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It is written in Fortran90 and optimized for a fast execution on a broad range of computer architectures, spanning from single processor PCs over PC-clusters and vector computers to high-end parallel machines.
It relies on large spaced seeds for a fast execution, and on many different indexes for sensitivity.
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However, to allow a fast execution, this function can be approximated using a LUT approach.
We chose this simple approach for a number of reasons, but primarily because we performed many analyses on replicated datasets and therefore needed a fast execution time.
Subsequently, we found that Apache Spark provided a faster execution engine for distributed processing and claimed that it is much faster than Hadoop MapReduce as it exploits the advantages of in-memory computations which is particularly more beneficial for iterative computations in the case of iterative algorithms.
Moreover, the running times for EXCELLENT show that increasing q may not lead to a faster execution of EXCELLENT, since the fastest execution was obtained for q = 12.
It may result in a faster execution time compared to the synchronous case.
As with the genomes, 2D had a faster execution time than gzip, while gzip had a better compression ratio.
This approach reduces data dimensionality to one dimension, which allows for a significantly faster execution of an approximate nearest neighbor search, based on the indexed elements.
This restriction allowed a very fast execution time enabling the investigation of a wide range of scenarios.
The main contributions of this paper are summarized as follows: We designed the global random sampling for fast execution of window functions with ROW mode and RANGE mode: Range-based global sampling algorithm (RGSA) and row-labeled sampling algorithm (RLSA).
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