User:Ppomorsk/Test

PyCUDA makes it possible to easily use CUDA inside Python code.

Documentation can be found on the package webpage.

This package is not currently installed as SHARCNET-supported software, but it's easy for users to install it on their own following instructions below. If any difficulties are encountered when following these instructions, please ask SHARCNET staff for help.

See also: PyOpenCL

SHARCNET installation instructions

Monk cluster

(These instructions were tested on Oct 22, 2014)

1. Unload the Intel compiler module (loaded by default), so that GCC becomes the default compiler. Also, use a later python version.

module unload intel
module unload openmpi
module load gcc/4.8.2
module load openmpi/gcc/1.8.1
module load python/gcc/2.7.8

Note: openmpi module is loaded because the python module needs it (it is not actually used by PyCuda)

2. Create some directory you want to build the package in, cd into it, then get the PyCUDA source code:

git clone http://git.tiker.net/trees/pycuda.git
cd pycuda
git submodule init
git submodule update
 
wget https://pypi.python.org/packages/source/p/pycuda/pycuda-2014.1.tar.gz#md5=fdc2f59e57ab7256a7e0df0d9d943022
tar xfz pycuda-2014.1.tar.gz
cd pycuda-2014.1

3. At this point decide where you want the package to be installed. In this example will use a directory called python_packages in the home directory. If this directory does not yet exist, make it with:

mkdir -p ~/python_packages/lib/python/

Doing it this way creates the required subdirectories as well.

4. Edit file Makefile.in to add --home flag pointing to the directory you created to the setup install line, so that it reads:

${PYTHON_EXE} setup.py install --home=~/python_packages

5.You now need to update the PYTHONPATH variable to point to the library directory:

export PYTHONPATH=~/python_packages/lib/python/:$PYTHONPATH

6. Configure and compile, providing a path to the CUDA files on monk:

python configure.py --cuda-root=/opt/sharcnet/cuda/6.0.37/toolkit
make install

7. Do the first test of the installation to make sure the pycuda module can be imported, by starting python and executing:

import pycuda

If no errors are reported, everything worked and the package is ready for use.

8. Add the lines:

module unload intel
module unload openmpi
module load gcc/4.8.2
module load openmpi/gcc/1.8.1
module load python/gcc/2.7.8
export PYTHONPATH=~/python_packages/lib/python/:$PYTHONPATH

to your ~/.bashrc file so that this variable is set automatically for you on every login.

9. Test PyCUDA on a development node which has a GPU (the login node does not have one so PyCUDA tests will produce an error). To do this, execute on monk login node:

ssh mon54

Then go to the directory where you put the PyCuda source code, and execute:

python test/test_driver.py

Gives error:

[ppomorsk@mon241:~/supported_sharcnet_packages/pycuda/pycuda-2014.1/test] python test_driver.py Traceback (most recent call last):

 File "test_driver.py", line 4, in <module>
   from pycuda.tools import mark_cuda_test
 File "/home/ppomorsk/python_packages/lib/python/pycuda-2014.1-py2.7-linux-x86_64.egg/pycuda/tools.py", line 30, in <module>
   import pycuda.driver as cuda
 File "/home/ppomorsk/python_packages/lib/python/pycuda-2014.1-py2.7-linux-x86_64.egg/pycuda/driver.py", line 2, in <module>
   from pycuda._driver import *  # noqa
ImportError: /home/ppomorsk/python_packages/lib/python/pycuda-2014.1-py2.7-linux-x86_64.egg/pycuda/_driver.so: undefined symbol: cuStreamAttachMemAsync

ldd output

[ppomorsk@mon54:~/supported_sharcnet_packages/pycuda/pycuda-2014.1/test] ldd /home/ppomorsk/python_packages/lib/python/pycuda-2014.1-py2.7-linux-x86_64.egg/pycuda/_driver.so

linux-vdso.so.1 => (0x00007fffac5c1000) libcuda.so.1 => /usr/lib64/libcuda.so.1 (0x00002b7dbda9e000) libcurand.so.6.0 => /opt/sharcnet/cuda/6.0.37/toolkit/lib64/libcurand.so.6.0 (0x00002b7dbea01000) libstdc++.so.6 => /opt/sharcnet/gcc/4.8.2/lib64/libstdc++.so.6 (0x00002b7dc3f08000) libm.so.6 => /lib64/libm.so.6 (0x00002b7dc4211000) libgcc_s.so.1 => /opt/sharcnet/gcc/4.8.2/lib64/libgcc_s.so.1 (0x00002b7dc4496000) libpthread.so.0 => /lib64/libpthread.so.0 (0x00002b7dc46ac000) libc.so.6 => /lib64/libc.so.6 (0x00002b7dc48c9000) libz.so.1 => /lib64/libz.so.1 (0x00002b7dc4c5e000) libdl.so.2 => /lib64/libdl.so.2 (0x00002b7dc4e74000) librt.so.1 => /lib64/librt.so.1 (0x00002b7dc5078000) /lib64/ld-linux-x86-64.so.2 (0x00002b7dbd4a9000)

and the symbol does seem to exist

[ppomorsk@mon54:/usr/lib64] readelf -Ws libcuda.so.331.89 | grep cuStreamAttachMemAsync
   43: 0000000000139890   538 FUNC    GLOBAL DEFAULT   10 cuStreamAttachMemAsync

If everything is working properly, the output should look like this:

[ppomorsk@mon54:~/pycuda] python test/test_driver.py 
============================= test session starts ==============================
platform linux2 -- Python 2.6.6 -- pytest-2.3.4
collected 21 items 
 
test_driver.py .....................
 
========================== 21 passed in 61.39 seconds ==========================

10. Try the example programs provided with the source code, found in the examples subdirectory of your pycuda source directory:

python examples/dump_properties.py
python examples/hello_gpu.py

Sample PyCUDA code

This is code from the hello_gpu.py example program. It multiplies two vectors elementwise on the GPU, and then verifies the result with a standard calculation on the CPU.

import pycuda.driver as drv
import pycuda.tools
import pycuda.autoinit
import numpy
import numpy.linalg as la
from pycuda.compiler import SourceModule
 
mod = SourceModule("""
__global__ void multiply_them(float *dest, float *a, float *b)
{
  const int i = threadIdx.x;
  dest[i] = a[i] * b[i];
}
""")
 
multiply_them = mod.get_function("multiply_them")
 
a = numpy.random.randn(400).astype(numpy.float32)
b = numpy.random.randn(400).astype(numpy.float32)
 
dest = numpy.zeros_like(a)
multiply_them(
        drv.Out(dest), drv.In(a), drv.In(b),
        block=(400,1,1))
 
print dest-a*b

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