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Empirical study design in the area of high-performance computing (HPC)

机译:高性能计算面积的经验研究设计(HPC)

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The development of high-performance computing (HPC) programs is crucial to progress in many fields of scientific endeavor. We have run initial studies of the productivity of HPC developers and of techniques for improving that productivity, which have not previously been the subject of significant study. Because of key differences between development for HPC and for more conventional software engineering applications, this work has required the tailoring of experimental designs and protocols. A major contribution of our work is to begin to quantify the code development process in a specialized area that has previously not been extensively studied. Specifically, we present an analysis of the domain of high-performance computing for the aspects that would impact experimental design; show how those aspects are reflected in experimental design for this specific area; and demonstrate how we are using such experimental designs to build up a body of knowledge specific to the domain. Results to date build confidence in our approach by showing that there are no significant differences across studies comparing subjects with similar experience tackling similar problems, while there are significant differences in performance and effort among the different parallel models applied.
机译:高性能计算(HPC)计划的发展对于在许多科学努力领域进展至关重要。我们已经运行了对HPC开发人员的生产率和改善生产力的技术的初步研究,以提高其尚未成为重要研究的主题。由于HPC的开发和更传统的软件工程应用之间的关键差异,这项工作需要剪裁实验设计和协议。我们的工作的主要贡献是开始量化以前未经广泛研究的专业领域的代码开发过程。具体地,我们对影响实验设计的方面的高性能计算领域的分析;展示这些方面如何反映在该特定区域的实验设计中;并展示我们如何使用这种实验设计来构建特定于域的知识体。结果迄今为止,通过表明,在研究具有类似体验的类似经验的研究中没有显着差异,可以在研究中进行解决方案,而不同的并行模型之间存在显着差异。

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