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使用Hadoop MapReduce进行分布式X射线光子相关光谱数据约简

Distributed X-ray photon correlation spectroscopy data reduction using Hadoop MapReduce.

作者信息

Khan Faisal, Narayanan Suresh, Sersted Roger, Schwarz Nicholas, Sandy Alec

机构信息

X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA.

APS Engineering Support, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA.

出版信息

J Synchrotron Radiat. 2018 Jul 1;25(Pt 4):1135-1143. doi: 10.1107/S160057751800601X. Epub 2018 Jun 14.

Abstract

Multi-speckle X-ray photon correlation spectroscopy (XPCS) is a powerful technique for characterizing the dynamic nature of complex materials over a range of time scales. XPCS has been successfully applied to study a wide range of systems. Recent developments in higher-frame-rate detectors, while aiding in the study of faster dynamical processes, creates large amounts of data that require parallel computational techniques to process in near real-time. Here, an implementation of the multi-tau and two-time autocorrelation algorithms using the Hadoop MapReduce framework for distributed computing is presented. The system scales well with regard to the increase in the data size, and has been serving the users of beamline 8-ID-I at the Advanced Photon Source for near real-time autocorrelations for the past five years.

摘要

多散斑X射线光子相关光谱学(XPCS)是一种强大的技术,可用于在一系列时间尺度上表征复杂材料的动态特性。XPCS已成功应用于研究广泛的系统。更高帧率探测器的最新发展,虽然有助于研究更快的动态过程,但也产生了大量数据,需要并行计算技术进行近实时处理。在此,介绍了一种使用Hadoop MapReduce框架进行分布式计算的多延迟和双时自相关算法的实现。该系统在数据量增加方面具有良好的扩展性,并且在过去五年中一直在为先进光子源的8-ID-I光束线用户提供近实时自相关服务。

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