This project is an OpenCL-based simulator for brain models built from NEural ENGineering Objects in Nengo. It can be orders of magnitude faster than the default simulator in nengo for large models.

To use the nengo_ocl projects OpenCL simulator, build a nengo model as usual, but pass sim_ocl.Simulator when creating a simulator for your model.

import nengo
from nengo_ocl.sim_ocl import Simulator
import pyopencl as cl

ctx = cl.create_some_context()

# -- build a model
m = nengo.Model('foo')
m.make_node('in', output=1)
m.make_ensemble('A', nengo.LIF(40), 1)
m.connect('in', 'A')
m.probe('A', filter=0.01)

# -- create an OpenCL-backed simulator using a
#    particular device context:
sim = m.simulator(sim_class=Simulator, context=ctx)

sim.run(1.0)
print sim.data('A')

General:

• Python 2.6 or better (Python 3 untested)
• One or more OpenCL implementations (test with e.g. PyOpenCl)

Python packages:

• mako
• nengo
• networkx
• NumPy
• PyOpenCL

( set -e ; for PCK in networkx numpy mako pyopencl ; do pip install $PCK ; done )

Intel provides an OpenCL driver for at least some of their multicore processors. Core-i7 and Xeon chips can be quite good for running nengo simulations.

Details: http://software.intel.com/en-us/forums/topic/390630

1. Download Intel SDK for OpenCL for applications from Intel OpenCL website

2. Extract

   tar zxvf intel_sdk_for_ocl_applications_2012_x64.tgz

3. Convert RPM files to .deb

   sudo apt-get install -y rpm alien libnuma1   # Get conversion packages
   fakeroot alien --to-deb opencl-1.2-*.rpm     # Convert all RPMs

4. Install .deb packages. They will be put in /opt/intel

   sudo dpkg -i opencl-1.2-*.deb # Install all .debs

5. Add library to search path

   sudo touch /etc/ld.so.conf.d/intelOpenCL.conf

   Put in the line: /opt/intel/opencl-1.2-3.0.67279/lib64

6. Link the Intel icd file

   sudo ln /opt/intel/opencl-1.2-3.0.67279/etc/intel64.icd /etc/OpenCL/vendors/intel64.icd

7. Run ldconfig

   sudo ldconfig

Can be easy: AMD provides binary drivers and wants people to use OCL. Instructions on PyOpenCL wiki

On Debian unstable (sid) there are packages in non-free and contrib to install AMD's OCL implementation easily. Actually, the easiest thing would be to apt-get install python-pyopencl. But if you're using a virtual environment, you can apt-get install opencl-headers libboost-python-dev amd-opencl-icd amd-libopencl1 and then pip install pyopencl.

Debian (at least the rolling "sid" distribution) provides easily-installable .deb files for OpenCL:

sudo apt-get install nvidia-opencl-common nvidia-libopencl1

Ensure that the nvidia driver version matches the opencl library version. You can check the nvidia driver version by running nvidia-smi in the command line. You can find the opencl library version by looking at the libnvidia-opencl.so.XXX.XX file in the /usr/lib/x86_64-linux-gnu/ folder.

N.B. that at the time of writing (Sept 2013) these drivers provide only OpenCL-1.1 rather than the more current OpenCL-1.2. Consequently, you may find that pyopencl's default build creates a binary Python module (_cl.so) that cannot be loaded (i.e. import pyopencl fails in the Python interpreter). You can fix this one of two ways:

1. Use the generic libOpenCL.so driver-loading library from another provider (by e.g. following the Intel instructions above), and simply don't try to use new 1.2 features on NVidia devices,
2. Follow PyOpenCL's build instructions to compile an OpenCL-1.1 version of PyOpenCL.

It's nice to have a CPU OpenCL driver, so we recommend option (1).