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首页> 外文期刊>Journal of systems and software >Building problem-solving environments with the Arches framework
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Building problem-solving environments with the Arches framework

机译：使用Arches框架构建解决问题的环境

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摘要

The computational problems that scientists face are rapidly escalating in size and scope. Moreover, the computer systems used to solve these problems are becoming significantly more complex than the familiar, well-understood sequential model on their desktops. While it is possible to re-train scientists to use emerging high-performance computing (HPC) models, it is much more effective to provide them with a higher-level programming environment that has been specialized to their particular domain. By fostering interaction between HPC specialists and the domain scientists, problem-solving environments (PSEs) provide a collaborative environment. A PSE environment allows scientists to focus on expressing their computational problem while the PSE and associated tools support mapping that domain-specific problem to a high-performance computing system.rnThis article describes Arches, an object-oriented framework for building domain-specific PSEs. The framework was designed to support a wide range of problem domains and to be extensible to support very different high-performance computing targets. To demonstrate this flexibility, two PSEs have been developed from the Arches framework to solve problem in two different domains and target very different computing platforms. The Coven PSE supports parallel applications that require large-scale parallelism found in cost-effective Beowulf clusters. In contrast, RCADE targets FPGA-based reconfigurable computing and was originally designed to aid NASA Earth scientists studying satellite instrument data.
机译：科学家面临的计算问题的规模和范围正在迅速扩大。而且，用于解决这些问题的计算机系统比其台式机上熟悉的，易于理解的顺序模型变得更加复杂。尽管可以对科学家进行再培训以使用新兴的高性能计算（HPC）模型，但为他们提供针对其特定领域的高级编程环境要有效得多。通过促进HPC专家和领域科学家之间的互动，问题解决环境（PSE）提供了一个协作环境。 PSE环境允许科学家专注于表达他们的计算问题，而PSE和相关工具则支持将特定于领域的问题映射到高性能计算系统。本文介绍了Arches，这是一种用于构建特定于领域的PSE的面向对象的框架。该框架旨在支持广泛的问题领域，并且可扩展以支持非常不同的高性能计算目标。为了证明这种灵活性，已从Arches框架开发了两个PSE，以解决两个不同领域中的问题并针对非常不同的计算平台。 Coven PSE支持需要在具有成本效益的Beowulf集群中发现的大规模并行性的并行应用程序。相比之下，RCADE的目标是基于FPGA的可重构计算，最初旨在帮助NASA地球科学家研究卫星仪器数据。

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