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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Semiglobal Sequence Alignment with Gaps Using GPU
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Semiglobal Sequence Alignment with Gaps Using GPU

机译：使用GPU与间隙的半球形序列对齐

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In this paper, we consider the pair-wise semiglobal sequence alignment problem with gaps, which is motivated by the re-sequencing problem that requires to assemble short reads sequences into a genome sequence by referring to a reference sequence. The problem has been studied before for single gap and bounded number of gaps. For single gap, there is a GPU-based algorithm proposed (Barton et al., 2015). In our work, we propose a GPU-based algorithm for the bounded number of gaps case, called GPUGapsMis. We implement the algorithm and compare the performance with the CPU-based algorithm, called CPUGapsMis. The algorithm has two distinct stages: the alignment phase, and the backtrack phase. We investigate several different approaches, in order to determine the most favorable for this problem, by means of a Hybrid model or a wholly-GPU based model, as well as the alignment of single text sequences or multiple text sequences on the GPU at a time. We show that the alignment phase of the algorithm is a good candidate for parallelization, with peak speedup of 11 times. We show that although the backtracking phase is sequential, it is more beneficial to perform it on the GPU, as opposed to returning to the CPU and performing there. When performing both phases on the GPU, GPUGapsMis achieves a peak speedup of 10.4 times against CPUGapsMis. Our data parallel GPU algorithm achieves results which are an improvement on those of an existing GPU data parallel implementation (Ojiaku, 2014).
机译：在本文中，我们考虑通过参考参考序列将简短读取序列组装成基因组序列的重新排序问题的间隙的成对半球序列对准问题。在单个间隙和有界差距的情况下已经研究过问题。对于单个间隙，提出了一种基于GPU的算法（Barton等，2015）。在我们的工作中，我们提出了一种基于GPU的算法，用于界限的差距差距，称为GPugapsmis。我们实现了算法，并将性能与基于CPU的算法进行了比较，称为cpugapsmis。该算法具有两个不同的阶段：对齐阶段和返回阶段。我们调查几种不同的方法，以通过混合模型或基于GPU的型号来确定对该问题最有利的，以及一次在GPU上对齐单个文本序列或多个文本序列的对准。我们表明该算法的对准阶段是一个很好的并行化候选者，峰值加速为11次。我们表明，尽管回溯阶段是顺序的，但在GPU上执行它更有益，而不是返回到CPU并在那里执行。在对GPU上进行两个阶段时，GPUGAPSMIS达到CPUGAPAPSMIS的峰值加速10.4次。我们的数据并行GPU算法达到了对现有GPU数据并行实现（Ojiaku，2014）的结果的改进。

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