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  • fujiisoup · 2 ✖

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  • xarray.dot() dask problems · 2 ✖

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  • MEMBER · 2 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
383754980 https://github.com/pydata/xarray/issues/2074#issuecomment-383754980 https://api.github.com/repos/pydata/xarray/issues/2074 MDEyOklzc3VlQ29tbWVudDM4Mzc1NDk4MA== fujiisoup 6815844 2018-04-23T23:32:33Z 2018-04-23T23:32:33Z MEMBER

@crusaderky , Thanks for the detailed benchmarking. Further note:

  • xr.dot uses tensordot if possible, as when I implemented dask did not have einsum. In the other cases, we use dask.atop with np.einsum.

In your example, bench(100, False, ['t'], '...i,...i') uses dask.tensordot, bench(100, True, ['t'], '...i,...i') uses np.einsum.

bench(100, True, [], ...i,...i->...i) also uses np.einsum. But I have no idea yet why dot(a, b, dims=[]) is faster than a * b.

 
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   xarray.dot() dask problems 316618290
383419435 https://github.com/pydata/xarray/issues/2074#issuecomment-383419435 https://api.github.com/repos/pydata/xarray/issues/2074 MDEyOklzc3VlQ29tbWVudDM4MzQxOTQzNQ== fujiisoup 6815844 2018-04-22T23:05:39Z 2018-04-22T23:06:05Z MEMBER

xr.dot was implemented before dask/dask#3412 was merged, and thus it is not very efficient for dask now.

The proposed solution is to simply wait for dask/dask#3412 to reach the next release and then reimplement xarray.dot to use dask.array.einsum.

Agreed. I think the reimplementation would be easy, https://github.com/pydata/xarray/blob/99b457ce5859bd949cfea4671db5150c7297843a/xarray/core/computation.py#L1039-L1043 dask='parallelrized' -> dask='allow'

 
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   xarray.dot() dask problems 316618290

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