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README.txt

== gpanalyser ==
( http://code.google.com/p/gpanalyser/ )

[VERSION HISTORY]
v0.9.1
14 April 2011 - Initial release based on a collection of experimental tools. 

================

[INTRODUCTION]

gpanalyser (GPA) is a tool for analysing stochastic models described in a stochastic process algebra. 
GPA implements the fluid analysis techniques [1] that derive systems of ordinary differential 
equations (ODE) from models defined in the Grouped PEPA (GPEPA) variant of the PEPA 
stochastic process algebra.

Based on an earlier version [4], GPA can additionally compute further derived measures,
 such as various passage times [2] and moments of accumulated rewards [3].

================ 
 
[GETTING STARTED]

GPA is a command line tool, but also provides a fast graphical output of the calculated data.
GPA requires Java JDK 6 (can be obtained here http://www.oracle.com/technetwork/java/javase/downloads/index.html )
GPA can run on any platform with Java JDK. However, in case of 3D graphical output GPA is using OpenGL 
libraries requiring native support. The GPA archive contains scripts to run GPA on a variety of platforms
(Linux and Windows 32/64 bit and Mac OS X). On other platforms, GPA can be run without the 3D capabilities 
with the command

	java -jar build/dist/gpa-0.9.1.jar

GPA requires an input model file as an argument on the command line. Example models can be found in 
the models repository http://code.google.com/p/gpanalyser/source/browse/?repo=models#hg%2Fexamples
More details on the model and analysis syntax can be found in the main documentation.    

[AUTHORS]

Anton Stefanek, Richard Hayden, Jeremy Bradley

================

[REFERENCES] 

[1] Richard Hayden, Jeremy T. Bradley: A fluid analysis framework for a Markovian process algebra
     Theoretical Computer Science, Volume 411, May 2010 
     ( http://dx.doi.org/10.1016/j.tcs.2010.02.001 )
[2] Richard Hayden, Anton Stefanek, Jeremy T. Bradley: Fluid computation of passage time distributions in large Markov models
     Technical report, November 2010 
     ( http://aesop.doc.ic.ac.uk/pubs/fluid-passage-time/ ).
[3] Anton Stefanek, Richard Hayden, Jeremy T. Bradley:  Fluid analysis of energy consumption using rewards in massively parallel Markov models
     2nd ACM/SPEC International Conference on Performance Engineering, March 14-16, 2011, Karlsruhe, Germany
     ( http://aesop.doc.ic.ac.uk/pubs/fluid-energy-rewards/ )
[4] Anton Stefanek, Richard Hayden, Jeremy T. Bradley:  A new tool for the performance analysis of massively parallel computer systems
     Eighth Workshop on Quantitative Aspects of Programming Languages, March 27-28, 2010, Paphos, Cyprus
     ( http://dx.doi.org/10.4204/EPTCS.28.11 )