Biology Reference
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sparse matrices, the memory footprint is still large for large-scale
datasets. Hence, we recommend the use of a computer with
a large amount of shared memory to compute MSA of large-
scale data. In the future, we plan to design an out-of-core
pairwise probability matrix computation by storing all matrices
on disk.
3. In our program, the profile-profile progressive alignment stage
has not yet been parallelized. Hence, for large-scale data,
this stage might become a parallel scalability bottleneck when
using multiple threads. In MSA-CUDA [
15
], a dynamic
scheduling parallelization has been proposed to parallelize the
profile-profile progressive alignment stage of ClustalW [
16
]on
graphics processing units. This method is also suitable for
parallelization based on multi-threading on CPUs. Hence,
our future work also includes the parallelization of this stage.
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