A new tool for the performance analysis of massively parallel computer systems

Anton Stefanek
(Imperial College London)
Richard Hayden
(Imperial College London)
Jeremy Bradley
(Imperial College London)

We present a new tool, GPA, that can generate key performance measures for very large systems. Based on solving systems of ordinary differential equations (ODEs), this method of performance analysis is far more scalable than stochastic simulation. The GPA tool is the first to produce higher moment analysis from differential equation approximation, which is essential, in many cases, to obtain an accurate performance prediction. We identify so-called switch points as the source of error in the ODE approximation. We investigate the switch point behaviour in several large models and observe that as the scale of the model is increased, in general the ODE performance prediction improves in accuracy. In the case of the variance measure, we are able to justify theoretically that in the limit of model scale, the ODE approximation can be expected to tend to the actual variance of the model.

In Alessandra Di Pierro and Gethin Norman: Proceedings Eighth Workshop on Quantitative Aspects of Programming Languages (QAPL 2010), Paphos, Cyprus, 27-28th March 2010 , Electronic Proceedings in Theoretical Computer Science 28, pp. 159–181.
Published: 26th June 2010.

ArXived at: http://dx.doi.org/10.4204/EPTCS.28.11 bibtex PDF

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