Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Geosci. Model Dev., 9, 2293-2300, 2016
https://doi.org/10.5194/gmd-9-2293-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Development and technical paper
05 Jul 2016
Performance evaluation of a throughput-aware framework for ensemble data assimilation: the case of NICAM-LETKF
Hisashi Yashiro1, Koji Terasaki1, Takemasa Miyoshi1,2,3, and Hirofumi Tomita1 1RIKEN Advanced Institute for Computational Science, Kobe, Japan
2Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
3University of Maryland, College Park, Maryland, USA
Abstract. In this paper, we propose the design and implementation of an ensemble data assimilation (DA) framework for weather prediction at a high resolution and with a large ensemble size. We consider the deployment of this framework on the data throughput of file input/output (I/O) and multi-node communication. As an instance of the application of the proposed framework, a local ensemble transform Kalman filter (LETKF) was used with a Non-hydrostatic Icosahedral Atmospheric Model (NICAM) for the DA system. Benchmark tests were performed using the K computer, a massive parallel supercomputer with distributed file systems. The results showed an improvement in total time required for the workflow as well as satisfactory scalability of up to 10 K nodes (80 K cores). With regard to high-performance computing systems, where data throughput performance increases at a slower rate than computational performance, our new framework for ensemble DA systems promises drastic reduction of total execution time.

Citation: Yashiro, H., Terasaki, K., Miyoshi, T., and Tomita, H.: Performance evaluation of a throughput-aware framework for ensemble data assimilation: the case of NICAM-LETKF, Geosci. Model Dev., 9, 2293-2300, https://doi.org/10.5194/gmd-9-2293-2016, 2016.
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Short summary
We propose the design and implementation of an ensemble data assimilation framework for weather prediction at a high resolution and with a large ensemble size. We consider the deployment of this framework on the data throughput of file I/O and multi-node communication. With regard to high-performance computing systems, where data throughput performance increases at a slower rate than computational performance, our new framework promises drastic reduction of total execution time.
We propose the design and implementation of an ensemble data assimilation framework for weather...
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