Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/deeplearningais/CUV

Matrix library for CUDA in C++ and Python
https://github.com/deeplearningais/CUV

Last synced: 10 days ago
JSON representation

Matrix library for CUDA in C++ and Python

Awesome Lists containing this project

README 

        
CUV Documentation



0.9.201107041204



Summary



CUV is a C++ template and Python library which makes it easy to use NVIDIA(tm)

CUDA.



Features



Supported Platforms:



  • 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.



Supported GPUs:



  • 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.



Structure:



  • 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

    convention, row-major)

  • 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!



Python Integration



  • 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.



Implemented Functionality



  • 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.



Documentation



  • 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

    internet: http://www.ais.uni-bonn.de/deep_learning/doc/html/index.html



Contact



  • 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://

    www.ais.uni-bonn.de/deep_learning/index.html.



Installation



Requirements



For C++ libs, you will need:



  • cmake (and cmake-curses-gui for easy configuration)

  • libboost-dev >= 1.37

  • libblas-dev

  • libtemplate-perl -- (we might get rid of this dependency soon)

  • NVIDIA CUDA (tm), including SDK. We support versions 3.X and 4.0

  • thrust library - included in CUDA since 4.0 (otherwise available from http:

    //code.google.com/p/thrust/)

  • doxygen (if you want to build the documentation yourself)



For Python Integration, you additionally have to install



  • pyublas -- from http://mathema.tician.de/software/pyublas

  • python-nose -- for python testing

  • python-dev



Optionally, install dependent libraries



  • cimg-dev for visualization of matrices (grayscale only, ATM)



Obtaining CUV



You should check out the git repository



   $ git clone git://github.com/deeplearningais/CUV.git



Installation Procedure



Building a debug version:



 $ cd cuv-version-source

 $ mkdir -p build/debug

 $ cd build/debug

 $ cmake -DCMAKE_BUILD_TYPE=Debug ../../

 $ ccmake .          # adjust paths to your system (cuda, thrust, pyublas, ...)!

                     # turn on/off optional libraries (CImg, ...)

 $ make -j

 $ ctest             # run tests to see if it went well

 $ sudo make install

 $ export PYTHONPATH=`pwd`/src      # only if you want python bindings



Building a release version:



 $ cd cuv-version-source

 $ mkdir -p build/release

 $ cd build/release

 $ cmake -DCMAKE_BUILD_TYPE=Release ../../

 $ ccmake .          # adjust paths to your system (cuda, thrust, pyublas, ...)!

                     # turn on/off optional libraries (CImg, ...)

 $ make -j

 $ ctest             # run tests to see if it went well

 $ sudo make install

 $ export PYTHONPATH=`pwd`/src      # only if you want python bindings



On Debian/Ubuntu systems, you can skip the sudo make install step and instead

do



 $ cpack -G DEB

 $ sudo dpkg -i cuv-VERSION.deb



Building the documentation



 $ cd build/debug    # change to the build directory

 $ make doc



Sample Code



We show two brief examples. For further inspiration, please take a look at the

test cases implemented in the src/tests directory.



Pushing and pulling of memory



C++ Code:



 #include 

     using namespace cuv;



     int main(void){

         tensor h(256);  // reserves space in host memory

         tensor  d(256);  // reserves space in device memory



         fill(h,0);                          // terse form

         apply_0ary_functor(h,NF_FILL,0.f);    // more verbose



         d=h;                                // push to device

         sequence(d);                        // fill device vector with a sequence



         h=d;                                // pull to host

         for(int i=0;i

 using namespace cuv;



 int main(void){

     tensor C(2048,2048),A(2048,2048),B(2048,2048);



     fill(C,0);         // initialize to some defined value, not strictly necessary here

     sequence(A);

     sequence(B);



     apply_binary_functor(A,B,BF_MULT);  // elementwise multiplication

     A *= B;                             // operators also work (elementwise)

     prod(C,A,B, 'n','t');               // matrix multiplication

 }



Python Code



 import cuv_python as cp

 import numpy as np

 C = cp.dev_tensor_float_cm([2048,2048])   # column major tensor

 A = cp.dev_tensor_float_cm([2048,2048])

 B = cp.dev_tensor_float_cm([2048,2048])

 cp.fill(C,0)                       # fill with some defined values, not really necessary here

 cp.sequence(A)

 cp.sequence(B)

 cp.apply_binary_functor(B,A,cp.binary_functor.MULT) # elementwise multiplication

 B *= A                                              # operators also work (elementwise)

 cp.prod(C,A,B,'n','t')                              # matrix multiplication



The examples can be found in the "examples/" folder under "python" and "cpp"



Generated on Mon Jul 4 2011 12:04:53 for CUV by  doxygen 1.7.1