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  • JoyMonteiro · 3 ✖

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  • Making xray use multiple cores · 3 ✖

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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
162463237 https://github.com/pydata/xarray/issues/672#issuecomment-162463237 https://api.github.com/repos/pydata/xarray/issues/672 MDEyOklzc3VlQ29tbWVudDE2MjQ2MzIzNw== JoyMonteiro 7300413 2015-12-07T09:33:17Z 2015-12-07T09:33:17Z NONE

You were right, my chunk sizes were too large. It did not matter how many threads dask used either (4 vs. 8). The I/O component is still high, but that is also because I'm writing the final computed DataArray to disk.

Thanks!

 
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   Making xray use multiple cores 120681918
162419595 https://github.com/pydata/xarray/issues/672#issuecomment-162419595 https://api.github.com/repos/pydata/xarray/issues/672 MDEyOklzc3VlQ29tbWVudDE2MjQxOTU5NQ== JoyMonteiro 7300413 2015-12-07T06:11:39Z 2015-12-07T06:11:39Z NONE

Hello, I ran it with the dask profiler, and I looked at the top output disaggregated by core. It does seem to use multiple cores, but it seems to be using 8 threads when I looked at prof.visualize() (hyperthreading :P) and I feel this is killing performance.

How can I control how many threads to use?

Thanks, Joy 

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   Making xray use multiple cores 120681918
162417283 https://github.com/pydata/xarray/issues/672#issuecomment-162417283 https://api.github.com/repos/pydata/xarray/issues/672 MDEyOklzc3VlQ29tbWVudDE2MjQxNzI4Mw== JoyMonteiro 7300413 2015-12-07T05:46:15Z 2015-12-07T05:46:15Z NONE

I was trying to read ERA-Interim data, calculate anomalies using ds = ds - ds.mean(dim='longitude'), and similar operations along the time axis. Are such operations restricted to single cores?

Just multiplying two datasets (u*v) seems to be faster, though top shows two cores being used (I have 4 physical cores).

TIA, Joy

 
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   Making xray use multiple cores 120681918

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