Off-Road Performance Modeling - How to Deal with Segmented Data

Off-Road Performance Modeling - How to Deal with Segmented Data
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越野性能建模 - 如何处理分段数据

DOI:
10.1007/978-3-319-64203-1_3
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发表时间:
2017
期刊:
影响因子:
--
通讯作者:
Wolf F.
Wolf F.
中科院分区:
--
文献类型:
--
作者:
Ilyas M.K;Calotoiu A;Wolf F.

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除了正确性,可伸缩性是并行程序员的首要任务之一。由于手动分析性能建模通常过于费力,开发人员越来越多地求助于经验性能建模作为可行的替代方案,它从有限的性能测量中学习性能模型。虽然强大的自动化技术存在于这个目的,他们通常挣扎的情况下,表现两个或两个以上的不同现象的性能数据合并成一个单一的性能模型。这不仅会为给定数据生成一个不准确的模型,而且可能无法指出现有的可伸缩性问题,或者在没有问题的情况下创建这些问题的外观。在本文中,我们提出了一种算法来检测分割的一系列性能测量和估计点的行为变化。我们的方法在520万个合成测试中正确识别了超过80%的分割,并在三个应用案例研究中确认了预期的分割。
Besides correctness, scalability is one of the top priorities of parallel programmers. With manual analytical performance modeling often being too laborious, developers increasingly resort to empirical performance modeling as a viable alternative, which learns performance models from a limited amount of performance measurements. Although powerful automatic techniques exist for this purpose, they usually struggle with the situation where performance data representing two or more different phenomena are conflated into a single performance model. This not only generates an inaccurate model for the given data, but can also either fail to point out existing scalability issues or create the appearance of such issues when none are present. In this paper, we present an algorithm to detect segmentation in a sequence of performance measurements and estimate the point where the behavior changes. Our method correctly identified segmentation in more than 80% of 5.2 million synthetic tests and confirmed expected segmentation in three application case studies.
使用自动化性能建模来查找复杂代码中的可扩展性错误
DOI: 10.1145/2503210.2503277
发表时间: 2013
期刊: 2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC)
影响因子: --
作者:
Calotoiu;Hoefler
通讯作者: Hoefler
DOI: 10.1016/s0092-8240(89)80047-3
发表时间: 1989-01-01
影响因子: 3.5
作者:
AUGER, IE;LAWRENCE, CE
通讯作者: LAWRENCE, CE
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DOI: --
发表时间: 2006
期刊:
影响因子: --
作者:
G. Fagg;Jelena Pjesivac;G. Bosilca;T. Angskun;J. Dongarra;E. Jeannot
通讯作者: E. Jeannot