issue_comments: 116162351

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https://github.com/pydata/xarray/issues/444#issuecomment-116162351 https://api.github.com/repos/pydata/xarray/issues/444 116162351 MDEyOklzc3VlQ29tbWVudDExNjE2MjM1MQ== 306380 2015-06-27T22:12:37Z 2015-06-27T22:12:37Z MEMBER

There was a similar problem with PyTables, which didn't support concurrency well. This resulted in the from-hdf5 function in dask array which uses explicit locks to avoid concurrent access.

We could repeat this treatment more generally without much trouble to force single threaded access on access but still allow parallelism otherwise. On Jun 27, 2015 2:33 PM, "Răzvan Rădulescu" notifications@github.com wrote:

So I just tried @mrocklin https://github.com/mrocklin's idea with using single-threaded stuff. This seems to fix the segmentation fault, but I am very curious as to why there's a problem with working in parallel. I tried two different hdf5 libraries (I think version 1.8.13 and 1.8.14) but I got the same segmentation fault. Anyway, working on a single thread is not a big deal, I'll just do that for the time being... I already tried gdb on python but I'm not experienced enough to make heads or tails of it... I have the gdb backtrace here https://gist.github.com/razvanc87/0986c4f7a591772e1778 but I don't know what to do with it...

@shoyer https://github.com/shoyer, the files are not the issue here, they're the same ones I provided in #443 https://github.com/xray/xray/issues/443.

Question: does the hdf5 library need to be built with parallel support (mpi or something) maybe?... thanks guys

— Reply to this email directly or view it on GitHub https://github.com/xray/xray/issues/444#issuecomment-116146897.

 
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