Parallel HDF5

Starting with version 2.2.0, h5py includes support for Parallel HDF5. This is the “native” way to use HDF5 in a parallel computing environment.

How does Parallel HDF5 work?

Parallel HDF5 is a configuration of the HDF5 library which lets you share open files across multiple parallel processes. It uses the MPI (Message Passing Interface) standard for interprocess communication. Consequently, when using Parallel HDF5 from Python, your application will also have to use the MPI library.

This is accomplished through the mpi4py Python package, which provides excellent, complete Python bindings for MPI. Here’s an example “Hello World” using mpi4py:

>>> from mpi4py import MPI
>>> print "Hello World (from process %d)" % MPI.COMM_WORLD.rank

To run an MPI-based parallel program, use the mpiexec program to launch several parallel instances of Python:

$ mpiexec -n 4 python demo.py
Hello World (from process 1)
Hello World (from process 2)
Hello World (from process 3)
Hello World (from process 0)

The mpi4py package includes all kinds of mechanisms to share data between processes, synchronize, etc. It’s a different flavor of parallelism than, say, threads or multiprocessing, but easy to get used to.

Check out the mpi4py web site for more information and a great tutorial.

Building against Parallel HDF5

HDF5 must be built with at least the following options:

$./configure --enable-parallel --enable-shared

Note that --enable-shared is required.

Often, a “parallel” version of HDF5 will be available through your package manager. You can check to see what build options were used by using the program h5cc:

$ h5cc -showconfig

Once you’ve got a Parallel-enabled build of HDF5, h5py has to be compiled in “MPI mode”. This is simple; set your default compiler to the mpicc wrapper and build h5py with the --mpi option:

$ export CC=mpicc
$ python setup.py build --mpi [--hdf5=/path/to/parallel/hdf5]

Using Parallel HDF5 from h5py

The parallel features of HDF5 are mostly transparent. To open a file shared across multiple processes, use the mpio file driver. Here’s an example program which opens a file, creates a single dataset and fills it with the process ID:

from mpi4py import MPI
import h5py

rank = MPI.COMM_WORLD.rank  # The process ID (integer 0-3 for 4-process run)

f = h5py.File('parallel_test.hdf5', 'w', driver='mpio', comm=MPI.COMM_WORLD)

dset = f.create_dataset('test', (4,), dtype='i')
dset[rank] = rank

f.close()

Run the program:

$ mpiexec -n 4 python demo2.py

Looking at the file with h5dump:

$ h5dump parallel_test.hdf5
HDF5 "parallel_test.hdf5" {
GROUP "/" {
   DATASET "test" {
      DATATYPE  H5T_STD_I32LE
      DATASPACE  SIMPLE { ( 4 ) / ( 4 ) }
      DATA {
      (0): 0, 1, 2, 3
      }
   }
}
}

Collective versus independent operations

MPI-based programs work by launching many instances of the Python interpreter, each of which runs your script. There are certain requirements imposed on what each process can do. Certain operations in HDF5, for example, anything which modifies the file metadata, must be performed by all processes. Other operations, for example, writing data to a dataset, can be performed by some processes and not others.

These two classes are called collective and independent operations. Anything which modifies the structure or metadata of a file must be done collectively. For example, when creating a group, each process must participate:

>>> grp = f.create_group('x')  # right

>>> if rank == 1:
...     grp = f.create_group('x')   # wrong; all processes must do this

On the other hand, writing data to a dataset can be done independently:

>>> if rank > 2:
...     dset[rank] = 42   # this is fine

MPI atomic mode

HDF5 versions 1.8.9+ support the MPI “atomic” file access mode, which trades speed for more stringent consistency requirements. Once you’ve opened a file with the mpio driver, you can place it in atomic mode using the settable atomic property:

>>> f = h5py.File('parallel_test.hdf5', 'w', driver='mpio', comm=MPI.COMM_WORLD)
>>> f.atomic = True

More information

Parallel HDF5 is a new feature in h5py. If you have any questions, feel free to ask on the mailing list (h5py at google groups). We welcome bug reports, enhancements and general inquiries.