Graham Taylor

Modules for GPUmat

These are tested and working with the current (as of May 18, 2010) release 0.251 of GPUmat. They require the CUDA 3.0 driver & runtime. If you find a bug, of course, I am happy to hear from you. But please note, these are provided without support.

Download my modules here:

I also have a version for the CUDA 2.3 runtime. These were tested and working with GPUmat 0.25. This is likely the last time I will release versions for the 2.3 runtime. They can be downloaded here:

They are pre-compiled for the x86_64 architecture. You should be able to re-compile them for another architecture. However, since with GPUmat we are forced to use the Driver API, this makes things tricky for template kernels. In the convolution & subsampling functions, I have had to copy the mangled name directly from the cubin file to the driver (cpp) code where the module is loaded. Since changing the architecture will change the mangled name, this may require some manual labour on your part.
Update: It seems that since 3.0 the .cubin files are no longer ASCII so you can no longer find out the mangled names just by opening them up in a text editor. However, you can still retrieve this information by passing some additional flags to nvcc: See here.

Python users should check out cudamat.

Provided functions

Note: though GPUmat provides double precision & complex-number support my functions only work with single precision (i.e. GPUsingle types).

See the help associated with each file. Many of these functions are simply wrappers to Alex Krizhevsky's code and he deserves all the credit.

Release Notes


Download GPUmat and verify that it is working for you. In particular, you should be able to compile and run their sample modules. It was necessary for me to add:
CUDA_ROOT = '/usr/local/pkg/cuda/current/cuda' to:
This should reflect the actual location of your CUDA install.

Make sure your environment is set correctly. This will, of course, depend on the actual location of your CUDA install. These statements, in my ~/.bash_profile were sufficient for the cs machines:
[ -d /usr/local/pkg/cuda/current/cuda/bin ] && export PATH=/usr/local/pkg/cuda/current/cuda/bin:$PATH
[ -d /usr/local/pkg/cuda/current/cuda/lib64 ] && export LD_LIBRARY_PATH=/usr/local/pkg/cuda/current/cuda/lib64:$LD_LIBRARY_PATH
[ -d /usr/local/pkg/cuda/current/cuda ] && export CUDA_INSTALL_PATH=/usr/local/pkg/cuda/current/cuda

Unzip the above zip file in your GPUmat directory. This should create:

Provided you don't need to recompile for your platform, running GPUstart should automatically initialize my modules (as well as the default modules). You can also manually initialize each module by using its moduleinit.m script. The test scripts (test___.m) demonstrate how to call each function.

One small hiccup (that I will eventually get around to addressing) is that it is necessary to have rnd_multipliers_32bit.txt in the current directory (this is provided in modules/gwt/cuMisc) before calling any of the functions that use random number generation (cuRand,cuRandn,cuBinarizeProbs) for the first time. Otherwise it will complain about the missing file.