compyle 0.9.1

Installation

Compyle is itself largely pure Python but depends on numpy and requires either Cython or PyOpenCL or PyCUDA along with the respective backends of a C/C++ compiler, OpenCL and CUDA. If you are only going to execute code on a CPU then all you need is Cython.

You should be able to install Compyle by doing:

$ pip install compyle

A simple example

Here is a very simple example:

from compyle.api import Elementwise, annotate, wrap, get_config
import numpy as np

@annotate
def axpb(i, x, y, a, b):
    y[i] = a*sin(x[i]) + b

x = np.linspace(0, 1, 10000)
y = np.zeros_like(x)
a, b = 2.0, 3.0

backend = 'cython'
get_config().use_openmp = True
x, y = wrap(x, y, backend=backend)
e = Elementwise(axpb, backend=backend)
e(x, y, a, b)

This will execute the elementwise operation in parallel using OpenMP with Cython. The code is auto-generated, compiled and called for you transparently. The first time this runs, it will take a bit of time to compile everything but the next time, this is cached and will run much faster.

If you just change the backend = 'opencl', the same exact code will be executed using PyOpenCL and if you change the backend to 'cuda', it will execute via CUDA without any other changes to your code. This is obviously a very trivial example, there are more complex examples available as well.

Examples

Some simple examples and benchmarks are available in the examples directory.

You may also run these examples on the Google Colab notebook