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issue 1
- Wall time much greater than CPU time · 2 ✖
| 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 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 |
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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.
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Wall time much greater than CPU time 99026442 |
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