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| 001 | 1022046 | ||
| 005 | 20240130202646.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2024-01183 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2024-01183 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Tashakor, Ghazal |0 P:(DE-Juel1)188963 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a HiPEAC |c Munich |d 2024-01-16 - 2024-01-19 |w Germany |
| 245 | _ | _ | |a Towards Exascale through Modularity And I/O Management, Performance study under Modular computing with TSMP |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
| 336 | 7 | _ | |a Output Types/Conference Poster |2 DataCite |
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| 500 | _ | _ | |a Platform link:https://www.hipeac.net/2024/munich/#/program/ |
| 520 | _ | _ | |a This abstract highlights the pivotal role of the Terrestrial Systems Modelling Platform (TSMP) as a core use case in two pioneering projects, DEEP-SEA and IO-SEA, within the context of the Modular Supercomputing Architecture (MSA). The MSA, developed throughout the DEEP projects, serves as a blueprint for heterogeneous HPC systems, promoting the highest efficiency and scalability by integrating different compute modules tailored to specific performance characteristics for diverse workloads.The Terrestrial Systems Modelling Platform (TSMP) stands as a versatile, fully coupled Earth system model designed for regional simulations, emphasizing complex interactions within the geo-ecosystem. As an open community code, TSMP integrates various atmospheric models like COSMO and ICON, the Community Land Model (CLM), and the hydrological model ParFlow. With modular coupling, TSMP supports multiple programming languages, parallelization schemes, and hardware architectures.In the DEEP-SEA project, TSMP is utilized as a key use case to address the challenges of exascale computing within the MSA. The modular approach of the MSA aligns with TSMP's capabilities, allowing for the execution of different components, such as CPU and GPU-enabled COSMO, CUDA-ported ParFlow, and Fortran-based CLM, on the most suitable platform. The focus within DEEP-SEA is on mapping these specific components to optimize memory usage and scalability. This approach enables TSMP to conduct simulations at unprecedented resolutions and speeds, leveraging the innovative capabilities of the DEEP infrastructure.Simultaneously, in the IO-SEA project, TSMP serves as a foundational use case to drive advancements in data-centric I/O and workflow execution within the MSA. The adaptability and scalability of the MSA align with the goals of IO-SEA in optimizing data-centric workflows. The project extends existing I/O instrumentation tools, such as Smart burst buffering (SBB) and Data Access and Storage application Interface (DASI), to accommodate the intricacies of TSMP's coupled workflow.In conclusion, the integration of TSMP within the DEEP projects exemplifies a collaborative effort to address the challenges of exascale computing within the innovative Modular Supercomputing Architecture. TSMP's coupled approach aligns seamlessly with the adaptability and scalability of the MSA, allowing for enhanced efficiency and performance in the rapidly evolving landscape of heterogeneous HPC systems. This collaborative effort serves as a model for leveraging versatile Earth system models to improve energy and resource use efficiency in cutting-edge supercomputing infrastructures. |
| 536 | _ | _ | |a 2A5 - Exascale Earth System Modeling (CARF - CCA) (POF4-2A5) |0 G:(DE-HGF)POF4-2A5 |c POF4-2A5 |f POF IV |x 0 |
| 536 | _ | _ | |a 2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217) |0 G:(DE-HGF)POF4-2173 |c POF4-217 |f POF IV |x 1 |
| 650 | 2 | 7 | |a Geosciences |0 V:(DE-MLZ)SciArea-140 |2 V:(DE-HGF) |x 0 |
| 650 | 2 | 7 | |a Instrument and Method Development |0 V:(DE-MLZ)SciArea-220 |2 V:(DE-HGF) |x 1 |
| 650 | 2 | 7 | |a Others |0 V:(DE-MLZ)SciArea-250 |2 V:(DE-HGF) |x 2 |
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| 650 | 1 | 7 | |a Energy |0 V:(DE-MLZ)GC-110 |2 V:(DE-HGF) |x 1 |
| 650 | 1 | 7 | |a Engineering, Industrial Materials and Processing |0 V:(DE-MLZ)GC-1601-2016 |2 V:(DE-HGF) |x 2 |
| 650 | 1 | 7 | |a Instrument and Method Development |0 V:(DE-MLZ)GC-2002-2016 |2 V:(DE-HGF) |x 3 |
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| 693 | _ | _ | |0 EXP:(DE-MLZ)SCG-20150203 |5 EXP:(DE-MLZ)SCG-20150203 |e SCG: Scientific Computing Group |x 0 |
| 700 | 1 | _ | |a Caviedes Voullieme, Daniel |0 P:(DE-Juel1)186723 |b 1 |e Contributor |u fzj |
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| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)188963 |
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| 913 | 1 | _ | |a DE-HGF |b Forschungsbereich Erde und Umwelt |l COOPERATION ACROSS RESEARCH FIELDS (CARFs) |1 G:(DE-HGF)POF4-2A0 |0 G:(DE-HGF)POF4-2A5 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-200 |4 G:(DE-HGF)POF |v Exascale Earth System Modeling (CARF - CCA) |x 0 |
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| 914 | 1 | _ | |y 2024 |
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