Hardware-aware block size tailoring on adaptive spacetree grids for shallow water waves.

Spacetrees are a popular formalism to describe dynamically adaptive Cartesian grids. Though they directly yield an adaptive spatial discretisation, i.e. a mesh, it is often more efficient to augment them by regular Cartesian blocks embedded into the spacetree leaves. This facilitates stencil kernels working efficiently on homogeneous data chunks. The choice of a proper block size, however, is delicate. While large block sizes foster simple loop parallelism, vectorisation, and lead to branch-free compute kernels, they bring along disadvantages. Large blocks restrict the granularity of adaptivity and hence increase the memory footprint and lower the numerical-accuracy-per-byte efficiency. Large block sizes also reduce the block-level concurrency that can be used for dynamic load balancing. In the present paper, we therefore propose a spacetree-block coupling that can dynamically tailor the block size to the compute characteristics. For that purpose, we allow different block sizes per spacetree node. Groups of blocks of the same size are identied automatically throughout the simulation iterations, and a predictor function triggers the replacement of these blocks by one huge, regularly rened block. This predictor can pick up hardware characteristics while the dynamic adaptivity of the fine grid mesh is not constrained. We study such characteristics with a state-of-the-art shallow water solver and examine proper block size choices on AMD Bulldozer and Intel Sandy Bridge processors

Hardware-aware block size tailoring on adaptive spacetree grids for shallow water waves

Spacetrees are a popular formalism to describe dynamically adaptive Cartesian grids. Though they directly yield an adaptive spatial discretisation, i.e. a mesh, it is often more efficient to augment them by regular Cartesian blocks embedded into the spacetree leaves. This facilitates stencil kernels working efficiently on homogeneous data chunks. The choice of a proper block size, however, is delicate. While large block sizes foster simple loop parallelism, vectorisation, and lead to branch-free compute kernels, they bring along disadvantages. Large blocks restrict the granularity of adaptivity and hence increase the memory footprint and lower the numerical-accuracy-per-byte efficiency. Large block sizes also reduce the block-level concurrency that can be used for dynamic load balancing. In the present paper, we therefore propose a spacetree-block coupling that can dynamically tailor the block size to the compute characteristics. For that purpose, we allow different block sizes per spacetree node. Groups of blocks of the same size are identied automatically throughout the simulation iterations, and a predictor function triggers the replacement of these blocks by one huge, regularly rened block. This predictor can pick up hardware characteristics while the dynamic adaptivity of the fine grid mesh is not constrained. We study such characteristics with a state-of-the-art shallow water solver and examine proper block size choices on AMD Bulldozer and Intel Sandy Bridge processors

Setup of the 2004 Sumatra-Andaman earthquake for SeisSol, version Shaking Corals

Sample input dataset of the 2004 Sumatra-Andaman earthquake for SeisSol, version Shaking corals. The mesh consists of 3.645.163 elements and is partitioned to run with 20 MPI processes. SeisSol can be obtained from from Github (https://github.com/SeisSol/SeisSol/releases/tag/201703). A description on how to set up SeisSol is available in the artifact description of Uphoff et al. "Extreme scale multi-physics simulations of the tsunamigenic 2004 Sumatra megathrust earthquake", 2017 and in the Github Wiki (https://github.com/SeisSol/SeisSol/wiki)

Setup of the TPV16 benchmark for dynamic rupture verification

Verification benchmark for simulations with dynamic rupture and local time stepping with SeisSol, version Shaking Corals. More details can be found in the publication Uphoff et al. "Extreme scale multi-physics simulations of the tsunamigenic 2004 Sumatra megathrust earthquake", 2017 and on the project homepage (http://www.seissol.org/)