EM of GMMs computed by the graphics card

[aharp]

For my project, I implemented Expectation Maximization of Gaussian Mixture Models with Nvidia's CUDA SDK. This allows me to run the EM algorithm in a massively parallel nature, resulting in order of magnitude speedups over the conventional C++ version.



Links:
Project Home Page
Matlab File Exchange
Report
Source Code
Bonus: Parallel IQAgent

Readme contents:
ABOUT
================================================== ==============================
This is a parallel implementation of the Expectation Maximization algorithm for
Gaussian Mixture Models, designed to run on NVidia graphics cards supporting
CUDA. On my machine, it provides up to 60x performance increases. See the
report available at http://andrewharp.com/gmmcuda for more information.

The interesting code is all in gpugaumixmod.h and gpugaumixmod_kernel.h.
The reference CPU implementation is in gaumixmod.h.

It can be integrated into any C program on a CUDA enabled system. Additionally,
Matlab integration is provided in gmm.cu.

E-mail me with any questions or comments!


COMPILING
================================================== ==============================
You'll probably have trouble compiling as-is, as the config files are set up to
run on my Windows Vista 64bit machine, but it's just a standard Cuda kernel
underneath so it should be portable. A precompiled Windows 64-bit version is
included.

See compile.m for the command I use to compile the CUDA/Mex files.

Go here to find the toolkit that contains the files you'll need for compiling on
your platform: http://developer.nvidia.com/object/matlab_cuda.html



RUNNING
================================================== ==============================
Once compiled, start off by running gmm_example in Matlab to see it in action.

See experiment1, experiment2, experiment3 for ready to run experiments


-Andrew Harp