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ESM dwarfs (or mini-apps) are concise blocks of code comprising key algorithms and mathematical kernels, abstracted from their original code base. They offer the possibility of exploring improvements and optimisations in a a focused way, avoiding the complexities of working with a full model system. They naturally encourage to work within the concept of separation of concerns, by facilitating experimenting with low-level libraries, hardware-specific programming models and optimisations, and high-level abstractions to encode them.
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### Exploitation of adaptive grids
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Adaptive mesh refinement (AMR) offers many potential advantages for the scalability of ESM codes. The basic idea is to increase resolution in those regions of the problem which require it, and reduce resolution elsewhere. With AMR it is possible to reduce the number of cells without compromising accuracy. This reduces the computing effort, the memory footprint and the output data volume. There are many open challenges, e.g., dynamic load balancing, efficient memory movements upon re-meshing, and finding optimal refinement criteria.
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### Lagrangian modeling
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