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The goal of this PhD thesis is to evaluate if a solution based on C++ template metaprogramming
can offer high-level (zero-cost) abstractions handling a large range of data discretization at compile time. The work will take place in the framework of the ddc library and will be evaluated on the very demanding simulation code GYSELA that leverage the largest existing super computers and manipulates multiple complex discretizations of its high-dimension data along execution. The approach will have to handle seamless replacement of discretization in code (e.g. structured uniform mesh to unstructured), while offering the best performance from each one. The approach will also have to handle parallelism at all levels: distributed-memory parallelism similarly to PGAS languages, shared-memory parallelism on both CPU and GPU, but also SIMD parallelism.