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The need for a standardized method to exchange scientific data between High Performance Computing codes and tools lead to the development of the eXtensible Data Model and Format (XDMF) . Uses for XDMF range from a standard format used by HPC codes to take advantage of widely used visualization programs like ParaView and EnSight, to a mechanism for performing coupled calculations using multiple, previously stand alone codes. | |||
XDMF categorizes data by two main attributes; size and function. Data can be Light (typically less than about a thousand values) of Heavy (megabytes, terabytes, etc.). In addition to raw values, data can refer to Format (rank and dimensions of an array) or Model (how that data is to be used. i.e. XYZ coordinates vs. Vector components). | |||
XDMF uses XML to store Light data and to describe the data Model. HDF5 is used to store Heavy data. The data Format is stored redundantly in both XML and HDF5. This allows tools to parse XML to determine the resources that will be required to access the Heavy data. | |||
While not required, a C++ API is provided to read and write XDMF data. This API has also been wrapped so it is available from popular languages like Python, Tcl, and Java. The API is not necessary in order to produce or consume XDMF data. Currently several HPC codes that already produced HDF5 data, use native text output to produce the XML necessary for valid XDMF. | |||
[[XDMF Model and Format]]XdmfFormat.html | |||
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Revision as of 07:44, 3 May 2007
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The need for a standardized method to exchange scientific data between High Performance Computing codes and tools lead to the development of the eXtensible Data Model and Format (XDMF) . Uses for XDMF range from a standard format used by HPC codes to take advantage of widely used visualization programs like ParaView and EnSight, to a mechanism for performing coupled calculations using multiple, previously stand alone codes.
XDMF categorizes data by two main attributes; size and function. Data can be Light (typically less than about a thousand values) of Heavy (megabytes, terabytes, etc.). In addition to raw values, data can refer to Format (rank and dimensions of an array) or Model (how that data is to be used. i.e. XYZ coordinates vs. Vector components).
XDMF uses XML to store Light data and to describe the data Model. HDF5 is used to store Heavy data. The data Format is stored redundantly in both XML and HDF5. This allows tools to parse XML to determine the resources that will be required to access the Heavy data.
While not required, a C++ API is provided to read and write XDMF data. This API has also been wrapped so it is available from popular languages like Python, Tcl, and Java. The API is not necessary in order to produce or consume XDMF data. Currently several HPC codes that already produced HDF5 data, use native text output to produce the XML necessary for valid XDMF.
XDMF Model and FormatXdmfFormat.html
