PyOpenCL gives you easy, Pythonic access to the OpenCL parallel computation API. What makes PyOpenCL special?
Here’s an example, to give you an impression:
#!/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.)
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!
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.