exascale自主性能环境的早期原型

K. Huck, S. Shende, A. Malony, Hartmut Kaiser, Allan Porterfield, R. Fowler, R. Brightwell
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引用次数: 20

摘要

极端规模计算需要从新的角度来看待性能观察在Exascale系统软件堆栈中的作用。由于这些系统中预期的高并发性和动态操作,期望事后性能度量和分析方法就足够了已经不再合理。相反,我们强烈需要将第一人称和第三人称观察、现场分析和跨堆栈层的内省结合起来的性能观察,以提供在线动态反馈和自适应。在本文中,我们描述了美国能源部资助的XPRESS项目以及自主性能支持在Exascale系统中的作用。XPRESS将构建一个集成的Exascale软件栈(称为OpenX),支持ParalleX执行模型,并针对未来的Exascale平台。使用当前的TAU性能技术为OpenX开发了一个名为APEX的自主性能环境的初始版本,其结果突出了高度集成观察和运行时分析的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An early prototype of an autonomic performance environment for exascale
Extreme-scale computing requires a new perspective on the role of performance observation in the Exascale system software stack. Because of the anticipated high concurrency and dynamic operation in these systems, it is no longer reasonable to expect that a post-mortem performance measurement and analysis methodology will suffice. Rather, there is a strong need for performance observation that merges first-and third-person observation, in situ analysis, and introspection across stack layers that serves online dynamic feedback and adaptation. In this paper we describe the DOE-funded XPRESS project and the role of autonomic performance support in Exascale systems. XPRESS will build an integrated Exascale software stack (called OpenX) that supports the ParalleX execution model and is targeted towards future Exascale platforms. An initial version of an autonomic performance environment called APEX has been developed for OpenX using the current TAU performance technology and results are presented that highlight the challenges of highly integrative observation and runtime analysis.
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