If you use the default file access property list (serial) for HDF5, you can read or write a dataset greater than 2GB with one call.
As of HDF5-1.10.2 MPI-IO transfers larger than 2GB are also supported, as described in the release notes:
Previous releases of PHDF5 would fail when attempting to read or write greater than 2GB of data in a single IO operation. This issue stems principally from an MPI API whose definitions utilize 32 bit integers to describe the number of data elements and datatype that MPI should use to effect a data transfer. Historically, HDF5 has invoked MPI-IO with the number of elements in a contiguous buffer represented as the length of that buffer in bytes. Resolving the issue and thus enabling larger MPI-IO transfers is accomplished first, by detecting when a user IO request would exceed the 2GB limit as described above. Once a transfer request is identified as requiring special handling, PHDF5 now creates a derived datatype consisting of a vector of fixed sized blocks which is in turn wrapped within a single MPI_Type_struct to contain the vector and any remaining data. The newly created datatype is then used in place of MPI_BYTE and can be used to fulfill the original user request without encountering API errors.
WIth releases prior to HDF5-1.10.2, MPI-IO transfers larger than 2GB were not supported.
There were ways in HDF5 to get around this limitation in the standard by concatenating several derived datatypes, in order to reduce the count to a lower number than what a 32-bit integer can hold. However, this also broke ROMIO (the MPI-IO implementation used by almost all MPI libraries). This is a known limitation of ROMIO, where the most I/O ROMIO can do in a single operation is 2 GB. That is not the same problem as the 'count' parameter being 32 bytes, but rather a limit in ROMIO itself. So unless a fix is implemented in the ROMIO library, the work around the MPI standard (mentioned above) will not work.
The previous solution was to do multiple read/writes as necessary so that the total number of data read/written per call is less than 2 GB. We have a Parallel HDF5 Tutorial here:
Introduction to Parallel HDF5
See the hyperslab selection examples in the tutorial for how to select a subset of a dataset:
Writing and Reading Hyperslabs