On Tue, Oct 6, 2015 at 1:14 AM, Daπid <davidmen...@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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