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R. Boubela, W. Huf, K. Kalcher, R. Stadky, P. Filzmoser, L. Pezawas, S. Kasper, C. Windischberger, E. Moser:
"A highly parallelized framework for computationally intensive MR data analysis";
Magnetic Resonance Materials in Physics, Biology and Medicine, 25 (2012), August; 313 - 320.

Object
The goal of this study was to develop a comprehensive magnetic resonance (MR) data analysis framework for handling very large datasets with user-friendly tools for parallelization and to provide an example implementation.
Materials and methods
Commonly used software packages (AFNI, FSL, SPM) were connected via a framework based on the free software environment R, with the possibility of using Nvidia CUDA GPU processing integrated for high-speed linear algebra operations in R. Three hundred single-subject datasets from the 1,000 Functional Connectomes project were used to demonstrate the capabilities of the framework.
Results
A framework for easy implementation of processing pipelines was developed and an R package for the example implementation of Fully Exploratory Network ICA was compiled. Test runs on data from 300 subjects demonstrated the computational advantages of a processing pipeline developed using the framework compared to non-parallelized processing, reducing computation time by a factor of 15.
Conclusion
The feasibility of computationally intensive exploratory analyses allows broader access to the tools for discovery science.

http://link.springer.com/article/10.1007%2Fs10334-011-0290-7?LI=true