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So, to marry the two, I wrote for myself some wrapper code. It’s pretty much only good for one thing: multiplying large matrices together really fast. But it’s really good at it. (and it’s really easy to use) For example:

import numpy
from pycublas import CUBLASMatrix
A = CUBLASMatrix( numpy.mat([[1,2,3],[4,5,6]],numpy.float32) )
B = CUBLASMatrix( numpy.mat([[2,3],[4,5],[6,7]],numpy.float32) )
C = A*B
print C.np_mat()


All CUBLAS alloc and free calls are mapped to the CUBLASMatrix object’s life in Python, so you don’t have to worry about memory management. (other than filling up the card, or course)

Here are some performance numbers: (includes memory transfer times)
(4160×4160)*(4160×4160) = 43.0X faster than numpy
(4096×4096)*(4096×4096) = 34.0X
(3900×3900)*(3900×3900) = 47.3X
(2048×2048)*(2048×2048) = 28.2X
(1024×1024)*(1024×1024) = 58.8X
(512×512)*(512×512) = 24.1X
(256×256)*(256×256) = 6.3X
(128×128)*(128×128) = 1.1X
CPU: Intel(R) Core(TM)2 Duo CPU E8400 @ 3.00GHz stepping 06
GPU: nVidia Corporation GeForce 8800 GT (rev a2)

Note: This version only supports float32.
Note: CUBLAS limits matrix dims to (65536×65536).

Source code available here: pycublas.py (rename download to pycublas.py to use)

corpus = LDC2006T13()
for ngram, count in corpus.ngrams(3):
  print ngram, count


Code is here: LDC2006T13.py

Install the CUDA toolkit. Ubuntu already comes with the 177.80 NVidia driver, so I didn’t install v177.73.

# install CUDA
wget http://developer.download.nvidia.com/compute/cuda/2_0/linux/driver/NVIDIA-Linux-x86_64-177.73-pkg2.run
wget http://developer.download.nvidia.com/compute/cuda/2_0/linux/toolkit/NVIDIA_CUDA_Toolkit_2.0_ubuntu7.10_x86_64.run
wget http://developer.download.nvidia.com/compute/cuda/2_0/linux/sdk/NVIDIA_CUDA_SDK_2.02.0807.1535_linux.run
chmod +x *.run
sudo ./NVIDIA_CUDA_Toolkit_2.0_ubuntu7.10_x86_64.run
# accepted default install location: /usr/local/cuda
su -
echo "include /usr/local/cuda/lib" >> /etc/ld.so.conf.d/cuda.conf
ldconfig
cd /usr/bin/
ln -s /usr/local/cuda/open64/bin/* .
exit


Install Boost libs, v1.35.

sudo apt-get install libboost-python1.35-dev


Install PyCUDA.

wget http://pypi.python.org/packages/source/p/pycuda/pycuda-0.91.tar.gz#md5=339dac0641cc5da2f5c153d134592ed3
tar -zxvf pycuda-0.91.tar.gz
cd pycuda-0.91/
./configure


Edit siteconf.py to look like this…

BOOST_INC_DIR = ['/usr/include/boost/']
BOOST_LIB_DIR = ['/usr/lib']
BOOST_PYTHON_LIBNAME = ['boost_python-mt-py25']
CUDA_ROOT = '/usr/local/cuda/'
CUDADRV_LIB_DIR = []
CUDADRV_LIBNAME = ['cuda']
CXXFLAGS = []
LDFLAGS = []


Then take her home…

python setup.py build
sudo make install


Now to test:

#export C_INCLUDE_PATH=/usr/local/cuda/include/
#export CPLUS_INCLUDE_PATH=/usr/local/cuda/include/
export PATH=$PATH:/usr/local/cuda/bin/
cd test
python test_driver.py


The tests run successfully for me, sans test_mempool (__main__.TestCuda). I’ll update this as I find out more.

http://www.nature.com/news/2008/080430/full/453012b.html (doi:10.1038/453012b)

Cool, eh?

Website: http://desigle.org/

Use if you wish.

My PyGTK skillz are improving…

Allow me to introduce gPapers, a Gnome-based Digital Library Manager. (think iTunes for all your PDF files)

If you have to ask “why?”, you’re probably not working in academia, and have never had to manage piles of journal papers. This isn’t for you. If you’re a Windows or OSX user, this isn’t for you. If you’re afraid of compiling a library or two, this isn’t for you. In fact, I believe there is a worldwide audience of perhaps seven people who will find this software useful.

But to those six others, I promise it’s a godsend.

This has been a side project for me for a little over a month now, and I’m ready to start collecting external feedback. So please, give it a whirl (and join the listserv).

And so was born ogmaps. It’s a fairly simple python script that downloads all the HTML/Javascript/image files used by gmaps, and modifies them to run right off your hard drive. (or flash drive, whatever) It then looks up whatever location you give it, and caches all the surrounding map files. (within reason….grabs about 5-10mb of data for each location you give it)

You don’t need a handheld to use it either… It’ll work wherever you have python and firefox. (I haven’t tried it with IE yet, and likely won’t – get a better browser!)

Anyway, tell me what you think.

derek

• watch their intro video

And they’re celebrating by also announcing a $10 million developer challenge, two be handed out in two $5 million dollar rounds with at least 50 individual recipients each round. Pretty exciting, eh?

So does anyone have any suggestions for “i wish my phone could do this”? I’d love to hear your thoughts…

http://code.google.com/p/flyback/