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Using GPU to accelerate the pairwise structural RNA alignment with base pair probabilities

机译：使用GPU加速碱基对概率的成对结构RNA对齐

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摘要

Structural alignments of Ribonucleic acid (RNA) sequences solved by the Sankoff algorithm are computationally expensive and often require constraints to be used in practice. Modern Graphics Processing Units (GPUs) contain more than 1000 cores, which compute in parallel to speed up applications. Here, we present a GPU-based solution to the RNA structural alignment problem that makes use of precalculated base pair probabilities on the individual sequences. We designed and developed an unconstrained version of the Sankoff algorithm, obtaining the optimal result and calculating the entire four-dimension dynamic programming matrix (4D DP). Our approach uses a two-level wavefront strategy to exploit parallelism. The 4D DP matrix is divided in one external matrix (EM) and several internal matrices (IM). We applied wavefront strategies on the EM and IMs in a two-level hierarchical way. At the first level, the wavefront is applied to the EM, calculating the cells that belong to the same diagonal in parallel. In the second level, since each cell in the EM is itself an IM matrix, the cells that belong to the same IM diagonal are calculated in parallel. The results obtained with real RNA sequences show that our GPU version is capable of outperforming a multicore CPU version of the unconstrained version of the Sankoff algorithm. Compared with the CPU-based version running on 32 cores, our approach is able to achieve a speedup of 7.81x on the NVidia Tesla P100. In this case, the execution time was reduced from 6 hours and 18 minutes (32 cores) to 48 minutes and 20 seconds (GPU).
机译：由Sankoff算法求解的核糖核酸（RNA）序列的结构比对在计算上很昂贵，并且在实践中经常需要约束。现代图形处理单元（GPU）包含1000多个内核，可以并行计算以加快应用程序的速度。在这里，我们提出了一种基于GPU的RNA结构比对问题解决方案，该解决方案利用了各个序列上预先计算的碱基对概率。我们设计并开发了Sankoff算法的无约束版本，从而获得了最佳结果并计算了整个四维动态规划矩阵（4D DP）。我们的方法使用两级波前策略来利用并行性。 4D DP矩阵分为一个外部矩阵（EM）和几个内部矩阵（IM）。我们以两级分层的方式在EM和IM上应用了波前策略。在第一层，将波前应用于EM，并行计算属于同一对角线的像元。在第二级中，由于EM中的每个单元本身都是IM矩阵，因此将并行计算属于同一IM对角线的单元。使用真实RNA序列获得的结果表明，我们的GPU版本能够胜过Sankoff算法的无约束版本的多核CPU版本。与运行在32核上的基于CPU的版本相比，我们的方法能够在NVidia Tesla P100上实现7.81倍的加速。在这种情况下，执行时间从6小时18分钟（32个内核）减少到48分钟20秒（GPU）。

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