Parallelization of DNA alignment algorithms using GPUs Dissertac¸ ˜ao para obtenc¸ ˜ao do Grau ...

94 Pages·2012·1.08 MB·English

Parallelization of DNA alignment algorithms using GPUs
Gustavo Pa´ramos Merino Faria Encarnac¸a˜o
Dissertac¸a˜o para obtenc¸a˜o do Grau de Mestre em
Engenharia Informa´tica e de Computadores
Ju´ri
Presidente: Doutor Joaquim Armando Pires Jorge
Orientador: Doutor Nuno Filipe Valentim Roma
Vogais: Doutor Luis Manuel Silveira Russo
Maio de 2012

Acknowledgments
Iwouldliketo thank all myfriendsand familywho, everydayin these pasttwoyears, through
peer-pressureandconstantlyquestioningmewhenIwouldfinallydelivermythesisandfinishmy
degree,gavememotivationtocontinueandtonotgiveup.
I would also like to thank Professor Nuno Roma and Nuno Sebastia˜o for their patience, sup-
port,adviceandideaswithoutwhichthisthesiswouldnothavehappened.
This thesis was performed in the scope of project “HELIX: Heterogeneous Multi-Core Archi-
tectureforBiologicalSequenceAnalysis”,fundedbythePortugueseFoundationforScienceand
Technology (FCT) with reference PTDC/EEA-ELC/113999/2009, and project “TAGS: The power
oftheshort-ToolsandAlgorithmsfornextGenerationSequencingapplications”,fundedbyFCT
withreferencePTDC/EIA-EIA/112283/2009.

Abstract
Since its discovery, the Deoxyribonucleic Acid (DNA) has been the object of thorough study.
Thesignificantadvancesinsequencingtechnologies[1]haveallowedresearcherstoobtainDNA
sequencesateverincreasingrates,withtheconsequentgrowthofthecorrespondingdatabases.
Such huge amounts of data require efficient processing tools to extract useful information. How-
ever,thesetoolshaveadvancedataslowerpaceandhavebecomeoneofthelimitingfactorsof
newdiscoveriesinthisfieldofresearch.
Recently, from the 3D game market, a new generation of hardware has emerged. This hard-
ware, known as Graphics Processing Units (GPUs), now offers the capability to perform general
purpose computations. With these new hardware devices, high performance computing has be-
come possible using cheap and readily available hardware which creates new opportunities to
studyandimprovecurrenttoolsandalgorithmsusedforthestudyofDNA.
The goal of this dissertation was to discover a way of using these new processing platforms
to improve the current tools, techniques and algorithms used for DNA analysis. To attain this
goal, several data structures and algorithms were considered and thoroughly analysed in terms
ofperformanceandflexibilitywhenexecutedonaGPU. Afterwards,thesestructureswereusedin
theimplementationofbothexactandapproximatestringmatchingalgorithmsespeciallyadapted
forDNAsequences.
The outcome of the performed work was the development of a new tool for heuristic DNA
alignmentthatiscapableofharnessingtherawcomputationalperformanceprovidedbythehighly
parallelGPUdevice.
Conductedtestsshowedthattheperformedworkledtothecreationofatoolthatiscapableof
competingwithcurrentlyavailableandcommonlyusedsoftwareinbothperformanceandquality
ofresults.
Keywords
ExactStringMatching,ApproximateStringMatching,DNAAlignment,High-PerformanceCom-
puting,GraphicsProcessingUnit(GPU)
iii

Resumo
Desdeasuadescoberta,oDNAtemsidoobjectotheestudointensivo. Osavanc¸ossignifica-
tivosemtecnologiasdesequenciac¸a˜o[1]teˆmpermitidoobtersequeˆnciasdeDNAaritmoscada
vez maiores, com o consequente crescimento das bases de dados correspondentes. Tamanha
quantidade de dados requer ferramentas de processamento eficientes para extrair informac¸a˜o
u´til. No entanto, estas ferramentasteˆm avanc¸ado a um ritmo mais lento e tornaram-senum dos
factoreslimitativosdenovasdescobertasnestecampodepesquisa.
Recentemente, impulsionada pelo mercado do entretenimento e dos jogos 3D, surgiu uma
novagerac¸a˜odeprocessadores. Estesprocessadores,conhecidoscomoprocessadoresgra´ficos
(GPU), sa˜o capazes de executar na˜o so´ as transformac¸o˜es geome´tricas necessa´rias em jogos
mas tambe´m programas gene´ricos de alguma complexidade. Com estes novos processadores,
computac¸a˜o de alto desempenho tornou-se possivel de realizar em plataformas baratas e de
acesso fa´cil o que cria novas oportunidades para o estudo e melhoramento das ferramentas e
te´cnicasparaoestudodoADNactuais.
Oobjectivodestadissertac¸a˜ofoiodedescobrirumaformadeaproveitarestasnovasplatafor-
mas para melhorar as ferramentas para estudo do ADN actuais. Para atingir este objectivo di-
versas estruturas de representac¸a˜o de dados e algoritmos foram estudadas e cuidadosamente
analisadasemtermosdesempenhoeflexibilidadequandoexecutadosnoGPU.Posteriormente,
estasestruturasforamutilizadas,naimplementac¸a˜odealgoritmosparaprocuraexactaeaproxi-
madadecadeiasdecaracteres,especialmenteadaptadosparasequeˆnciasdeDNA.
O resultado do trabalho realizado foi o desenvolvimento de uma nova ferramenta heuristica
paraalinhamentodeDNA,capazdeaproveitaropodercomputacionalfornecidopelosGPUs.
Ostestesrealizadosdemonstramqueotrabalhorealizadolevou criac¸a˜odeumaferramenta
capaz de competir com as ferramentas actuais, tanto em termos da qualidade dos resultados
comonavelocidadeaqueestesresultadossa˜oobtidos.
Palavras Chave
Procura Exacta, Procura Aproximada, Alinhamento de DNA, Computac¸a˜o de Alto Desem-
penho,UnidadedeProcessamentoGra´fico
v

Contents
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Maincontributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Dissertationoutline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 GeneralPurposeComputationonGraphicsProcessingUnits 5
2.1 GPGPUhardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.1 NVidiaG80andGF100architectures . . . . . . . . . . . . . . . . . . . . . 6
2.1.2 AMDFireStreamarchitecture . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 GPGPUperformanceissues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2.1 WarpdivergenceandSIMTparadigm . . . . . . . . . . . . . . . . . . . . . 8
2.2.2 Memoryaccess . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 GPGPUprogramminglanguages . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3.1 NVidiaCUDA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.2 ATIFireStreamSDK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.3 OpenCL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.4 MicrosoftDirectCompute . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3 DNAAlignmentMethods 15
3.1 Localandglobalalignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.2 DynamicprogrammingDNAalignment . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.2.1 DNAanalysisusingstrings . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.2.2 Needleman-Wunsch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.2.3 Smith-Waterman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.3 Indexedsearch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.3.1 Suffixtriesandsuffixtrees . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.3.2 Suffixarrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.3.3 Hashtablesandq-mers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.3.4 Burrows-Wheelertransform . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.4 SummaryofDNAAnalysistools . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4 ExactMatching 27
4.1 Datarepresentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.1.1 Referencesequence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.1.2 Querysequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
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Contents
4.2 GPUInitialization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.2.1 Memoryallocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.2.2 Hosttodevicedatacopy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.2.3 Kernellaunch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.2.4 Memoryde-allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.2.5 TimemeasurementandGPUevents . . . . . . . . . . . . . . . . . . . . . . 36
4.3 ExactmatchingontheGPU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.3.1 SuffixTreeKernel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.3.2 SuffixArrayKernel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.4 Comparisonanddiscussionofdifferentapproaches . . . . . . . . . . . . . . . . . 41
4.4.1 Overlapofcommunicationandcomputation . . . . . . . . . . . . . . . . . . 44
5 ApproximateMatching 47
5.1 Possibleapproachesforapproximatematching . . . . . . . . . . . . . . . . . . . . 48
5.1.1 Suffixarrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5.1.2 Enhancedsuffixarray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
5.1.3 Suffixtrees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.2 DiscussionandComparisonoftheconsideredapproaches . . . . . . . . . . . . . 61
6 DNAalignmentusingGPUs 63
6.1 Seeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
6.1.1 Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
6.1.2 SeedingByCounting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
6.1.3 SeedingBySorting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
6.2 Smith-Waterman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
6.2.1 Kernelmodifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
6.2.2 GPGPUKernel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
6.3 Resultsandcomparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6.3.1 Parameterinfluence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6.3.2 SeedingbycountingversusSeedingbysorting . . . . . . . . . . . . . . . . 72
6.3.3 ComparisonwithBLAST . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
6.3.4 ComparisonwithMUMmer . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
7 Conclusions 75
7.1 Futurework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
viii