We present a parallel, medical application for the analysis of dynamic positron emission tomography (PET) images together with a practical performance model. The parallel application improves the diagnosis for a patient (e. g. in epilepsy surgery) because it enables the fast computation of parametric images on a pixel level in contrast to the traditionally used region of interest (ROI) approach. We derive a simple performance model from the application context and demonstrate the accuracy of the model to predict the runtime of the application on a NOW. The model is used to determine an optimal value for the length of the messages with regard to the per message overhead and the load imbalance

@article{PAOPSATAPP00,
	author	 = {Frank Munz and Thomas Ludwig and Sibylle Ziegler and Peter Bartenstein and Markus Schwaiger and Arndt Bode},
	title	 = {{Performance assessment of parallel spectral analysis: Towards a practical performance model for parallel medical applications}},
	year	 = {2000},
	publisher	 = {Elsevier Science Publishers B. V.},
	address	 = {Amsterdam, The Netherlands},
	journal	 = {Future Generation Computer Systems},
	series	 = {16 (5)},
	pages	 = {553--562},
	issn	 = {0167-739X},
	doi	 = {http://dx.doi.org/10.1016/S0167-739X(99)00139-9},
	abstract	 = {We present a parallel, medical application for the analysis of dynamic positron emission tomography (PET) images together with a practical performance model. The parallel application improves the diagnosis for a patient (e. g. in epilepsy surgery) because it enables the fast computation of parametric images on a pixel level in contrast to the traditionally used region of interest (ROI) approach. We derive a simple performance model from the application context and demonstrate the accuracy of the model to predict the runtime of the application on a NOW. The model is used to determine an optimal value for the length of the messages with regard to the per message overhead and the load imbalance},
}