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Reproducibility in Practice: Lessons Learned from Research and Teaching Experiments

机译:实践中的再现性:从研究和教学实验中汲取的经验教训

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Nowadays computer systems, with the new multi-core architectures comprising accelerators such as GPU and Intel Xeon Phi, have a high complexity and are exclusively targeting performance: it becomes more and more difficult for scientists to preserve the context of their experiments and let them be reproducible by others. In the previous years researchers mainly focused on their own work, not caring about letting it be useful for the others and for science to move forward. The majority of the experiments on parallel computers have been reported at conferences and in journals usually without the possibility to verify the results presented. While this is still the state-of-the-art, current research targets for solutions to this problem. We discuss early results regarding our workflow system based approach, implemented in order to address the reproducibility problem in the context of high performance computation. We used our framework to reproduce the results of some papers and classroom code. In order to allow an easy interface to our system and let it be accessible from everywhere we set up a web application.
机译:如今计算机系统,新的多核架构包括GPU和英特尔Xeon Phi等加速器,具有很高的复杂性,并且专门针对性能:科学家对他们的实验的背景变得越来越困难,让他们成为其他人可重复。在前几年的研究人员主要专注于自己的工作,不关心让它对其他人有用,并为科学向前发展。在会议和期刊上报告了关于并行计算机的大部分实验,通常情况下,通常可以验证所提出的结果。虽然这仍然是最先进的,目前对这个问题的解决方案的研究目标。我们讨论了关于我们基于工作流系统的方法的早期结果,以便在高性能计算的背景下解决重现性问题。我们利用我们的框架重现了一些论文和课堂代码的结果。为了允许对我们的系统进行简单的界面并让您可以从我们设置Web应用程序的到处访问。

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