Hdf5 no way to run a parallel program

You can use serial HDF5 because each stream is a wholly independent file. Then, their work on the file has to be coordinated e. But, it sounds like MPI parallelism is not really what you are looking for and that is especially true if you only want N:N case. HDF5 will not do much to help you here. The support in HDF5 for calling it from multiple threads within the same executable is pretty limited.

The locking is very coarse grained and in all likelihood winds up serializing the threads. Finally, your note gives me the impression that maybe what you are looking for is one or more processes whose number grows and maybe shrinks over the life of the application and where each processor needs to write some data.

If that is your ultimate goal, I think there are various ways you could try to implement that both with and without MPI and parallel HDF5. Then, if you need to change the number of tasks, you would close the HDF5 file, close the communicator and create a new communicator on a different number of tasks and re-open the file with that new communicator. There is a lot involved there but I think it could be made to work.

We actually started our implementation tentative right from that blog article that you linked! I got the following.

This message basically states that you do not have enough hardware resources to run the 6 processes you requested OMPI assumes you are running for performance and refuses by default to oversubscribe your hardware resources. You can find more information in our FAQ. However, once I realised this, I still have issues:.

H5repack error - make check and HDF5 make test in error suggest that these tests are unreliable and to use the -i flag. This does work, but it would be good to have some confirmation from an HDF5 developer if there are plans to fix the tests. I factually know ompi works. You will run into problems with slurm perhaps, but ompi 4. Yes, on Ubuntu 16 I have, and it still fails. This is accomplished through the mpi4py Python package, which provides excellent, complete Python bindings for MPI. To run an MPI-based parallel program, use the mpiexec program to launch several parallel instances of Python:.

The mpi4py package includes all kinds of mechanisms to share data between processes, synchronize, etc. Check out the mpi4py web site for more information and a great tutorial. Note that --enable-shared is required. You can check to see what build options were used by using the program h5cc :.

The parallel features of HDF5 are mostly transparent. To open a file shared across multiple processes, use the mpio file driver.