Welcome to PyOpenCL’s documentation!

PyOpenCL gives you easy, Pythonic access to the OpenCL parallel computation API. What makes PyOpenCL special?

  • Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code.
  • Completeness. PyOpenCL puts the full power of OpenCL’s API at your disposal, if you wish. Every obscure get_info() query and all CL calls are accessible.
  • Automatic Error Checking. All errors are automatically translated into Python exceptions.
  • Speed. PyOpenCL’s base layer is written in C++, so all the niceties above are virtually free.
  • Helpful Documentation. You’re looking at it. ;)
  • Liberal license. PyOpenCL is open-source under the MIT license and free for commercial, academic, and private use.

Here’s an example, to give you an impression:

from __future__ import absolute_import
from __future__ import print_function
#!/usr/bin/env python
# -*- coding: utf-8 -*-

import numpy as np
import pyopencl as cl

a_np = np.random.rand(50000).astype(np.float32)
b_np = np.random.rand(50000).astype(np.float32)

ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)

mf = cl.mem_flags
a_g = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=a_np)
b_g = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=b_np)

prg = cl.Program(ctx, """
__kernel void sum(__global const float *a_g, __global const float *b_g, __global float *res_g) {
  int gid = get_global_id(0);
  res_g[gid] = a_g[gid] + b_g[gid];
}
""").build()

res_g = cl.Buffer(ctx, mf.WRITE_ONLY, a_np.nbytes)
prg.sum(queue, a_np.shape, None, a_g, b_g, res_g)

res_np = np.empty_like(a_np)
cl.enqueue_copy(queue, res_np, res_g)

# Check on CPU with Numpy:
print(res_np - (a_np + b_np))
print(np.linalg.norm(res_np - (a_np + b_np)))

(You can find this example as examples/demo.py in the PyOpenCL source distribution.)

Tutorials

Software that works with or enhances PyOpenCL

  • Two wrappers for clBLAS have emerged, one by Eric Hunsberger and one by Lars Ericson.
  • Bogdan Opanchuk’s reikna offers a variety of GPU-based algorithms (FFT, random number generation, matrix multiplication) designed to work with pyopencl.array.Array objects.
  • Gregor Thalhammer’s gpyfft provides a Python wrapper for the OpenCL FFT library clFFT from AMD.

If you know of a piece of software you feel that should be on this list, please let me know, or, even better, send a patch!

Contents

Note that this guide does not explain OpenCL programming and technology. Please refer to the official Khronos OpenCL documentation for that.

PyOpenCL also has its own web site, where you can find updates, new versions, documentation, and support.

Indices and tables