Ocean modelling and high performance computing
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Ocean Modelling and High Performance Computing. Andrew Coward. Cluster Computing Summer School 2009. Ocean Modelling and High Performance Computing. Introduction and rationale Historical perspective The NEMO model Example results. Introduction and rationale.

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Ocean modelling and high performance computing

Ocean Modelling and High Performance Computing

Andrew Coward

Cluster Computing Summer School 2009


Ocean modelling and high performance computing1

Ocean Modelling and High Performance Computing

  • Introduction and rationale

  • Historical perspective

  • The NEMO model

  • Example results


Ocean modelling and high performance computing

Introduction and rationale

Evidence of change: In-situ observations

Temperature change at 1.5-2.5km off Bermuda

Atlantic temperature change (oC) at 24N (1957-1992)

  • main warming is at mid-depth - unobservable from space

  • the best observed basin-wide, full depth hydrographic section

    …… 3 times in 35 yr!


Ocean modelling and high performance computing

Introduction and rationale

Ocean models are needed to “fill-in the gaps” and provide a predictive capability

Recipe for an ocean model:

Derive mathematical equations describing the ocean’s evolution from an initial state subject to surface forcing.

Discretize equations on a spatial grid (3-dimensional).

Obtain initial state from observations.

Obtain time varying surface forcing from observations or Numerical Weather Prediction program.

Integrate equations forward in time from initial state.

Test and develop model in hindcast mode.


Ocean modelling and high performance computing

Introduction and rationale: the need for resolution in ocean models

1. Complex domains

Greenland

Iceland

Scotland

Greenland-Scotland Topography


Ocean modelling and high performance computing

Ocean eddy (IR)

Introduction and rationale: the need for resolution in ocean models

2. Small scales of motion

1000 km

Atmospheric depression (IR)

  • note difference in horizontal scales

Observations from space


Ocean modelling and high performance computing

1/12o

1/4o

1o

1o

1/4o

1/12o

Satellite observed sea surface temperature

Simulated sea surface temperature


Evolution

Evolution

Cray T3D/E

}

37M gridcells

1/4o x 1/4o Global Ocean

Model (mid nineties)

Origin 3800

}

1/12o x 1/12o Global Ocean

model

608M gridcells

IBM Regatta

?

Cray XMP/YMP autotasking parallelism

8 processors

memory slab window with SSD

asynchronous "putwa's and getwa's"

The other changing environment:

Cray X/YMP

5M gridcells

1/2o x 1/4o Southern Ocean

Model (circa 1990)

Typical performance: 20 model days in 12 hours using 512 HPCx processors

Storage requirement ~ 1TB per model year

Historical perspective


Ocean modelling and high performance computing

Recipe for a High Performance ocean model:.

Discretize equations on a spatial grid (3-dimensional).

Decompose grid into multiple overlapping tiles

Introduce a message-passing harness to exchange information between tiles.

Obtain time varying surface forcing from observations or Numerical Weather Prediction program.

Integrate equations forward in time from initial state.

Test and develop model in hindcast mode.

A separate processor computes values in each differently coloured patch


Ocean modelling and high performance computing

NEMO example 16x16 domain decomposition requiring 221 processors


Agulhas sea surface temperature

Agulhas Sea Surface Temperature

Sea Surface Temperature

Range: 11 oC to 25 oC


Ocean modelling and high performance computing

The role of eddies in global heat transport


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