GPA - A Tool for Fluid Scalability Analysis of Massively Parallel Systems
GPA - A Tool for Fluid Scalability Analysis of Massively Parallel Systems
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GPA - 大规模并行系统的流体可扩展性分析工具
DOI：
10.1109/qest.2011.26
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Jeremy T. Bradley
中科院分区：
文献类型：
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作者：
Anton Stefanek;R. A. Hayden;Jeremy T. Bradley
最近基于常微分方程(ODE)的技术使得对具有极大状态空间的马尔可夫种群模型进行有效分析成为可能。在大多数真实规模的情况下，它们提供了随机模拟的唯一替代方案。此外，常微分方程组的数值解在计算上比用数量级模拟要便宜。我们提出了具有新功能的分组Pepa Analyser(GPA)工具，以利用计算成本低的流体分析技术来允许在具有大状态空间的模型中探索大量的系统配置。GPA为随机进程代数中描述的模型提供了流体分析技术的有效实现。它实现了最近开发的扩展，允许使用基于状态、累积比率和脉冲奖励的组合来指定复杂的奖励措施。与有效捕获各种通行时间指标的能力相结合，GPA可用于解决不同服务级别协议类型约束下具有奖励目标函数的优化问题。
Recent ordinary differential equation (ODE) based techniques allow efficient analysis of Markovian population models with extremely large state spaces. In most cases of realistic scale, they provide the only alternative to stochastic simulation. Moreover, numerical solution of the ODEs is cheaper computationally than simulation by orders of magnitude. We present the Grouped PEPA Analyser (GPA) tool with new functionality to exploit computationally inexpensive fluid analysis techniques to allow the exploration of large numbers of system configurations in models with large state spaces. GPA provides an efficient implementation of the fluid analysis techniques for models described in a stochastic process algebra. It implements recently developed extensions allowing specifications of complex reward measures using combinations of state based, rate accumulated and impulse rewards. Combined with the ability to efficiently capture various passage time metrics, GPA can be used to solve optimisation problems with a reward objective function under different service level agreement type constraints.
DOI：
10.1016/j.tcs.2010.02.001
发表时间：
2010-05
期刊：
Theor. Comput. Sci.
影响因子：
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作者：
R. A. Hayden;J. Bradley
通讯作者：
R. A. Hayden;J. Bradley
影响因子：
1.1
作者：
Ciocchetta, Federica;Hillston, Jane
通讯作者：
Hillston, Jane