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GPAW - massively parallel electronic structure calculations with Python-based software

机译:GpaW - 使用基于python的软件进行大规模并行电子结构计算

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摘要

Electronic structure calculations are a widely used tool in materials science and large consumer of supercomputing resources. Traditionally, the software packages for these kind of simulations have been implemented in compiled languages, where Fortran in its different versions has been the most popular choice. While dynamic, interpreted languages, such as Python, can increase the effciency of programmer, they cannot compete directly with the raw performance of compiled languages. However, by using an interpreted language together with a compiled language, it is possible to have most of the productivity enhancing features together with a good numerical performance. We have used this approach in implementing an electronic structure simulation software GPAW using the combination of Python and C programming languages. While the chosen approach works well in standard workstations and Unix environments, massively parallel supercomputing systems can present some challenges in porting, debugging and profiling the software. In this paper we describe some details of the implementation and discuss the advantages and challenges of the combined Python/C approach. We show that despite the challenges it is possible to obtain good numerical performance and good parallel scalability with Python based software.
机译:电子结构计算是材料科学中的一种广泛使用的工具,并且是超级计算资源的大量消耗者。传统上,用于这类模拟的软件包是用编译语言实现的,其中不同版本的Fortran是最受欢迎的选择。尽管动态的解释型语言(例如Python)可以提高程序员的效率,但它们不能直接与编译语言的原始性能竞争。但是,通过将解释性语言与编译语言一起使用,可以具有大多数提高生产率的功能以及良好的数值性能。我们已经使用这种方法结合Python和C编程语言来实现电子结构仿真软件GPAW。尽管选择的方法在标准工作站和Unix环境中效果很好,但是大规模并行超级计算系统在移植,调试和配置软件方面可能会遇到一些挑战。在本文中,我们描述了实现的一些细节,并讨论了Python / C组合方法的优点和挑战。我们证明,尽管存在挑战,但使用基于Python的软件仍可以获得良好的数值性能和良好的并行可伸缩性。

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