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In the majority of the cases, DIAMoND outperformed the parallel algorithm by a wide margin.
The differences between the two algorithms are not significant, even though DIAMoND outperforms the parallel algorithm in precision.
When we move the parallel threshold to the lower value, s iL, then DIAMoND outperforms the parallel algorithm again (Fig. 7).
Yellow colors in these plots indicate that DIAMoND outperforms the parallel version (and red that it significantly outperforms it), while the parallel version performs better when the color is blue.
It can be observed that when the data size is small, the standalone BPNN outperforms the parallel BPNN due to the overhead of Hadoop framework.
In terms of accuracy, we here find that the DIAMoND algorithm significantly outperforms the parallel detection, in some cases as much as 20%.
When queries are done on a database with several million compounds the indexed approach can outperform the parallel approach easily if the index can be used to quickly narrow down a small set of candidates.
Meanwhile, the proposed method outperforms the traditional parallel structure on both time consumption and recognition rate.
Concerning the BMOPSO-CDRHS algorithm, it is possible to observe from Table 2 that for almost all metrics, the sequential strategy is outperformed by the parallel strategy.
Simply because they are outperforming the locals.
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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