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This naive approach suffers from low efficiency, large memory usage, difficulties to handle resource updates, and it is not scalable.
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Attending to the largest memory usage configuration considered in [14], 3.5 GB of global memory was used, which was close to the capacity limit of a single GPU.
The larger that number, the more precise the reference function (and hence the STO estimation) will be and the larger the memory usage as well.
Conventional SNPs analysis mechanisms suffer from large run times, inefficient memory usage, and frequent overestimation.
When compressing a large amino acid data set such as NCBI's 'NR', memory usage can grow large.
I will show our results: faster execution of most programs (by a factor between 1.5x and 20x), and smaller total memory usage for large programs.
When the grid size is small (i.e., close to 100 meters), the accuracy of all three methods stays high (i.e., close to 96%%.) Combining the results of grid partition shown in Fig. 7 and Table 2, large grid size indicates low memory usage and low overhead.
The storage of sequences is designed to minimize memory usage for large collections using a 'proxy' storage concept.
The implementation uses the FM-index data structure, which allows queries to be performed over a large text collection while limiting memory usage.
Although Pyicos is not as fast as the combination of BEDTools with edgeR or DESeq, it still can handle very large datasets at much lower memory usage (Supplementary Fig. S16 and Fig. 2c).
20, 21 Due to the amount of information to be processed, relatively large memory and central processing unit usage are required for this approach.
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