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  • clarkfitzg · 1 ✖

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  • Automatic parallelization for dask arrays in apply_ufunc · 1 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
324692881 https://github.com/pydata/xarray/pull/1517#issuecomment-324692881 https://api.github.com/repos/pydata/xarray/issues/1517 MDEyOklzc3VlQ29tbWVudDMyNDY5Mjg4MQ== clarkfitzg 5356122 2017-08-24T16:50:45Z 2017-08-24T16:50:45Z MEMBER

Wow, this is great stuff!

What's rs.randn()?

When this makes it into the public facing API it would be nice to include some guidance on how the chunking scheme affects the run time. Imagine a plot with run time plotted as a function of chunk size or number of chunks. Of course it also depends on the data size and the number of cores available.

To say it in a different way, array1.chunk({'place': 10}) is a performance tuning parameter, semantically no different than array1.

More ambitiously I could imagine an API such as array1.chunk('place') or array1.chunk('auto') meaning to figure out a reasonable chunking scheme only once .compute() is called so that all the compute steps are known. Maybe this is more specific to dask than xarray. I believe it would also be difficult.

 
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   Automatic parallelization for dask arrays in apply_ufunc 252358450

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