[Numpy-discussion] Cython-based OpenMP-accelerated quartic polynomial solver

[Stephan Hoyer]

On Tue, Oct 6, 2015 at 1:14 AM, Daπid <davidmenhur at gmail.com> wrote:

> One idea: what about creating a "parallel numpy"? There are a few
> algorithms that can benefit from parallelisation. This library would mimic
> Numpy's signature, and the user would be responsible for choosing the
> single threaded or the parallel one by just changing np.function(x, y) to
> pnp.function(x, y)
>

I would recommend taking a look at dask.array [1], which in many cases
works exactly like a parallel NumPy, though it also does lazy and
out-of-core computation. It's a new project, but it's remarkably mature --
we use it as an alternative array backend (to numpy) in xray, and it's also
being used by scikit-image.

[1] http://dask.pydata.org/en/latest/array.html


> If that were deemed a good one, what would be the best parallelisation
> scheme? OpenMP? Threads?
>

Dask uses threads. That works pretty well as long as all the hard work is
calling into something that releases the GIL (which includes NumPy, of
course).
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