High-Performance Computing for Earth System Modeling

2020 ◽  
pp. 175-184
Author(s):  
Dali Wang ◽  
Fengming Yuan
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
System Modeling ◽  
Earth System ◽  
Earth System Modeling ◽  
Performance Computing

Coupled earth system modeling on heterogeneous HPC architectures with ParFlow in the Terrestrial Systems Modeling Platform

2021 ◽  
Author(s):  
Jaro Hokkanen ◽  
Stefan Kollet ◽  
Jiri Kraus ◽  
Andreas Herten ◽  
Markus Hrywniak ◽  
...  
Keyword(s):  
Land Surface ◽  
Memory Management ◽  
High Performance ◽  
Data Exchange ◽  
System Modeling ◽  
Hydrologic Model ◽  
Earth System ◽  
Single Node ◽  
Earth System Modeling ◽  
Domain Specific

<p>Rapidly changing heterogeneous supercomputer architectures pose a great challenge to many scientific communities trying to leverage the latest technology in high-performance computing. Implementations that simultaneously result in a good performance and developer productivity while keeping the codebase adaptable and well maintainable in the long-term are of high importance. ParFlow, a widely used hydrologic model, achieves these attributes by hiding the architecture-dependent code in preprocessor macros (ParFlow embedded Domain Specific Language, eDSL) and leveraging NVIDIA's Unified Memory technology for memory management. The implementation results in very good weak scaling with up to 26x speedup when using four NVIDIA A100 GPUs per node compared to using the available 48 CPU cores. Good weak scaling is observed using hundreds of nodes on the new JUWELS Booster system at the Jülich Supercomputing Centre, Germany. Furthermore, it is possible to couple ParFlow with other earth system compartment models such as land surface and atmospheric models using the OASIS-MCT coupler library, which handles the data exchange between the different models. The ParFlow GPU implementation is fully compatible with the coupled implementation with little changes to the source code. Moreover, coupled simulations offer interesting load-balancing opportunities for optimal usage of the existing resources. For example, running ParFlow on GPU nodes, and another application component on CPU-only nodes, or efficiently distributing the CPU and GPU resources of a single node between the different application components may result in the best usage of heterogeneous architectures.</p>

High-performance Earth system modeling with NASA/GSFC’s Land Information System

2007 ◽  
Vol 3 (3) ◽  
pp. 157-165 ◽  
Author(s):  
C. D. Peters-Lidard ◽  
P. R. Houser ◽  
Y. Tian ◽  
S. V. Kumar ◽  
J. Geiger ◽  
...  
Keyword(s):  
Information System ◽  
High Performance ◽  
System Modeling ◽  
Earth System ◽  
Earth System Modeling ◽  
Land Information System

Boosting climate change research with direct access to high performance computers

2020 ◽  
Author(s):  
Maria Moreno de Castro ◽  
Stephan Kindermann ◽  
Sandro Fiore ◽  
Paola Nassisi ◽  
Guillaume Levavasseur ◽  
...  
Keyword(s):  
Climate Change ◽  
High Performance Computing ◽  
High Performance ◽  
System Modelling ◽  
Earth System ◽  
Climate Data ◽  
Climate Change Research ◽  
Detection And Attribution ◽  
Wide Range ◽  
Performance Computing

<p>Earth System observational and model data volumes are constantly increasing and it can be challenging to discover, download, and analyze data if scientists do not have the required computing and storage resources at hand. This is especially the case for detection and attribution studies in the field of climate change research since we need to perform multi-source and cross-disciplinary comparisons for datasets of high-spatial and large temporal coverage. Researchers and end-users are therefore looking for access to cloud solutions and high performance compute facilities. The Earth System Grid Federation (ESGF, https://esgf.llnl.gov/) maintains a global system of federated data centers that allow access to the largest archive of model climate data world-wide. ESGF portals provide free access to the output of the data contributing to the next assessment report of the Intergovernmental Panel on Climate Change through the Coupled Model Intercomparison Project. In order to support users to directly access to high performance computing facilities to perform analyses such as detection and attribution of climate change and its impacts, the EU Commission funded a new service within the infrastructure of the European Network for Earth System Modelling (ENES, https://portal.enes.org/data/data-metadata-service/analysis-platforms). This new service is designed to reduce data transfer issues, speed up the computational analysis, provide storage, and ensure the resources access and maintenance. Furthermore, the service is free of charge, only requires a lightweight application. We will present a demo on how flexible it is to calculate climate indices from different ESGF datasets covering a wide range of temporal and spatial scales using cdo (Climate Data Operators, https://code.mpimet.mpg.de/projects/cdo/) and Jupyter notebooks running directly on the ENES partners: the DKRZ (Germany), JASMIN (UK), CMCC(Italy), and IPSL (France) high performance computing centers.</p>

The Recent Revolution in High Performance Computing

MRS Bulletin ◽  
1997 ◽  
Vol 22 (10) ◽  
pp. 5-6
Author(s):  
Horst D. Simon
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
New Technologies ◽  
New Technology ◽  
Parallel Architecture ◽  
Time Frame ◽  
Good News ◽  
Computing Industry ◽  
Time And Energy ◽  
Performance Computing

Recent events in the high-performance computing industry have concerned scientists and the general public regarding a crisis or a lack of leadership in the field. That concern is understandable considering the industry's history from 1993 to 1996. Cray Research, the historic leader in supercomputing technology, was unable to survive financially as an independent company and was acquired by Silicon Graphics. Two ambitious new companies that introduced new technologies in the late 1980s and early 1990s—Thinking Machines and Kendall Square Research—were commercial failures and went out of business. And Intel, which introduced its Paragon supercomputer in 1994, discontinued production only two years later.During the same time frame, scientists who had finished the laborious task of writing scientific codes to run on vector parallel supercomputers learned that those codes would have to be rewritten if they were to run on the next-generation, highly parallel architecture. Scientists who are not yet involved in high-performance computing are understandably hesitant about committing their time and energy to such an apparently unstable enterprise.However, beneath the commercial chaos of the last several years, a technological revolution has been occurring. The good news is that the revolution is over, leading to five to ten years of predictable stability, steady improvements in system performance, and increased productivity for scientific applications. It is time for scientists who were sitting on the fence to jump in and reap the benefits of the new technology.

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