-Juel1)JSC-20090406
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980 _ _ |a poster
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)JSC-20090406
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