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  • Wall time much greater than CPU time · 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
135510417 https://github.com/pydata/xarray/issues/516#issuecomment-135510417 https://api.github.com/repos/pydata/xarray/issues/516 MDEyOklzc3VlQ29tbWVudDEzNTUxMDQxNw== wesleybowman 3688009 2015-08-27T18:11:43Z 2015-08-27T18:11:43Z NONE

using ncdump -hs, I found the chunk sizes of any of the files to be: _ChunkSizes = 1, 90, 180 ;

Using that, it took even more time:

``` datal = xray.open_mfdataset(filename, chunks={'time':1, 'lat':90, 'lon':180})

In [7]: %time datal.tasmax[:, 360, 720].values CPU times: user 3min 3s, sys: 59.4 s, total: 4min 3s Wall time: 12min 8s ```

I should say that I am using open source data, and therefore do not control how the original data is being chunked. This is also using open_mfdataset on around 100 files

 
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   Wall time much greater than CPU time 99026442
129995032 https://github.com/pydata/xarray/issues/516#issuecomment-129995032 https://api.github.com/repos/pydata/xarray/issues/516 MDEyOklzc3VlQ29tbWVudDEyOTk5NTAzMg== wesleybowman 3688009 2015-08-11T18:01:04Z 2015-08-11T18:01:04Z NONE

Hmm. I moved the uncompressed files to my local hard drive, and I am still getting a lot more wall time than CPU time. 31 seconds would be more than acceptable, but 8 minutes is really pushing it.

%time datal.tasmax[:, 360, 720].values CPU times: user 25.2 s, sys: 5.83 s, total: 31 s Wall time: 8min 1s

 
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   Wall time much greater than CPU time 99026442

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