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- Making xray use multiple cores · 2 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
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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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