Difference between revisions of "Summer2009:Using CUDA for stochastic tractography"

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* Cuda was integrated through PyCuda with support for numpy arrays syntax
 
* Cuda was integrated through PyCuda with support for numpy arrays syntax
 
* direct call to driver api tested
 
* direct call to driver api tested
* driver kernels tested (simple matrices operations)
+
* driver kernels tested (simple matrix operations)
 
   
 
   
  

Revision as of 21:50, 26 June 2009

Home < Summer2009:Using CUDA for stochastic tractography


Key Investigators

  • BWH: Julien de Siebenthal, Sylvain Bouix

Objective

Stochastic tractography does not provide interactive visualization so far due to its intensive computational needs.

Approach, Plan

Idea would be to visualize online paths generated in a point of interest like a fiducial. This approach would be based on the online visualization of streamline tractography done by moving a fiducial interactively.

During the summer week, we will continue our work to develop a concrete solution in investigating acceleration means based mainly on CUDA.

Progress

  • Cuda was integrated through PyCuda with support for numpy arrays syntax
  • direct call to driver api tested
  • driver kernels tested (simple matrix operations)



References