构建更少的代码交付更多的科学:一份使用基于组件和商品软件平台组合科学环境的经验报告

I. Gorton, Yan Liu, C. Lansing, T. Elsethagen, K. K. Dam
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引用次数: 13

摘要

现代科学软件的多样性和复杂性令人生畏。从在世界上最大的超级计算机上运行的大规模并行模拟,到管理不断增长的复杂数据集的可视化和用户支持环境,软件工程师面临的挑战是丰富的。虽然高性能模拟器是在特定的超级计算机架构上最大化性能的必要专用代码,但我们认为绝大多数支持基础设施、数据管理和分析工具可以利用商品开源和基于组件的技术。这种方法可以显著降低构建复杂、协作的科学用户环境的工作量和成本,并提高它们的可靠性和可扩展性。在本文中,我们描述了我们在为参与气候变化对选定地理区域环境的详细影响建模的科学家创建初始用户环境方面的经验。我们的方法使用Velo科学知识管理平台和MeDICi科学工作流集成框架组成用户环境。这些已建立的平台利用了基于组件的技术,并通过抽象和功能扩展了商品开源平台,使它们能够在科学中广泛使用。使用这种方法,我们能够交付一个可操作的用户环境,能够在7个月的时间内运行数千次模拟,并实现重要的软件重用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Build less code deliver more science: an experience report on composing scientific environments using component-based and commodity software platforms
Modern scientific software is daunting in its diversity and complexity. From massively parallel simulations running on the world's largest supercomputers, to visualizations and user support environments that manage ever growing complex data collections, the challenges for software engineers are plentiful. While high performance simulators are necessarily specialized codes to maximize performance on specific supercomputer architectures, we argue the vast majority of supporting infrastructure, data management and analysis tools can leverage commodity open source and component-based technologies. This approach can significantly drive down the effort and costs of building complex, collaborative scientific user environments, as well as increase their reliability and extensibility. In this paper we describe our experiences in creating an initial user environment for scientists involved in modeling the detailed effects of climate change on the environment of selected geographical regions. Our approach composes the user environment using the Velo scientific knowledge management platform and the MeDICi Integration Framework for scientific workflows. These established platforms leverage component-based technologies and extend commodity open source platforms with abstractions and capabilities that make them amenable for broad use in science. Using this approach we were able to deliver an operational user environment capable of running thousands of simulations in a 7 month period, and achieve significant software reuse.
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