If you are new to HDF5 please read the Learning the Basics topic first.
There were several requirements that we had for Parallel HDF5 (PHDF5). These were:
Parallel HDF5 files had to be compatible with serial HDF5 files and sharable between different serial and parallel platforms.
Parallel HDF5 had to be designed to have a single file image to all processes, rather than having one file per process. Having one file per process can cause expensive post processing, and the files are not usable by different processes.
A standard parallel I/O interface had to be portable to different platforms.
With these requirements of HDF5 our initial target was to support MPI programming, but not for shared memory programming. We had done some experimentation with thread-safe support for Pthreads and for OpenMP, and decided to use these.
Implementation requirements were to:
Not use Threads, since they were not commonly supported in 1998 when we were looking at this.
Not have a reserved process, as this might interfere with parallel algorithms.
Not spawn any processes, as this is not even commonly supported now.
The following shows the Parallel HDF5 implementation layers:
This tutorial assumes that you are somewhat familiar with parallel programming with MPI (Message Passing Interface).
If you are not familiar with parallel programming, here is a tutorial that may be of interest:
Some of the terms that you must understand in this tutorial are:
Allows a group of processes to communicate with each other.
Following are the MPI routines for initializing MPI and the communicator and finalizing a session with MPI:
C Fortran Description -- ------- ----------- MPI_Init MPI_INIT Initialize MPI (MPI_COMM_WORLD usually) MPI_Comm_size MPI_COMM_SIZE Define how many processes are contained in the communicator MPI_Comm_rank MPI_COMM_RANK Define the process ID number within the communicator (from 0 to n-1) MPI_Finalize MPI_FINALIZE Exiting MPI
Parallel HDF5 opens a parallel file with a communicator. It returns a file handle to be used for future access to the file.
All processes are required to participate in the collective Parallel HDF5 API. Different files can be opened using different communicators.
Examples of what you can do with the Parallel HDF5 collective API:
Once a file is opened by the processes of a communicator:
Please refer to the Supported Configuration Features Summary in the release notes for the current release of HDF5 for an up-to-date list of the platforms that we support Parallel HDF5 on.
The programming model for creating and accessing a file is as follows:
1. Set up an access template object to control the file access mechanism.
2. Open the file.
3. Close the file.
Each process of the MPI communicator creates an access template and sets it up with MPI parallel access information. This is done with the H5Pcreate / h5pcreate_f call to obtain the file access property list and the H5Pset_fapl_mpio / h5pset_fapl_mpio_f call to set up parallel I/O access.
Following is example code for creating an access template in HDF5:
C: 23 MPI_Comm comm = MPI_COMM_WORLD; 24 MPI_Info info = MPI_INFO_NULL; 25 26 /* 27 * Initialize MPI 28 */ 29 MPI_Init(&argc, &argv); 30 MPI_Comm_size(comm, &mpi_size); 31 MPI_Comm_rank(comm, &mpi_rank); 32 33 /* 34 * Set up file access property list with parallel I/O access 35 */ 36 plist_id = H5Pcreate(H5P_FILE_ACCESS); 37 H5Pset_fapl_mpio(plist_id, comm, info); FORTRAN90: 23 comm = MPI_COMM_WORLD 24 info = MPI_INFO_NULL 25 26 CALL MPI_INIT(mpierror) 27 CALL MPI_COMM_SIZE(comm, mpi_size, mpierror) 28 CALL MPI_COMM_RANK(comm, mpi_rank, mpierror) 29 ! 30 ! Initialize FORTRAN interface 31 ! 32 CALL h5open_f(error) 33 34 ! 35 ! Setup file access property list with parallel I/O access. 36 ! 37 CALL h5pcreate_f(H5P_FILE_ACCESS_F, plist_id, error) 38 CALL h5pset_fapl_mpio_f(plist_id, comm, info, error)
The following example programs create an HDF5 file using Parallel HDF5: C F90
The programming model for accessing a dataset with Parallel HDF5 is:
H5Dcreate (C) / h5dcreate_f (F90)
Then set the data transfer mode to either use independent I/O access or to use collective I/O, with a call to:H5Pset_dxpl_mpio (C) / h5pset_dxpl_mpio_f (F90)
Following are the parameters required by this call:
C: herr_t H5Pset_dxpl_mpio (hid_t dxpl_id, H5FD_mpio_xfer_t xfer_mode ) dxpl_id IN: Data transfer property list identifier xfer_mode IN: Transfer mode: H5FD_MPIO_INDEPENDENT - use independent I/O access (default) H5FD_MPIO_COLLECTIVE - use collective I/O access F90: h5pset_dxpl_mpi_f (prp_id, data_xfer_mode, hdferr) prp_id IN: Property List Identifer (INTEGER (HID_T)) data_xfer_mode IN: Data transfer mode (INTEGER) H5FD_MPIO_INDEPENDENT_F (0) H5FD_MPIO_COLLECTIVE_F (1) hdferr IN: Error code (INTEGER)
All processes that have opened a dataset may do collective I/O. Each process may do an independent and arbitrary number of data I/O access calls, using:H5Dwrite (C) / h5dwrite_f (F90)
If a dataset is unlimited, you can extend it with a collective call to:
The following code demonstrates a collective write using Parallel HDF5:
C: 95 /* 96 * Create property list for collective dataset write. 97 */ 98 plist_id = H5Pcreate (H5P_DATASET_XFER); 99 H5Pset_dxpl_mpio (plist_id, H5FD_MPIO_COLLECTIVE); 100 101 status = H5Dwrite (dset_id, H5T_NATIVE_INT, memspace, filespace, 102 plist_id, data); F90: 108 ! Create property list for collective dataset write 109 ! 110 CALL h5pcreate_f (H5P_DATASET_XFER_F, plist_id, error) 111 CALL h5pset_dxpl_mpio_f (plist_id, H5FD_MPIO_COLLECTIVE_F, error) 112 113 ! 114 ! Write the dataset collectively. 115 ! 116 CALL h5dwrite_f (dset_id, H5T_NATIVE_INTEGER, data, dimsfi, error, & 117 file_space_id = filespace, mem_space_id = memspace, xfer_prp = plist_id)
The following example programs create a dataset in an HDF5 file using Parallel HDF5: C F90
The programming model for writing and reading hyperslabs is:
The memory and file hyperslabs in the first step are defined with the H5Sselect_hyperslab (C) / h5sselect_hyperslab_f (F90).
The start (or offset), count, stride, and block parameters define the portion of the dataset to write to. By changing the values of these parameters you can write hyperslabs with Parallel HDF5 by contiguous hyperslab, by regularly spaced data in a column/row, by patterns, and by chunks: