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Advancing throughput of HEP analysis work-flows using caching concepts

机译:使用缓存概念提高HEP分析工作流程的吞吐量

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High throughput and short turnaround cycles are core requirements for efficient processing of data-intense end-user analyses in High Energy Physics (HEP). Together with the tremendously increasing amount of data to be processed, this leads to enormous challenges for HEP storage systems, networks and the data distribution to computing resources for end-user analyses. Bringing data close to the computing resource is a very promising approach to solve throughput limitations and improve the overall performance. However, achieving data locality by placing multiple conventional caches inside a distributed computing infrastructure leads to redundant data placement and inefficient usage of the limited cache volume. The solution is a coordinated placement of critical data on computing resources, which enables matching each process of an analysis work-flow to its most suitable worker node in terms of data locality and, thus, reduces the overall processing time. This coordinated distributed caching concept was realized at KIT by developing the coordination service NaviX that connects an XRootD cache proxy infrastructure with an HTCondor batch system. We give an overview about the coordinated distributed caching concept and experiences collected on prototype system based on NaviX.
机译:高通量和短周转周期是高效处理高能物理(HEP)中数据密集型最终用​​户分析的核心要求。再加上要处理的数据量大大增加,这给HEP存储系统,网络以及将数据分发到计算资源以进行最终用户分析带来了巨大挑战。将数据靠近计算资源是解决吞吐量限制和提高整体性能的非常有前途的方法。但是,通过在分布式计算基础结构中放置多个常规缓存来实现数据局部性会导致冗余数据放置以及有限缓存量的低效使用。解决方案是将关键数据协调地放置在计算资源上,从而使分析工作流的每个过程都可以在数据局部性方面与其最合适的工作程序节点相匹配,从而减少了总体处理时间。通过开发将XRootD缓存代理基础结构与HTCondor批处理系统连接起来的协调服务NaviX,在KIT上实现了这种协调的分布式缓存概念。我们概述了在基于NaviX的原型系统上收集的分布式分布式缓存概念和经验。

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