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than the parallel frameworks that integrate work-stealing (such as OpenMP).
The proposed load balancing technique was implemented by MPI, but can easily generalize to other shared-nothing parallel frameworks such as MapReduce.
Our parallel solutions are implemented and evaluated on MapReduce, a popular shared-nothing parallel framework, but can easily generalize to other shared-nothing or shared-memory parallel frameworks.
Lastly, the single- and multi-GPU codes are further compared with existing state-of-the-art SPH parallel frameworks for a comprehensive assessment.
In this exploratory study, we consider two different parallel frameworks and present a thorough computational study in order to understand algorithmic decisions that are closely related to performance.
A detailed discussion is provided in our earlier work [8, 9] on choosing Spark for parallel virtual screening in comparison to other parallel frameworks, such as OpenMPI, MPI and Google MapReduce.
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The parallel framework increases exploitation search of genetic algorithm.
Fig. 2 The proposed parallel framework for GA-TPC algorithm.
Hence, the proposed parallel framework is effective on antenna design.
A software prototype modeling such a parallel framework is built.
In this section, the proposed parallel framework is studied and compared with GA without parallel.
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