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On the use of models for high-performance scientific computing applications: an experience report

机译:关于将模型用于高性能科学计算应用程序的经验报告

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This paper reports on a four-year project that aims to raise the abstraction level through the use of model-driven engineering (MDE) techniques in the development of scientific applications relying on high-performance computing. The development and maintenance of high-performance scientific computing software is reputedly a complex task. This complexity results from the frequent evolutions of supercomputers and the tight coupling between software and hardware aspects. Moreover, current parallel programming approaches result in a mixing of concerns within the source code. Our approach relies on the use of MDE and consists in defining domain-specific modeling languages targeting various domain experts involved in the development of HPC applications, allowing each of them to handle their dedicated model in a both user-friendly and hardware-independent way. The different concerns are separated thanks to the use of several models as well as several modeling viewpoints on these models. Depending on the targeted execution platforms, these abstract models are translated into executable implementations by means of model transformations. To make all of these effective, we have developed a tool chain that is also presented in this paper. The approach is assessed through a multi-dimensional validation that focuses on its applicability, its expressiveness and its efficiency. To capitalize on the gained experience, we analyze some lessons learned during this project.
机译:本文报告了一个为期四年的项目,该项目旨在在依靠高性能计算的科学应用程序开发中通过使用模型驱动工程(MDE)技术来提高抽象水平。高性能科学计算软件的开发和维护据说是一项复杂的任务。这种复杂性是由于超级计算机的频繁发展以及软件和硬件方面之间的紧密耦合而导致的。而且,当前的并行编程方法导致源代码中的关注点混合。我们的方法依赖于MDE的使用,在于定义针对特定领域的建模语言,这些语言针对参与HPC应用程序开发的各个领域专家,从而使他们每个人都可以以用户友好和独立于硬件的方式处理其专用模型。由于使用了多个模型以及这些模型上的多个建模观点,因此将不同的关注点分离了。根据目标执行平台的不同,这些抽象模型通过模型转换被转换为可执行的实现。为了使所有这些有效,我们已经开发了一个工具链,本文也介绍了该工具链。该方法是通过多维验证来评估的,重点是其适用性,表达性和效率。为了利用获得的经验,我们分析了在该项目中获得的一些经验教训。

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