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HemeLB weighted decomposition EASC 2014 slides

Obtaining a good load balance is a significant challenge in scaling up lattice-Boltzmann simulations of realistic sparse problems to the exascale. Here we analyze the effect of weighted decomposition on the performance of the HemeLB lattice-Boltzmann simulation environment, when applied to sparse domains. Prior to domain decomposition, we assign wall and in/outlet sites with increased weights which reflect their increased computational cost. We combine our weighted decomposition with a second optimization, which is to sort the lattice sites according to a space filling curve. We tested these strategies on a sparse bifurcation and very sparse aneurysm geometry, and find that using weights reduces calculation load imbalance by up to 85%, although the overall communication overhead is higher than some of our runs.

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Weighted Decomposition
Are some lattice sites more
equal than others?
Derek Groen
David Abou Chacra
Jiri Jaros
Rupert Nash
Miguel Bernabeu
Peter Coveney
Overview
● Cerebrovascular bloodflow and HemeLB.
● What type performance matters.
● Weighted decomposition
● Tests & results
● Discussion / Future work
Cerebrovascular diseases
● Stroke is the main cause of about 1.1M deaths per year
in Europe.
● ~15% are caused by bleeding in the brain.
● We aim to accurately model cerebral bloodflow, and to
eventually provide assistance with cerebrovascular
surgery.
Features in HemeLB
1. Generation of computational models from medical
images.
2. A wide range of collision kernels and boundary
conditions.
3. Sparse geometries with negligible overhead (using
ParMETIS).
4. Streaming visualization and steering of the simulation.
5. Coupling to external models.
HemeLB weighted decomposition EASC 2014 slides
HemeLB weighted decomposition EASC 2014 slides

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HemeLB weighted decomposition EASC 2014 slides