Performance evaluation of a throughput-aware framework for ensemble data assimilation: the case of NICAM-LETKF
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 communicat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-07-01
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| Series: | Geoscientific Model Development |
| Online Access: | http://www.geosci-model-dev.net/9/2293/2016/gmd-9-2293-2016.pdf |
| Summary: | 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. |
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| ISSN: | 1991-959X 1991-9603 |