Optimal multiple sequence alignment by dynamic programming, like many highly dimensional scientific computing problems, has failed to benefit from the improvements in computing performance brought about by multi-processor systems, due to the lack of suitable scheme to manage partitioning and dependencies. A scheme for parallel implementation of the dynamic programming multiple sequence alignment is presented, based on a peer to peer design and a multidimensional array indexing method. This design results in up to 5-fold improvement compared to a previously described master/slave design, and scales favourably with the number of processors used. This study demonstrates an approach for parallelising multi-dimensional dynamic programming and similar algorithms utilizing multi-processor architectures.

翻译：最优多序列比对的动态规划方法，如同许多高维科学计算问题，由于缺乏合适的分区与依赖管理方案，未能从多处理器系统带来的计算性能提升中受益。本文提出了一种基于对等设计（peer-to-peer design）和多维数组索引方法的动态规划多序列比对并行实现方案。与先前描述的主从设计（master/slave design）相比，该设计实现了最高5倍的性能提升，且随处理器数量增加具有良好的可扩展性。本研究展示了一种利用多处理器架构对多维动态规划及类似算法进行并行化的方法。