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A full tensor computation requires three computations, each equivalent in time and memory consumption to one experiment simulation.
Our evaluations indicate that MaFIB can minimize the on-chip memory consumption to 4.194 MB and can accelerate the lookup process in the memory.
The above results show that our MaFIB based on MBF can extremely reduce the on-chip memory consumption to 4.194 MB for 2 million names with the probability of false positive under 1%.
It also reduces memory consumption to a linear scaling.
We rank the assemblers based on their memory consumption to decide the candidate assemblers.
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This approach can further reduce the memory consumption compared to the COO format, and it is suitable for parallelizing matrix vector multiplication.
Memory consumption seems to increase in a linear fashion with the number of cloud offerings, since their descriptions are cached and in this test require roughly the same amount of memory (Fig. 7b).
For situations where the memory consumption due to given estimate for (N_{max}) becomes prohibitive, one could start with a lower number and for example double the size of the allocation on-the-fly whenever all available space for ghost peaks is occupied.
Consistency-based methods have increased CPU and memory consumption proportional to the number of sequences being processed.
Therefore in order to reduce memory consumption, we chose to use 40 nucleotides in the PDEGEM.
Experiments on realistic and synthetic IP forwarding tables show that the trie merging scheme reduces the number of nodes by up to 109.2 times as well as the memory consumption by up to 11.8 times compared to previous schemes.
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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