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Now focus on the serial implementation.
Several techniques to optimize the serial implementation of the DSMC method are first discussed.
The model was derived from our CPU serial implementation, named GAME (Genetic Algorithm Model Experiment).
The only reason I modified the proc advance in the past (for serial implementation) was to store the csd vector.
The parallelization is theoretically unbiased as it provides the same result as the serial implementation, without introducing any approximation.
Comparison of our on-GPU assembly versus a traditional serial implementation on the CPU achieves up to an 87× speedup.
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These implementations are found to be straightforward and highly efficient compared to serial implementations.
Experimental results on multiple datasets showed an average speedup of 8.1× compared to serial implementations.
As a first step to insure fast performance, we study several possible serial implementations of the CA. The simulations are performed in three infrastructures, comparing two different microarchitectures.
Cellular Automata (CA) are of interest in several research areas and there are many available serial implementations of CA. However, there are relatively few studies analyzing in detail High Performance Computing (HPC) implementations of CA which allow research on large systems.
Experimental results show that our proposed implementation on a tightly coupled heterogeneous processor outperforms all other alternatives including traditional discrete GPU, quad-core CPU using OpenMP, and serial implementations by 67%, 50%, and 154% respectively even though the embedded GPU in the heterogeneous processor has significantly less number of cores than high-end discrete GPU.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com