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Optimizing Burrows-Wheeler Transform-Based Sequence Alignment on Multicore Architectures

机译:在多核体系结构上优化基于Burrows-Wheeler变换的序列比对

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Computational biology sequence alignment tools using the Burrows-Wheeler Transform (BWT) are widely used in next-generation sequencing (NGS) analysis. However, despite extensive optimization efforts, the performance of these tools still cannot keep up with the explosive growth of sequencing data. Through an in-depth performance analysis of BWA, a popular BWT-based aligner on multicore architectures, we demonstrate that such tools are limited by memory bandwidth due to their irregular memory access patterns. We then propose a locality-aware implementation of BWA that aims at optimizing its performance by better exploiting the caching mechanisms of modern multicore processors. Experimental results show that our improved BWA implementation can reduce last-level cache (LLC) misses by 30% and translation look aside buffer (TLB) misses by 20%, resulting in up to 2.6-fold speedup over the original BWA implementation.
机译:使用Burrows-Wheeler变换(BWT)的计算生物学序列比对工具已广泛用于下一代测序(NGS)分析中。但是,尽管进行了大量优化工作,但这些工具的性能仍无法跟上测序数据的爆炸式增长。通过对BWA(一种在多核体系结构上流行的基于BWT的对齐器)进行深入的性能分析,我们证明了此类工具由于其不规则的内存访问模式而受到内存带宽的限制。然后,我们提出了一种BWA本地感知实现,旨在通过更好地利用现代多核处理器的缓存机制来优化其性能。实验结果表明,我们改进的BWA实现可以将最后一级缓存(LLC)的丢失减少30%,而转换后备缓存(TLB)的丢失则可以减少20%,与原始BWA实现相比,速度提高了2.6倍。

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