Explore New Computing Environment for LHAASO Offline Data Analysis

Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD) Pub Date : 2018-12-12 DOI:10.22323/1.327.0021

Qiulan Huang, Gongxing Sun, Qiao Yin, Zhanchen Wei, Qiang Li

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Abstract

This paper explores a way to build a new computing environment based on Hadoop to make the Large High Altitude Air Shower Observatory(LHAASO) jobs run on it transparently. Particularly, we discuss a new mechanism to support LHAASO software to random access data in HDFS. This new feature allows the Map/Reduce tasks to random read/write data on the local file system instead of using Hadoop data streaming interface. This makes HEP jobs run on Hadoop possible. We also develop MapReduce patterns for LHAASO jobs such as Corsika simulation, ARGO detector simulation (Geant4), KM2A simulation and Medea++ reconstruction. And user-friendly interface is provided. In addition, we provide the real-time cluster monitoring in terms of cluster healthy, number of running jobs, finished jobs and killed jobs. Also the accounting system is included. This work has been in production for LHAASO offline data analysis to gain about 20,000 CPU hours per month since September, 2016. The results show the efficiency of IO intensive job can be improved about 46%. Finally, we describe our ongoing work of data migration tool to serve the data move between HDFS and other storage systems.

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探索LHAASO离线数据分析的新计算环境

本文探讨了一种基于Hadoop构建新的计算环境的方法，使大型高空风淋观测站(Large High Altitude Air Shower Observatory, LHAASO)的作业能够透明地在Hadoop上运行。特别地，我们讨论了一种支持LHAASO软件随机访问HDFS数据的新机制。这个新特性允许Map/Reduce任务在本地文件系统上随机读取/写入数据，而不是使用Hadoop数据流接口。这使得在Hadoop上运行HEP作业成为可能。我们还开发了用于LHAASO作业的MapReduce模式，如Corsika模拟、ARGO探测器模拟(Geant4)、KM2A模拟和Medea++重建。并提供用户友好的界面。此外，我们还提供实时集群监控，包括集群运行状况、正在运行的作业数量、已完成的作业和已终止的作业。会计系统也包括在内。该工作已投入生产，用于LHAASO离线数据分析，自2016年9月起每月获得约20,000 CPU小时。结果表明，IO密集型作业的效率可提高约46%。最后，我们描述了我们正在进行的数据迁移工具，以服务于HDFS和其他存储系统之间的数据迁移。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)

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