论文标题
算法的改进和GPU基因算法的加速度
Algorithmic Improvement and GPU Acceleration of the GenASM Algorithm
论文作者
论文摘要
我们通过显着降低其记忆足迹和带宽需求来改进基因组序列比对的最新算法。我们的算法改进将内存足迹减少了24 $ \ times $，而内存访问的数量则减少了12 $ \ times $。我们有效地使GPU的算法并行化，在同一算法的CPU实现上，达到了4.1 $ \ times $速度，在MiniMap2的基于CPU的KSW2上的62 $ \ times $ speedup以及基于CPU的EDLIB的7.2 $ \ times $ speedub for长期阅读。
We improve on GenASM, a recent algorithm for genomic sequence alignment, by significantly reducing its memory footprint and bandwidth requirement. Our algorithmic improvements reduce the memory footprint by 24$\times$ and the number of memory accesses by 12$\times$. We efficiently parallelize the algorithm for GPUs, achieving a 4.1$\times$ speedup over a CPU implementation of the same algorithm, a 62$\times$ speedup over minimap2's CPU-based KSW2 and a 7.2$\times$ speedup over the CPU-based Edlib for long reads.
