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); National Science Foundation (NSF)

Grant/Contract Number:: AC02-05CH11231; SC0008700; 1823034; AC05-00OR22725

OSTI ID:: 1598527

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

Publisher:: The Royal Society PublishingCopyright Statement

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 11 works

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Parallel distributed memory construction of suffix and longest common prefix arrays Flick, Patrick; Aluru, Srinivas Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis on - SC '15 https://doi.org/10.1145/2807591.2807609	conference	January 2015
Apache Spark: a unified engine for big data processing Zaharia, Matei; Franklin, Michael J.; Ghodsi, Ali Communications of the ACM, Vol. 59, Issue 11 https://doi.org/10.1145/2934664	journal	October 2016
The UPC++ PGAS library for Exascale Computing Bachan, John; Bonachea, Dan; Hargrove, Paul H. Proceedings of the Second Annual PGAS Applications Workshop on ZZZ - PAW17 https://doi.org/10.1145/3144779.3169108	conference	January 2017
MerBench: PGAS Benchmarks for High Performance Genome Assembly Georganas, Evangelos; Ellis, Marquita; Egan, Rob Proceedings of the Second Annual PGAS Applications Workshop on ZZZ - PAW17 https://doi.org/10.1145/3144779.3169109	conference	January 2017
An overview of the sparse basic linear algebra subprograms: The new standard from the BLAS technical forum Duff, Iain S.; Heroux, Michael A.; Pozo, Roldan ACM Transactions on Mathematical Software, Vol. 28, Issue 2 https://doi.org/10.1145/567806.567810	journal	June 2002
Algorithm 679; a set of level 3 basic linear algebra subprograms: model implementation and test programs Dongarra, J. J.; Cruz, Jermey Du; Hammerling, Sven ACM Transactions on Mathematical Software, Vol. 16, Issue 1 https://doi.org/10.1145/77626.77627	journal	March 1990
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Communication-Avoiding and Memory-Constrained Sparse Matrix-Matrix Multiplication at Extreme Scale Hussain, Md Taufique; Selvitopi, Oguz; Buluç, Aydin arXiv https://doi.org/10.48550/arxiv.2010.08526	preprint	January 2020
Additional file 1: Figure S1. of Mash: fast genome and metagenome distance estimation using MinHash Ondov, Brian; Treangen, Todd; Melsted, Páll Figshare https://doi.org/10.6084/m9.figshare.c.3635585_d1	text	January 2016

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Communication-Avoiding and Memory-Constrained Sparse Matrix-Matrix Multiplication at Extreme Scale Hussain, Md Taufique; Selvitopi, Oguz; Buluç, Aydin arXiv https://doi.org/10.48550/arxiv.2010.08526	preprint	January 2020

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