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  • aquasync · 2 ✖

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  • bottleneck : Wrong mean for float32 array · 2 ✖

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  • NONE · 2 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
464002579 https://github.com/pydata/xarray/issues/1346#issuecomment-464002579 https://api.github.com/repos/pydata/xarray/issues/1346 MDEyOklzc3VlQ29tbWVudDQ2NDAwMjU3OQ== aquasync 5469 2019-02-15T11:06:06Z 2019-02-15T11:06:06Z NONE

Ah ok, I suppose bottleneck is indeed now avoided for float32 xarray. Yeah that issue is for a different function, but the source of the problem and proposed solution in the thread is the same - use higher precision intermediates for float32 (double arithmetic); a small speed vs accuracy/precision trade off.

 
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   bottleneck : Wrong mean for float32 array 218459353
458427512 https://github.com/pydata/xarray/issues/1346#issuecomment-458427512 https://api.github.com/repos/pydata/xarray/issues/1346 MDEyOklzc3VlQ29tbWVudDQ1ODQyNzUxMg== aquasync 5469 2019-01-29T06:52:01Z 2019-01-29T06:52:01Z NONE

Is it worth changing bottleneck to use double for single precision reductions? AFAICT this is a matter of changing npy_DTYPE0 to double in the float{64,32} versions of functions in reduce_template.c.

 
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   bottleneck : Wrong mean for float32 array 218459353

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