HDF5 is a unique technology suite that makes possible the management of extremely large and complex data collections.
Here are some key features of "HDF5":
· HDF5 does not limit the size of files or the size or number of objects in a file.
· The HDF5 format and library are extensible and designed to evolve gracefully to satisfy new demands.
· HDF5 functionality and data is portable across virtually all computing platforms and is distributed with C, C++, Java, and Fortran90 programming interfaces.
· HDF5 has a simple but versatile data model.
· The HDF5 data model supports complex data relationships and dependencies through its grouping and linking mechanisms.
· HDF5 accommodates many common types of metadata and arbitrary user-defined metadata.
· HDF5 supports a rich set of pre-defined datatypes as well as the creation of an unlimited variety of complex user-defined datatypes.
· Datatype definitions can be shared among objects in an HDF file, providing a powerful and efficient mechanism for describing data.
· Datatype definitions include information such as byte order (endian), size, and floating point representation, to fully describe how the data is stored, insuring portability to other platforms.
· HDF5, through its virtual file layer, offers extremely flexible storage and data transfer capabilities. Standard (Posix), Parallel, and Network I/O file drivers are provided with HDF5.
· Application developers can write additional file drivers to implement customized data storage or transport capabilities.
· The parallel I/O driver for HDF5 reduces access times on parallel systems by reading/writing multiple data streams simultaneously.
· HDF5 employs various compression, extensibility, and chunking strategies to improve access, management, and storage efficiency.
· HDF5 provides for external storage of raw data, allowing raw data to be shared among HDF5 files and/or applications, and often saving disk space.
· HDF5 enables datatype and spatial transformation during I/O operations.
· HDF5 data I/O functions can operate on selected subsets of the data, reducing transferred data volume and improving access speed.
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Here are some key features of "HDF5":
· HDF5 does not limit the size of files or the size or number of objects in a file.
· The HDF5 format and library are extensible and designed to evolve gracefully to satisfy new demands.
· HDF5 functionality and data is portable across virtually all computing platforms and is distributed with C, C , Java, and Fortran90 programming interfaces.
· HDF5 has a simple but versatile data model.
· The HDF5 data model supports complex data relationships and dependencies through its grouping and linking mechanisms.
· HDF5 accommodates many common types of metadata and arbitrary user-defined metadata.
· HDF5 supports a rich set of pre-defined datatypes as well as the creation of an unlimited variety of complex user-defined datatypes.
· Datatype definitions can be shared among objects in an HDF file, providing a powerful and efficient mechanism for describing data.
· Datatype definitions include information such as byte order (endian), size, and floating point representation, to fully describe how the data is stored, insuring portability to other platforms.
· HDF5, through its virtual file layer, offers extremely flexible storage and data transfer capabilities. Standard (Posix), Parallel, and Network I/O file drivers are provided with HDF5.
· Application developers can write additional file drivers to implement customized data storage or transport capabilities.
· The parallel I/O driver for HDF5 reduces access times on parallel systems by reading/writing multiple data streams simultaneously.
· HDF5 employs various compression, extensibility, and chunking strategies to improve access, management, and storage efficiency.
· HDF5 provides for external storage of raw data, allowing raw data to be shared among HDF5 files and/or applications, and often saving disk space.
· HDF5 enables datatype and spatial transformation during I/O operations.
· HDF5 data I/O functions can operate on selected subsets of the data, reducing transferred data volume and improving access speed.
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