Trelles O
Computer Architecture Department, University of Malaga, Spain.
Brief Bioinform. 2001 May;2(2):181-94. doi: 10.1093/bib/2.2.181.
This paper surveys the computational strategies followed to parallelise the most used software in the bioinformatics arena. The studied algorithms are computationally expensive and their computational patterns range from regular, such as database-searching applications, to very irregularly structured patterns (phylogenetic trees). Fine- and coarse-grained parallel strategies are discussed for these very diverse sets of applications. This overview outlines computational issues related to parallelism, physical machine models, parallel programming approaches and scheduling strategies for a broad range of computer architectures. In particular, it deals with shared, distributed and shared/distributed memory architectures.
本文综述了为使生物信息学领域最常用的软件实现并行化而采用的计算策略。所研究的算法计算成本高昂，其计算模式从规则的（如数据库搜索应用）到结构非常不规则的模式（系统发育树）不等。针对这些非常多样的应用集，讨论了细粒度和粗粒度并行策略。本综述概述了与并行性、物理机器模型、并行编程方法以及适用于广泛计算机架构的调度策略相关的计算问题。特别是，它涉及共享内存、分布式内存以及共享/分布式内存架构。
