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MCX is a GPU-accelerated, general-purpose, physically-accurate and feature-rich 3-D light transport simulator. It is one of the fastest simulators because it can use tens of thousands of GPU threads to simulate photons in parallel.
This project, initially developed at University College London, contains programs to perform rigid, affine and non-linear registration of nifti or analyse images. Two versions of the algorithms are included, a CPU- and a GPU- (using CUDA) based implementation.
This project, developed at UCL London, provides code for tomographic reconstruction. NiftyRec is written in C and has Python and Matlab extensions. Computationally intensive functions have a GPU accelerated version based on CUDA.
...(The code section is currently close until our article gets published)
Commiting publicly to the project is currently closed but feel free to email us.
The source code is dependant on CUDA 5.0 Samples now available as part of the CUDA Toolkit.
"This software contains source code provided by NVIDIA Corporation."
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CUDA accelerated spherical model forward solution for EEG/MEG
CUDA-SPHERE-FWD-MEEG is a CUDA C based toolkit which provides a GPU based implementation of the spherical model forward solution for the 306 channel Elekta Neuromag MEG system and the EEG. The 1-Sphere forward solution for the MEG and the 4-Sphere forward solution for the EEG is implemented in CUDA C and an accelerated solution is obtained using the NVIDIA GPU when the solution is calculated for a large number of dipoles (on the order of 15000 and above) and sensor locations. ...
Using the CUDA API this project modifies the AutoDock software to run in parallel on NVIDIA GPUs. Users will be able to download and compile the code and use AutoDock on CUDA capable Graphics Cards. Autodock is located at http://autodock.scripps.edu/