高效计算路线图
Computing alignment plots efficiently
摘要：
点图是生物序列局部比较的标准方法。在点图中，计算输入字符串中所有固定大小窗口对的子串到子串的距离。通常，使用汉明距离，因为它可以在线性时间内计算。然而，汉明距离是字符串相似性的一个相当粗略的度量，使用基于汉明距离的编辑距离可以大大提高点图方法的灵敏度。在本文中，我们将展示如何有效地计算后一种类型的对齐图。给定两个长度为m和n的字符串以及窗口大小w，这个问题包括计算所有长度为w的子串对之间的编辑距离，每个输入字符串一个。该问题可以通过在时间O（mnw^2）内重复应用标准动态规划算法来解决。本文提出了一种改进的数据并行算法，该算法在时间上运行$O(mnw/γ/p)$，使用向量运算对$γ$值进行并行运算，并使用$p$处理器。我们展示了实验结果，从实现该算法，它使用英特尔的MMX/SSE指令的向量并行和MPI的粗粒度并行。
英文摘要：
Dot plots are a standard method for local comparison of biological sequences. In a dot plot, a substring to substring distance is computed for all pairs of fixed-size windows in the input strings. Commonly, the Hamming distance is used since it can be computed in linear time. However, the Hamming distance is a rather crude measure of string similarity, and using an alignment-based edit distance can greatly improve the sensitivity of the dot plot method. In this paper, we show how to compute alignment plots of the latter type efficiently. Given two strings of length m and n and a window size w, this problem consists in computing the edit distance between all pairs of substrings of length w, one from each input string. The problem can be solved by repeated application of the standard dynamic programming algorithm in time O(mnw^2). This paper gives an improved data-parallel algorithm, running in time $O(mnw/γ/p)$ using vector operations that work on $γ$ values in parallel and $p$ processors. We show experimental results from an implementation of this algorithm, which uses Intel's MMX/SSE instructions for vector parallelism and MPI for coarse-grained parallelism.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
