Difference between revisions of "Summer2009:Using CUDA for stochastic tractography"
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<h3>Progress</h3> | <h3>Progress</h3> | ||
* 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 | + | * Check compilation&instalation under Linux&Windows |
| − | * driver kernels | + | * Implemented part of the tractography algorithm in kernel |
| + | * Tested direct call to driver api - ok | ||
| + | * Tested driver kernels (simple matrix operations) - ok | ||
| + | * Still need to complete the whole tractography algorithm in kernel mode | ||
Latest revision as of 22:07, 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
- Check compilation&instalation under Linux&Windows
- Implemented part of the tractography algorithm in kernel
- Tested direct call to driver api - ok
- Tested driver kernels (simple matrix operations) - ok
- Still need to complete the whole tractography algorithm in kernel mode
