.png){kind=icon}
返回
A global climate model agent for high spatial and temporal resolution data
.png){kind=icon}
PRE
AI
DOI：10.1177/1094342013518808.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Fine cell granularity in modern climate models can produce terabytes of data in each snapshot, causing significant I/O overhead. To address this issue, a method of reducing the I/O latency of high-resolution climate models by identifying and selectively outputting regions of interest is presented. Working with a global cloud-resolving model and running with up to 10,240 processors on a Cray XE6, this method provides significant I/O bandwidth reduction depending on the frequency of writes and the size of the region of interest. The implementation challenges of determining global parameters in a strictly core-localized model and properly formatting output files that only contain subsections of the global grid are addressed, as well as the overall bandwidth impact and benefits of the method. The gains in I/O throughput provided by this method allow dual output rates for high-resolution climate models: a low-frequency global snapshot as well as a high-frequency regional snapshot when events of particular interest occur.
Keyword:
Global cloud-resolving model
software agent
Hoshen-Kopelman algorithm
parallel clustering
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.5
论文数:
1.1K
被引数:
1.3K
机构
引用论文
.png){kind=icon}
Improving the detection and tracking of tropical cyclones in atmospheric general circulation models
OAAI
OAAI分享收藏Simulation of interannual variability of tropical storm frequency in an ensemble of GCM integrationsOAAI
OAAI分享收藏分享收藏
查看更多内容