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An HDF5-WRF module
MuQun Yang, Robert E. McGrath, Mike Folk
National Center for Supercomputing Applications
University of Illinois, Urbana-Champaign
What is WRF?
• Weather Research and Forecasting Model –
Framework for Weather Model Community
• Primarily uses sequential I/O with NetCDF
Goal
• Provide an optional HDF5 IO module for
WRF
• Investigate IO performance of WRF
Results
• Map WRF data model to HDF5
• Implemented two WRF IO modules
– Sequential IO module with compression
– Parallel IO module
– Parallel IO module is include in WRF
distribution

• Performance studies
– Presented to WRF workshop, conferences
Findings
1. Parallel HDF5-WRF module can greatly reduce
wall clock time in some WRF applications.
2. Sequential HDF5-WRF module can greatly
reduce the WRF file size with szip or shuffling
and deflate compression algorithms.
3. Parallel HDF5 library needs to be improved for
chunking storage.
Impact
• Parallel HDF5 WRF I/O is available to
modelling community
– Included in WRF 2.0 source distribution (May
2004)

• This investigation has led to improvements
in Parallel HDF5
For more information
• HDF5 WRF IO:
http://hdf.ncsa.uiuc.edu/apps/WRF-ROMS
• WRF: http://www.wrf-model.org/
Acknowledgements
• This work is part of NSF-funded Modeling
Environment for Atmospheric Discovery
Expedition (MEAD)
(http://www.ncsa.uiuc.edu/AboutUs/Focus
Areas/MEADExpedition.html)

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An HDF5-WRF module

  • 1. An HDF5-WRF module MuQun Yang, Robert E. McGrath, Mike Folk National Center for Supercomputing Applications University of Illinois, Urbana-Champaign
  • 2. What is WRF? • Weather Research and Forecasting Model – Framework for Weather Model Community • Primarily uses sequential I/O with NetCDF
  • 3. Goal • Provide an optional HDF5 IO module for WRF • Investigate IO performance of WRF
  • 4. Results • Map WRF data model to HDF5 • Implemented two WRF IO modules – Sequential IO module with compression – Parallel IO module – Parallel IO module is include in WRF distribution • Performance studies – Presented to WRF workshop, conferences
  • 5. Findings 1. Parallel HDF5-WRF module can greatly reduce wall clock time in some WRF applications. 2. Sequential HDF5-WRF module can greatly reduce the WRF file size with szip or shuffling and deflate compression algorithms. 3. Parallel HDF5 library needs to be improved for chunking storage.
  • 6. Impact • Parallel HDF5 WRF I/O is available to modelling community – Included in WRF 2.0 source distribution (May 2004) • This investigation has led to improvements in Parallel HDF5
  • 7. For more information • HDF5 WRF IO: http://hdf.ncsa.uiuc.edu/apps/WRF-ROMS • WRF: http://www.wrf-model.org/
  • 8. Acknowledgements • This work is part of NSF-funded Modeling Environment for Atmospheric Discovery Expedition (MEAD) (http://www.ncsa.uiuc.edu/AboutUs/Focus Areas/MEADExpedition.html)