부산대학교 도서관

Clustering is used to partition genomic data into disjoint subsets to streamline further processing. Since inputs can contain billions of nucleotides, performance is paramount. Consequently, clustering software is typically developed as a tightly coupled monolithic system which hinders software reusability, extensibility and introduction of new algorithms as well as data structures. Having experienced similar issues in our own clustering software, we have developed a flexible and extensible parallel framework called PEACE. The objective of the framework is to ease design, implementation, and use of various clustering methods without compromising performance. This paper presents the PEACE framework, its software architecture, parallel infrastructure, and distributed data structures along with a case study of developing a clustering algorithm. Case studies of developing filters, heuristics, and comparison algorithms are also discussed to illustrate modularity and extensibility of PEACE which enables software reuse in unique ways that may not have been foreseen when individual components were developed.