issue_comments: 307977450
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| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/issues/1257#issuecomment-307977450 | https://api.github.com/repos/pydata/xarray/issues/1257 | 307977450 | MDEyOklzc3VlQ29tbWVudDMwNzk3NzQ1MA== | 1197350 | 2017-06-13T01:08:07Z | 2017-06-13T01:08:07Z | MEMBER | I am very interested. I have been doing a lot of benchmarking already wrt dask.distributed on my local cluster, focusing on performance with multi-terabyte datasets. At this scale, certain operations emerge as performance bottlenecks (e.g. index alignment of multi-file netcdf datasets, #1385). I think this should probably be done in AWS or Google Cloud. That way we can establish a consistent test environment for benchmarking. I might be able to pay for that (especially if our proposal gets funded)! |
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