Hi,
Thanks for this. Comments inline.
On 7/22/16 12:13 PM, Nelson, Jarom wrote:
If you can move to HDF5 1.10, I would recommend independent files for
each MPI rank, and then create a master file (created independently
perhaps by rank 0) with Virtual Datasets linking in the data from each
rank in the format you need. Virtual Datasets can be created with file
matching patterns for dynamically increasing datasets, so you might
look into using that feature.
We don't have existing tools relying on a particular version, so we are
nominally free to move to HDF5 1.10.x. However, it won't be completely
straightforward because I have been relying for now on using the
homebrew version, which is currently 1.18.16. I'd have to dink the
recipe to use 1.10.x, which is not a showstopper.
I found this approach much faster than creating a collective file
(~5-10x speedup on a Lustre filesystem). You don’t need to do any
collective reads or writes, and I think we could even bypass using
parallel HDF5 altogether. Note, this will only work if you only ever
need to open the Virtual Dataset in parallel (i.e. by more than one
process) as non-collective read-only. If you need to have read-write
access to the master file, you can’t access a Virtual Dataset using
collective operations. You can, however, have as many processes as you
like read from a virtual dataset from a file opened as read-only.
If you have other tools that use your data but can’t move to HDF5
1.10, you can h5repack a file with Virtual Datasets to remove the
Virtual Datasets, and it should be compatible with HDF5 1.8 (use
h5repack from HDF5 1.10 patch 1 or later). This also worked well for
us and I was able to load a repacked file in IDL under a 1.8 HDF5
library. However h5repack is not a parallel application, so it can be
slow to repack a very large file, on the order minutes per GB.
After having thought a little more about likely parallel models, I think
now we can arrange that:
*
Only one rank will write to a particular dataset.
*
A dataset will not be read from in the same job in which it was written.
*
A dataset may be read by one or more ranks.
I *think* if that's the case, we could use a hierarchical multi-file
format without resorting to virtual datasets, no? I still have some
reading and experimenting to do, but if you have particular information
that would speak to the likely success of this approach, I'd be happy to
hear it.
Thanks,
Chris.
Jarom
*From:*Hdf-forum [mailto:[email protected]] *On
Behalf Of *Chris Green
*Sent:* Friday, July 22, 2016 9:32 AM
*To:* [email protected]
*Subject:* [Hdf-forum] Parallel dataset resizing strategies
Hi,
I am relatively new to HDF5 and HDF5/parallel, and although I have
experience with MPI it is not extensive. We are exploring ways of
saving data in parallel using HDF5 in a field in which it is
practically unknown up to now.
Our paradigm is "parallel modular event processing:"
* A typical job processes many "events."
* An event contains all of the interesting data (raw and processed)
associated with some time interval.
* Each event can be processed independently of all other events.
* Each event's data can be subdivided into internal components,
"data products."
* "Modules" are processing subunits which read or generate one or
more data products for each event.
* One can calculate a data dependency graph specifying the allowed
ordering and/or parallelism of modules processing one or more
events simultaneously for a given job configuration and event
structure.
We have been using h5py with HDF5 and OpenMPI to explore different
strategies for parallel I/O in a future parallel event-processing
framework. One of the approaches we have come up with so far is to
have one HDF5 dataset per unique data product / writer module
combination, keeping track of the different relevant sections of each
dataset via (for now) an external database. This works well in serial
tests, but in parallel tests we are running up against the constraint
that dataset resizing is a collective operation, meaning that all
ranks including non-writers will have to become aware of and duplicate
dataset resizing operations required by other writers. The problem
seems to get even worse if there's a possibility that two or more
instances of a module would need to extend and write to the same
dataset at the same time (while processing different events, say),
since they will have to coordinate and agree on the new size of the
dataset and their respective sections thereof.
Are we misunderstanding the problem, or is it really this hard? Has
anyone else hit upon a reasonable strategy for handling this or
something like it?
Any pointers appreciated.
Thanks,
Chris Green.
--
Chris Green<[email protected]> <mailto:[email protected]>, FNAL CS/SCD/ADSS/SSI/TAC;
'phone (630) 840-2167; Skype: chris.h.green;
IM:[email protected] <mailto:[email protected]>, chissgreen (AIM),
chris.h.green (Google Talk).
_______________________________________________
Hdf-forum is for HDF software users discussion.
[email protected]
http://lists.hdfgroup.org/mailman/listinfo/hdf-forum_lists.hdfgroup.org
Twitter: https://twitter.com/hdf5
--
Chris Green <[email protected]>, FNAL CS/SCD/ADSS/SSI/TAC;
'phone (630) 840-2167; Skype: chris.h.green;
IM: [email protected], chissgreen (AIM),
chris.h.green (Google Talk).
_______________________________________________
Hdf-forum is for HDF software users discussion.
[email protected]
http://lists.hdfgroup.org/mailman/listinfo/hdf-forum_lists.hdfgroup.org
Twitter: https://twitter.com/hdf5
