Allegra De Filippo, Emanuele Di Giacomo, Andrea Borghesi
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In this work, we focus on the runtime predictions of the execution of the COSMO (COnsortium for SMall-scale MOdeling) weather forecasting model used at the Hydro-Meteo-Climate Structure of the Regional Agency for the Environment and Energy Prevention Emilia-Romagna. We show how a plethora of Machine Learning approaches can obtain accurate runtime predictions of this complex model, by designing a new well-defined benchmark for this application task. Indeed, our contribution is twofold: 1) the creation of a large public dataset reporting the runtime of COSMO run under a variety of different configurations; 2) a comparative study of ML models, which greatly outperform the current state-of-practice used by the domain experts. 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引用次数: 0
摘要
预测天气预报模型的执行时间是一项复杂的任务,因为这些模型通常是在需要大型计算能力的高性能计算系统上执行的。事实上,可靠的预测可以带来多种益处,包括改进模型执行计划、更好地分配可用资源以及识别可能的异常情况。然而,要做出这样的预测通常很难,因为现有的气象模拟模型缺乏基准数据集。在这项工作中,我们重点研究了艾米利亚-罗马涅大区环境和能源预防局水文气象气候结构使用的 COSMO(小尺度模拟联盟)天气预报模型的运行预测。我们通过为这一应用任务设计一个定义明确的新基准,展示了大量机器学习方法如何在运行时对这一复杂模型进行准确预测。事实上,我们的贡献是双重的:1)创建了一个大型公共数据集,报告 COSMO 在各种不同配置下的运行时间;2)对 ML 模型进行比较研究,这些模型大大优于领域专家目前使用的实践状态。这些数据收集是这一应用领域的重要初始基准,也是分析模型性能的有用资源:更准确的运行时间预测可以帮助设施所有者改进整个系统的作业调度和资源分配;而对于最终用户来说,后验分析可以帮助识别异常运行。
Machine learning approaches to predict the execution time of the meteorological simulation software COSMO
Predicting the execution time of weather forecast models is a complex task, since these models are usually performed on High Performance Computing systems that require large computing capabilities. Indeed, a reliable prediction can imply several benefits, by allowing for an improved planning of the model execution, a better allocation of available resources, and the identification of possible anomalies. However, to make such predictions is usually hard, since there is a scarcity of datasets that benchmark the existing meteorological simulation models. In this work, we focus on the runtime predictions of the execution of the COSMO (COnsortium for SMall-scale MOdeling) weather forecasting model used at the Hydro-Meteo-Climate Structure of the Regional Agency for the Environment and Energy Prevention Emilia-Romagna. We show how a plethora of Machine Learning approaches can obtain accurate runtime predictions of this complex model, by designing a new well-defined benchmark for this application task. Indeed, our contribution is twofold: 1) the creation of a large public dataset reporting the runtime of COSMO run under a variety of different configurations; 2) a comparative study of ML models, which greatly outperform the current state-of-practice used by the domain experts. This data collection represents an essential initial benchmark for this application field, and a useful resource for analyzing the model performance: better accuracy in runtime predictions could help facility owners to improve job scheduling and resource allocation of the entire system; while for a final user, a posteriori analysis could help to identify anomalous runs.
期刊介绍:
The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems.
These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to:
discover knowledge from large data collections,
provide cooperative support to users in complex query formulation and refinement,
access, retrieve, store and manage large collections of multimedia data and knowledge,
integrate information from multiple heterogeneous data and knowledge sources, and
reason about information under uncertain conditions.
Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces.
The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.