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The memory requirement shows an estimated trend lower than power 1.35 of the number of surface elements.
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The resulting algorithm has low memory requirements, shows no surface effects even for extremely long walks and is well suited for modern computer architectures.
For similar reasons, the memory requirements shown in Supplementary Table S3e grow with increasing numbers of overhanging residues.
Figure 7 shows the memory requirement affected by the page size.
The results show that memory requirement for proposed algorithm is reduced from 3 TB to 300 GB of RAM.
The first view on the left shows the total memory requirement.
Finally, the right bottom corner shows a summary including the peak memory requirement and total number of ''jitter".
Finally, the FMM is shown to reduce CPU time and memory requirement thus enabling us apply BEM to solve for large-scale problems.
Using published RRBS data, BS-SNPer showed higher specificity and sensitivity with lower memory requirement and was over 100 times faster than Bis-SNP.
The total memory requirement of the CTE is N c M L-1 + 4N c + L. Figure 9 shows the memory requirement of the HNN-TE and the CTE in bytes (32 bits = 8 bytes) for coded data block sizes of N c = 160, N c = 640, and N c = 2560 and CIR lengths increasing from L = 1 to L = 25.
In this paper, we show how this feature can be performed instead with a much smaller memory requirement.
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