CUV: Matrix library for CUDA in C++ and Python
CUV is a C++ template and Python library which makes it easy to use NVIDIA(tm)
• This library was only tested on Ubuntu Karmic, Lucid and Maverick. It uses
mostly standard components (except PyUBLAS) and should run without major
modification on any current linux system.
• By default, code is generated for the lowest compute architecture. We
recommend you change this to match your hardware. Using ccmake you can set
the build variable “CUDA_ARCHITECTURE” for example to -arch=compute_20
• All GT 9800 and GTX 280 and above
• GT 9200 without convolutions. It might need some minor modifications to
make the rest work. If you want to use that card and have problems, just
get in contact.
• On 8800GTS, random numbers and convolutions wont work.
• Like for example Matlab, CUV assumes that everything is an n-dimensional
array called “tensor”
• Tensors can have an arbitrary data-type and can be on the host (CPU-memory)
or device (GPU-memory)
• Tensors can be column-major or row-major (1-dimensional tensors are, by
• The library defines many functions which may or may not apply to all
possible combinations. Variations are easy to add.
• For convenience, we also wrap some of the functionality provided by Alex
Krizhevsky on his website (http://www.cs.utoronto.ca/~kriz/) with
permission. Thanks Alex for providing your code!
• CUV plays well with python and numpy. That is, once you wrote your fast GPU
functions in CUDA/C++, you can export them using Boost.Python. You can use
Numpy for pre-processing and fancy stuff you have not yet implemented, then
push the Numpy-matrix to the GPU, run your operations there, pull again to
CPU and visualize using matplotlib. Great.
• Simple Linear Algebra for dense vectors and matrices (BLAS level 1,2,3)
• Helpful functors and abstractions
• Sparse matrices in DIA format and matrix-multiplication for these matrices
• I/O functions using boost.serialization
• Fast Random Number Generator
• Up to now, CUV was used to build dense and sparse Neural Networks and
Restricted Boltzmann Machines (RBM), convolutional or locally connected.
• Tutorials are available on http://www.ais.uni-bonn.de/~schulz/tag/cuv
• The documentation can be generated from the code or accessed on the
• We are eager to help you getting started with CUV and improve the library
continuously! If you have any questions, feel free to contact Hannes Schulz
(schulz at ais dot uni-bonn dot de) or Andreas Mueller (amueller at ais dot
uni-bonn dot de). You can find the website of our group at http://