HIGH PERFORMANCE COMPUTING IN OPERATIONAL METEOROLOGY

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HIGH PERFORMANCE COMPUTING IN OPERATIONAL
METEOROLOGY

• Geoff Love
• President of the WMO
• Commission for Basic Systems

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OVERVIEW

• A couple of definitions
• Where we have come from
• Where we are now
• Where we might be going in the short- and
  longer-terms

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DEFINITIONS

• High Performance Computing Computing performed
  on a system that, at the time of its
  commissioning, qualified as one of the top 500
  (publicly benchmarked) systems in terms of
  ability to deliver sustained floating point
  operations.

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DEFINITIONS

• Operational Meteorology Operational requires
  that production systems are supported in a robust
  way (code upgrades are easily facilitated, data
  management is streamlined, visualisation tools
  are available, etc.) - to be distinguished
  from, for example, the research environment.
  Meteorology includes both climate and weather

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WHERE WE HAVE COME FROM ?

• YEAR MACHINE GFLOP
• 1968 IBM 360 0.00065
• 1982 FACOM M200 0.006
• 1988 ETA 10P 0.12
• 1990 CRAY X-MP 0.23
• 1992 CRAY Y-MP2E 0.7
• 1993 CRAY Y-MP3E 1
• 1995 CRAY Y-MP4E 1.3
• 1997 NEC SX-4 32
• 1998 2xNEC SX-4 64
• 1999 NEC SX-5 104
• 2000 NEC SX-5 128
• 2001 2xNEC SX-5 256

A
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WHERE WE HAVE COME FROM ?

A
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WHERE WE HAVE COME FROM ?

A
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WHERE WE HAVE COME FROM ?

A
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SYSTEM EVOLUTION

• 1968 Regional analysis, regional prediction
• 1984 Experimental hemispheric prediction,
  regional nesting
• 1986 Hemispheric prediction, regional prediction
• 1990 Global prediction
• 1994 Regional assimilation, global assimilation

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WHERE ARE WE NOW ?

• Global and regional 3-D variational scheme for
  data assimilation.
• Global, regional and mesoscale atmospheric and
  ocean forecast systems. Ensemble production.
• Air quality modelling, including a variety of
  chemistry options.
• Dispersion, tropical cyclone and hydrologic
  modelling.
• Climate simulation, regional downscaling - eg.,
  catchment scale water balances.

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• Multi-operational
• system environment

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Visualisation
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WHAT IS NEEDED TO SUPPORT THIS EFFORT ?

• Improving hardware, but of relatively stable
  design.
• Robust hardware.
• Software which can evolve to take best advantage
  of the hardware but is sufficiently stable so as
  to support older code, robust data management and
  modern visualisation (and the like).
• Use of industry standards.
• A mechanism to develop and maintain those
  standards likely to be peculiar to meteorology.

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FUTURE TRENDS

• .

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FUTURE TRENDS

• .

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FUTURE TRENDS

• Centres will specialise - no one will do it all.
• There will be greater, and more successful
  efforts to integrate models from different
  disciplines.
• Systems will be improved incrementally (modular
  architecture).
• End-to-end modelling, including data quality
  monitoring, assimilation, analysis and prognosis,
  visualisation, archival, product generation and
  dissemination will occur.

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FUTURE TRENDS

• The ultimate goal is clearly earth-system
  simulation
• The ultimate architecture would appear to be
  clusters of powerful computing and data storage
  environments (the level of interaction between
  modules, and time-critical nature of the various
  applications / modules will drive processor
  power-proximity relationship).
• Data management in meteorology will accommodate
  explosive increases in data volumes, and be
  synergistic with other geophysical modelling
  efforts.

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Afterhttp//www.top500.org
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After http//www.top500.org
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FUTURE TRENDS

• There will always be a role for operational
  meteorology - and a need for operational high
  performance computing.
• Operational meteorology will also be a component
  of a more integrated whole.
• There will need to be significantly greater
  collaboration across the boundary between
  meteorology and the other geophysical and
  biological scientists performing earth-system
  simulation. This interaction will grow in time.

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• The operational meteorologists (short) wish list
• Keep the hardware improving according to Moores
  law
• Maintain a software environment that protects our
  existing investment in model code
• Provide the capability to manage and visualise
  the increasingly large datasets that models and
  remote sensing are providing.

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• THANK YOU