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The proposed AMR data structure simplifies the implementation and parallelization of an AMR code.
In our work, we consider easing the implementation and parallelization of different reconstruction algorithms using a MapReduce-like middleware [6, 12, 13], and scale reconstruction operations to many compute nodes.
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This approach defines a high level of abstraction from the underlying data structures and frees modelers from issues of optimized implementation and parallelization.
They pay little attention to numerical methods such as the finite element method (FEM), their implementation and parallelization.
This requires immense computation power and parallelization of the simulation.
Two other ways are serialization and parallelization of PHCs.
Partition of data can be applied on arrays and parallelization of algorithms is a straightforward consequence.
To enable more transparent usage of hybrid systems, we introduce elastic computing, which is an optimization framework where application designers invoke specialized elastic functions that contain a knowledge-base of implementation alternatives and parallelization strategies.
We present block algorithms and their implementation for the parallelization of sub-cubic Gaussian elimination on shared memory architectures.
Due to efficient implementation and program parallelization, the whole several-hours-long batch can be simulated within a few seconds resulting in a model applicable in MPC as a soft-sensor for the detailed molecular architecture of the produced copolymer.
We describe the design, implementation and performance of the new hybrid parallelization scheme in our Monte Carlo radiative transfer code SKIRT, which has been used extensively for modelling the continuum radiation of dusty astrophysical systems including late-type galaxies and dusty tori.
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