Scalability analysis of large codes using factorial designs
Scalability analysis of large codes using factorial designs
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DOI：
10.1016/s0167-8191(01)00068-0
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发表时间：
2001-08-01
影响因子：
1.4
通讯作者：
Dhall, SK
中科院分区：
文献类型：
--
作者：
Alabdulkareem, M;Lakshmivarahan, S;Dhall, SK
并行算法-体系结构组合的可扩展性分析一直是人们关注的焦点。这种理论方法总是需要对算法有详细的了解。最近，Lyon和他在美国国家科学技术研究所（NIST）的同事开发了另一种基于黑盒的方法来分析大型并行代码的可伸缩性。这种方法基于统计学实验设计中历史悠久的原则。使用后一种方法，在本文中，我们分析了称为Advance Regional Prediction System的大型代码的可扩展性，该代码是GRAY J-90和IBM SP-2上最先进的数值天气预报系统。这种实验方法不需要对底层算法有广泛的了解，并且可以自动化。(C) 2001 Elsevier Science B.V.版权所有
Analysis of scalability of parallel algorithm-architecture combination has been the subject of intense scrutiny for quite some time. This theoretical approach invariably requires detailed knowledge of the algorithm. Recently, Lyon and his coworkers at the National Institute of Science and Technology (NIST) developed an alternate black-box based approach to the analysis of scalability of large parallel codes. This approach is based on the time-honored principles from experimental design in statistics. Using this later approach, in this paper we analyze the scalability of a large code called Advance Regional Prediction System, which is a state-of-the-art numerical weather prediction system on GRAY J-90 and IBM SP-2. This experimental approach does not require extensive knowledge of the underlying algorithm and can be automated. (C) 2001 Elsevier Science B.V. All rights reserved.