<p><span>Dynamic time warping (DTW) is an important metric for measuring similarity for most time series applications. The computations of DTW cost too much especially with the gigantic of sequence databases and lead to an urgent need for accelerating these computations. However, the multi-core cluster systems, which are available now, with their scalability and performance/cost ratio, meet the need for more powerful and efficient performance. This paper proposes a highly efficient parallel vectorized algorithm with high performance for computing DTW, addressed to multi-core clusters using the Intel quad-core Xeon co-processors. It deduces an efficient architecture. Implementations employ the potential of both message passing interface (MPI) and OpenMP libraries. The implementation is based on the OpenMP parallel programming technology and offloads execution mode, where part of the code sub-sequences on the processor side, which are uploaded to the co-processor for the DTW computations. The results of experiments confirm the effectiveness of the algorithm.</span></p>

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A B S T R A C TIn this paper, we introduce an improved parallel algorithm for computing the number of exact matches nid (S,T) in the local alignment of two biological sequences S and T. This number is used in the first stage of progressive alignment to compute the distance between two sequences. The distance computations are usually its most computationally intensive part. Therefore, this work concentrates on improving an algorithm for this stage using vectorizing technique and running on multi-core. Our program is able to compute nid (S,T) between very long sequences, up to 34 k residues by C++ with OpenMP library on an Intel Core-i7-3770 quad-core processor of 3.40 GHz and main memory of 8 GB. It outperforms ClustalW-MPI 0.13 with 2.9-fold speedup, and the efficiency reached 0.35. Furthermore, a higher speedup with improved efficiency can be accomplished. Its performance figures vary from a low of 0.438 GCUPS to a high of 3.66 GCUPS as the lengths of the query sequences decrease from 34,500 to 9200.

In this paper, the authors give a randomized algorithm which follows a Monte-Carlo method. This algorithm is a randomized fully polynomial-time approximation scheme for the given problem. Fortunately, the suggested algorithm is a one tackled the matching problem in both Euclidean nonbipartite and bipartite cases. Keywords-Perfect matching; approximation algorithm; MonteCarlo technique; a randomized fully polynomial-time approximation scheme; and randomized algorithm.