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Scalable multicore architectures for long DNA sequence comparison
Author(s) -
Sánchez Friman,
Cabarcas Felipe,
Ramirez Alex,
Valero Mateo
Publication year - 2011
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.1753
Subject(s) - computer science , scalability , parallel computing , multi core processor , implementation , memory bandwidth , sequence (biology) , computer architecture , architecture , programming language , biology , genetics , art , database , visual arts
SUMMARY Biological sequence comparison is one of the most important tasks in Bioinformatics. Owing to the fast growth of databases that contain biological information, sequence comparison represents an important challenge for high‐performance computing, especially when very long sequences are compared, i.e. the complete genome of several organisms. The Smith–Waterman (SW) algorithm is an exact method based on dynamic programming to quantify local similarity between sequences. The inherent large parallelism of the algorithm makes it ideal for architectures supporting multiple dimensions of parallelism (TLP, DLP and ILP). Concurrently, there is a paradigm shift towards chip multiprocessors in computer architecture, which offer a huge amount of potential performance that can only be exploited efficiently if applications are effectively mapped and parallelized. In this work, we analyze how large‐scale biology sequence comparison takes advantage of the current and future multicore architectures. Our starting point is the performance analysis of the current multicore IBM Cell B.E. processor; we analyze two different SW implementations on the Cell B.E. Then, using simulation tools, we study the performance scalability when a many‐core architecture is used for performing long DNA sequence comparison. We investigate the efficient memory organization that delivers the maximum bandwidth with the minimum cost. Our results show that a heterogeneous architecture can be an efficient alternative to execute challenging bioinformatic workloads. Copyright © 2011 John Wiley & Sons, Ltd.