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https://github.com/deeplearningais/CUV

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

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Matrix library for CUDA in C++ and Python

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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