ALICE分析训练系统
The ALICE analysis train system
摘要：
在ALICE实验中，数百名用户正在网格系统上分析大型数据集。高吞吐量和短周转时间是由一个集中的系统，称为乐高列车。该系统将来自不同用户的分析组合在所谓的分析训练中，然后在相同的网格作业中执行，从而减少了需要从存储系统读取数据的次数。与单用户分析相比，集中式训练提高了性能、用户可用性和簿记。训练系统建立在现有的ALICE工具上，即分析框架以及网格提交和监控基础设施。训练系统的入口点是用于配置分析和所需数据集以及测试和提交训练的网络接口。已经实施了若干措施来减少训练需要完成的时间并提高CPU效率。
英文摘要：
In the ALICE experiment hundreds of users are analyzing big datasets on a Grid system. High throughput and short turn-around times are achieved by a centralized system called the LEGO trains. This system combines analysis from different users in so-called analysis trains which are then executed within the same Grid jobs thereby reducing the number of times the data needs to be read from the storage systems. The centralized trains improve the performance, the usability for users and the bookkeeping in comparison to single user analysis. The train system builds upon the already existing ALICE tools, i.e. the analysis framework as well as the Grid submission and monitoring infrastructure. The entry point to the train system is a web interface which is used to configure the analysis and the desired datasets as well as to test and submit the train. Several measures have been implemented to reduce the time a train needs to finish and to increase the CPU efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
