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Ocean Modelling and High Performance Computing

This presentation provides an overview of ocean modeling and high-performance computing, focusing on the NEMO model. It discusses the historical perspective, including evidence of temperature changes and the role of eddies in global heat transport. Example results are presented, demonstrating the model's capabilities in filling observational gaps. The talk emphasizes the need for resolution in ocean models to capture complex domains and small-scale motions accurately. High-performance computing techniques for ocean modeling, including grid discretization and message passing, are highlighted using case studies.

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Ocean Modelling and High Performance Computing

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  1. Ocean Modelling and High Performance Computing Andrew Coward Cluster Computing Summer School 2009

  2. Ocean Modelling and High Performance Computing • Introduction and rationale • Historical perspective • The NEMO model • Example results

  3. 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!

  4. 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.

  5. Introduction and rationale: the need for resolution in ocean models 1. Complex domains Greenland Iceland Scotland Greenland-Scotland Topography

  6. 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

  7. 1/12o 1/4o 1o 1o 1/4o 1/12o Satellite observed sea surface temperature Simulated sea surface temperature

  8. 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

  9. 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

  10. NEMO example 16x16 domain decomposition requiring 221 processors

  11. Agulhas Sea Surface Temperature Sea Surface Temperature Range: 11 oC to 25 oC

  12. The role of eddies in global heat transport

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