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MASA-OpenCL: Parallel pruned comparison of long DNA sequences with OpenCL

机译:Masa-OpenCL:使用OpenCL的Long DNA序列的并行修剪比较

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Biological sequence comparison is often used as an auxiliary task in the analysis of genetic material. Pairwise comparison algorithms like Smith-Waterman evaluate two strings representing sequences of proteins, DNA or RNA to obtain optimal alignment between them. Many applications have been proposed to address the sequence comparison problem, prioritizing the use of graphics cards and proprietary languages such as CUDA. In this paper, we propose and evaluateMASA- OpenCL, an OpenCL solution for comparing long DNA sequences that is based on the MASA sequence alignment framework, with pruning capability proportional to the similarity of the sequences compared. The results of MASA-OpenCL were compared to its CUDA counterpart (MASA-CUDAlign) and, in most cases,MASA-OpenCLachieved better performance. In order to better understand the behavior of MASA-OpenCL, we performed a statistical analysis considering 11 comparisons of sequences with high, medium and low similarity in 4 GPUs. As a result, we obtained a multiple linear regression model that considers (a) the sizes of the sequences, (b) the similarity between them, (c) the computational power of the GPU, and (d) the GPU memory bandwidth. We used this model to predict the performance in two other GPUs, with low error rates.
机译:生物序列比较通常用作遗传物质分析中的辅助任务。像史密斯 - 水工等成对比较算法评估代表蛋白质,DNA或RNA序列的两个字符串,以获得它们之间的最佳对准。已经提出了许多应用来解决序列比较问题,优先考虑使用图形卡和专有语言,如CUDA。在本文中,我们提出和评估了用于比较基于MASA序列对准框架的长DNA序列的OpenCOSA- OpenCL,其与序列的相似性成比例的修剪能力。将Masa-OpenCL的结果与其CUDA对应物(Masa-Cudalign)进行比较,并且在大多数情况下,Masa-Openclachive更好的性能。为了更好地了解MASA-OPENCL的行为,我们考虑了11个序列的统计分析,在4个GPU中的高,中等和低相似性的序列比较。结果,我们获得了一种多元线性回归模型,其认为(a)序列的大小,(b)它们之间的相似性,(c)GPU的计算能力,以及(d)GPU存储器带宽。我们使用此模型来预测另外两个GPU的性能,具有较低的错误率。

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