[petsc-users] Problem with PETSc + HDF5 VecView

[Håkon Strandenes]

My local HPC group have found a solution to this problem:
On MPT it is possible to set an environment variable MPI_TYPE_DEPTH with 
default value 8. The MPI_TYPE_DEPTH variable limits the maximum depth of 
derived datatypes that an application can create.

I have found that setting this to at least 32 will make my examples run 
perfectly on up to 256 processes. No error messages what so ever, and in 
my simple load and write dataset roundtrip h5diff compares the two 
datasets and finds then identical. I also notice that Leibniz 
Rechenzentrum recommend to set this variable to 100 (or some other 
suitably large value) when using NetCDF together with MPT 
(https://www.lrz.de/services/software/io/netcdf/).

This bug have been a pain in the (***)... Perhaps it is worthy a FAQ entry?

Thanks for your time and effort.

Regards,
Håkon Strandenes


On 26. nov. 2014 08:01, Håkon Strandenes wrote:
>
>
> On 25. nov. 2014 22:40, Matthew Knepley wrote:
>> On Tue, Nov 25, 2014 at 2:34 PM, Håkon Strandenes <haakon at hakostra.net
>> <mailto:haakon at hakostra.net>> wrote:
>>
>> (...)
>>
>> First, this is great debugging.
>
> Thanks.
>
>>
>> Second, my reading of the HDF5 document you linked to says that either
>> selection should be valid:
>>
>>    "For non-regular hyperslab selection, parallel HDF5 uses independent
>> IO internally for this option."
>>
>> so it ought to fall back to the INDEPENDENT model if it can't do
>> collective calls correctly. However,
>> it appears that the collective call has bugs.
>>
>> My conclusion: Since you have determined that changing the setting to
>> INDEPENDENT produces
>> correct input/output in all the test cases, and since my understanding
>> of the HDF5 documentation is
>> that we should always be able to use COLLECTIVE as an option, this is an
>> HDF5 or MPT bug.
>
> I have conducted yet another test:
> My example (ex10) that I previously posted to the mailing list was set
> up with 250 grid points along each axis. When the topic on chunking was
> brought to the table, I realized that 250 is not evenly dividable on
> four. The example failed on 64 processes, that is four processes along
> each direction (the division is 62 + 62 + 63 + 63 = 250).
>
> So I have recompiled "my ex10" with 256 gridpoints in each direction. It
> turns out that this does in deed run successfully on 64 nodes. Great! It
> also runs on 128 processes, that is a 8x4x4 decomposition. However it
> does not run on 125 processes, that is a 5x5x5 decomposition.
>
> The same pattern is clear if I run my example with 250^3 grid points. It
> does not run on numbers like 64 and 128, but does run successfully on
> 125 processes, again only when the sub-domains are of exactly equal size
> (in this case the domain is divided as 5x5x5).
>
> However, I still believe that there is bugs. I did my "roundtrip" by
> loading a dataset and immediately writing the same dataset to a
> different file, this time a 250^3 dataset on 125 processes. It did not
> "pass" this test, i.e. the written dataset was just garbage. I have not
> yet identified if the garbling is introduced in the reading or writing
> of the dataset.
>
>>
>> Does anyone else see the HDF5 differently? Also, it really looks to me
>> like HDF5 messed up the MPI
>> data type in the COLLECTIVE picture below, since it appears to be sliced
>> incorrectly.
>>
>> Possible Remedies:
>>
>>    1) We can allow you to turn off H5Pset_dxpl_mpio()
>>
>>    2) Send this test case to the MPI/IO people at ANL
>>
>> If you think 1) is what you want, we can do it. If you can package this
>> work for 2), it would be really valuable.
>
> I will be fine editing gr2.c manually each time this file is changed (I
> use the sources from Git). But *if* this not a bug in MPT, but a bug in
> PETSc or HDF5 it should be fixed... Because it is that kind of bug that
> is extremely annoying and a read pain to track down.
>
> Perhaps the HDF5 mailing list could contribute in this issue?
>
>>
>>    Thanks,
>>
>>      Matt
>>
>>     Tanks for your time.
>>
>>     Best regards,
>>     Håkon Strandenes
>>
>>
>
> Again thanks for your time.
>
> Regards,
> Håkon
>
>>
>>
>>
>>
>> --
>> What most experimenters take for granted before they begin their
>> experiments is infinitely more interesting than any results to which
>> their experiments lead.
>> -- Norbert Wiener