Further Improving the Scalability of the Scalasca Toolset
Further Improving the Scalability of the Scalasca Toolset
复制标题
进一步提高 Scalasca 工具集的可扩展性
DOI：
10.1007/978-3-642-28145-7_45
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
B. Wylie
中科院分区：
文献类型：
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作者：
M. Geimer;P. Saviankou;A. Strube;Z. Szebenyi;F. Wolf;B. Wylie
Scalasca是一个开源工具集，可用于分析并行应用程序的性能行为并确定优化机会。目标应用包括基于并行编程接口MPI和/或OpenMP的来自科学和工程的仿真代码。Scalasca是专门为在IBM Blue Gene和Cray XT等大型计算机上使用而设计的，它集成了适合获取性能概述的运行时摘要，并通过事件跟踪对并发行为进行了深入研究。虽然Scalasca已经在72机架Blue Gene/P系统上成功地与294,912个内核运行的代码一起使用，但当前的软件设计显示出可伸缩性限制，对用户体验产生不利影响，这将在未来掌握更大规模的道路上构成严重障碍。在本文中，我们概述了如何解决两个最重要的问题，即测量结束时本地标识符的统一以及分析报告的整理和显示。
Scalasca is an open-source toolset that can be used to analyze the performance behavior of parallel applications and to identify opportunities for optimization. Target applications include simulation codes from science and engineering based on the parallel programming interfaces MPI and/or OpenMP. Scalasca, which has been specifically designed for use on large-scale machines such as IBM Blue Gene and Cray XT, integrates runtime summaries suitable to obtain a performance overview with in-depth studies of concurrent behavior via event tracing. Although Scalasca was already successfully used with codes running with 294,912 cores on a 72-rack Blue Gene/P system, the current software design shows scalability limitations that adversely affect user experience and that will present a serious obstacle on the way to mastering larger scales in the future. In this paper, we outline how to address the two most important ones, namely the unification of local identifiers at measurement finalization as well as collating and displaying analysis reports.