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  • rabernat · 2 ✖

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  • PERF: Add benchmarking? · 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
307977450 https://github.com/pydata/xarray/issues/1257#issuecomment-307977450 https://api.github.com/repos/pydata/xarray/issues/1257 MDEyOklzc3VlQ29tbWVudDMwNzk3NzQ1MA== rabernat 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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   PERF: Add benchmarking? 206632333
278845582 https://github.com/pydata/xarray/issues/1257#issuecomment-278845582 https://api.github.com/repos/pydata/xarray/issues/1257 MDEyOklzc3VlQ29tbWVudDI3ODg0NTU4Mg== rabernat 1197350 2017-02-10T03:04:31Z 2017-02-10T03:04:31Z MEMBER

Another 👍 for benchmarking. Especially as we start to get deep into integrating dask.distributed, having robust performance benchmarks will be very useful. One challenge is where to deploy the benchmarks. TravisCI might not be ideal, since performance can vary depending on competition from other virtual machines on the same system.

 
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   PERF: Add benchmarking? 206632333

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