The parallelism motifs of genomic data analysis

Yelick, Katherine; Buluç, Aydın; Awan, Muaaz; Azad, Ariful; Brock, Benjamin; Egan, Rob; Ekanayake, Saliya; Ellis, Marquita; Georganas, Evangelos; Guidi, Giulia; Hofmeyr, Steven; Selvitopi, Oguz; Teodoropol, Cristina; Oliker, Leonid

doi:10.1098/rsta.2019.0394

Title: The parallelism motifs of genomic data analysis

Journal Article · 20 January 2020 · Philosophical Transactions of the Royal Society. A, Mathematical, Physical and Engineering Sciences

DOI: https://doi.org/10.1098/rsta.2019.0394 · OSTI ID:1598527

^[1]; Buluç, Aydın ^[1]; Awan, Muaaz ^[2]; Azad, Ariful ^[3]; Brock, Benjamin ^[1]; Egan, Rob ^[4]; Ekanayake, Saliya ^[2]; Ellis, Marquita ^[1]; Georganas, Evangelos ^[5]; Guidi, Giulia ^[1]; Hofmeyr, Steven ^[2]; Selvitopi, Oguz ^[2]; Teodoropol, Cristina ^[1]; Oliker, Leonid ^[2]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Indiana Univ., Bloomington, IN (United States)
USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States)
Intel Labs, Santa Clara, CA (United States)

Genomic datasets are growing dramatically as the cost of sequencing continues to decline and small sequencing devices become available. Enormous community databases store and share these data with the research community, but some of these genomic data analysis problems require large-scale computational platforms to meet both the memory and computational requirements. These applications differ from scientific simulations that dominate the workload on high-end parallel systems today and place different requirements on programming support, software libraries and parallel architectural design. For example, they involve irregular communication patterns such as asynchronous updates to shared data structures. We consider several problems in high-performance genomics analysis, including alignment, profiling, clustering and assembly for both single genomes and metagenomes. We identify some of the common computational patterns or ‘motifs’ that help inform parallelization strategies and compare our motifs to some of the established lists, arguing that at least two key patterns, sorting and hashing, are missing. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21); National Science Foundation (NSF)

Grant/Contract Number:: AC02-05CH11231; SC0008700; AC05-00OR22725

OSTI ID:: 1598527

Journal Information:: Philosophical Transactions of the Royal Society. A, Mathematical, Physical and Engineering Sciences, Journal Name: Philosophical Transactions of the Royal Society. A, Mathematical, Physical and Engineering Sciences Journal Issue: 2166 Vol. 378; ISSN 1364-503X

Publisher:: The Royal Society PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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