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  • shoyer · 2 ✖

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  • Making xray use multiple cores · 2 ✖

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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
162426520 https://github.com/pydata/xarray/issues/672#issuecomment-162426520 https://api.github.com/repos/pydata/xarray/issues/672 MDEyOklzc3VlQ29tbWVudDE2MjQyNjUyMA== shoyer 1217238 2015-12-07T06:46:40Z 2015-12-07T06:46:40Z MEMBER

Those sorts of operations should be easily parallelized, although depending on what you're doing with the data they might also be IO bound. It's worth experimenting with chunk sizes. For control on the number of threads, see his page: http://dask.pydata.org/en/latest/scheduler-overview.html#configuring-the-schedulers

 
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   Making xray use multiple cores 120681918
162416658 https://github.com/pydata/xarray/issues/672#issuecomment-162416658 https://api.github.com/repos/pydata/xarray/issues/672 MDEyOklzc3VlQ29tbWVudDE2MjQxNjY1OA== shoyer 1217238 2015-12-07T05:38:39Z 2015-12-07T05:38:39Z MEMBER

What sort of computation are you doing? Some tasks are limited to a single core, notably reading netCDF4 files with in-file compression. Dask's profiler may be helpful here.

 
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   Making xray use multiple cores 120681918

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