dynamic program analysis algorithms to assist - SMARTech

235 Pages·2012·11.43 MB·English

DYNAMIC PROGRAM ANALYSIS ALGORITHMS TO ASSIST
PARALLELIZATION
ADissertation
Presentedto
TheAcademicFaculty
by
MinjangKim
InPartialFulfillment
oftheRequirementsfortheDegree
DoctorofPhilosophyin
ComputerScience
SchoolofComputerScience
GeorgiaInstituteofTechnology
December2012
Copyright©2012byMinjangKim
DYNAMIC PROGRAM ANALYSIS ALGORITHMS TO ASSIST
PARALLELIZATION
Approvedby:
Dr. HyesoonKim,Advisor Dr. SantoshPande
SchoolofComputerScience SchoolofComputerScience
GeorgiaInstituteofTechnology GeorgiaInstituteofTechnology
Dr. Chi-KeungLuk Dr. RichardVuduc
TechnologyPathfindingand SchoolofComputationalScienceand
Innovations Engineering
IntelCorporation GeorgiaInstituteofTechnology
Dr. Hsien-HsinS.Lee DateApproved: August16th,2012
SchoolofElectricalandComputer
Engineering
GeorgiaInstituteofTechnology
ACKNOWLEDGEMENTS
My Ph.D. would not have been successfully finished without support and
encouragement from my wife, Kyung Im. Besides this dissertation, our two
kids, Dowan and Jiu, are our big accomplishment and great pleasure during the
graduate study. My father, my mother, father-in-law, and late mother-in-law have
beenalwayssupportformeandmyfamily.
This work literally cannot be completed without guidance of my advisor, Prof.
Hyesoon Kim. From the very first project in Prospector to the last moment of this
thesis writing, she always have advised to the right direction and complement
what I missed. I have learned so many valuable lessons from her, not only how
to find good research topics and solve them analytically, but how to be a good
researcherandmentor.
I am also very fortunate to work with Dr. Chi-Keung Luk, for being a mentor
during the internship and a co-advisor to shape my thesis, besides physically
participating for my defense talk. He guided me many technical challenges to
improvemyalgorithms.
I am grateful to all the dissertation members: Prof. Hsien-Hsin Lee, Prof.
Richard Vuduc, and Prof. Santosh Pande. I particularly thank to Prof. Pande
for providing me a view from a compiler perspective and sharing time in Korea. I
thank to Prof. Milos Prvulovic for guiding my early graduate research. I am also
thankfultoBevinBrettandJohnPieperduringmyinternshipatNashua.
Ithanktoallourlabmembers: Sunpyoforhavingthesamechallengestofinish
the degree and find a job; Jaekyu and Nagesh for setting up our lab machines in
earlydaysandhelpingmanyLinux-relatedproblems;HyojongandChayoungfor
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helping parallelization experiments for the post-analyzer; Pranith for the parallel
prophetworkandstruggles;andPuyanforthechallengingandinterestingLLVM-
Prospector work and enjoying many talks. I also thank to my friends: Changhee
andSangminforbeinggreatcolleaguestodiscussthedetailsofmyandtheirwork;
MyungcheolforsharingmanytrickysituationsinPh.D.life;Minforsettlingdown
togetherintheUS;andDonghwan,whosedissertationprefacecanbesharedwith
mine,forbighelpduringjobsearching.
Ithanktomanyfamilieswhostayedtogetherinthetenthandhomeapartments
foryears,especiallyforChungHyuk’s,Hyojun’s,Hyungie’sJin-Kook’s,Kihwan’s,
Min’s,Minsung’s,Seung-Joon’s,Taesu’s,andTonghoon’sfamilies.
iv
TABLE OF CONTENTS
ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
LISTOFTABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
LISTOFFIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi
I INTRODUCTION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 TheProblem: ParallelizingSerialCode . . . . . . . . . . . . . . . . . 1
1.2 TheSolutionandContributions: Prospector . . . . . . . . . . . . . . 4
1.3 ThesisStatement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 OrganizationofThisProposalDocument . . . . . . . . . . . . . . . . 7
II MOTIVATIONSANDOVERVIEWOFPROSPECTOR . . . . . . . . . . 8
2.1 MotivationsforanEfficientLoopProfiler . . . . . . . . . . . . . . . 8
2.2 MotivationsforDynamicData-DependenceAnalysis . . . . . . . . . 11
2.2.1 CaseStudies: AutomaticParallelizationinC/C++Compilers 14
2.3 MotivationsforAnEfficientData-DependenceProfiler . . . . . . . . 20
2.4 MotivationsforCorrectData-DependenceProfiling . . . . . . . . . . 23
2.4.1 ExperimentationResultsofSimpleSamplingTechniques . . 26
2.5 MotivationsforDynamicParallelSpeedupPrediction . . . . . . . . 29
2.6 MotivationsforANewSpeedupPredictionAlgorithm . . . . . . . . 33
2.7 BackgroundonSchedulingPoliciesofOpenMPandCilkPlus . . . . 36
2.7.1 SchedulingPoliciesinOpenMP . . . . . . . . . . . . . . . . . 37
2.7.2 RecursiveandNestedParallelisminOpenMPandCilkPlus 37
2.8 MotivationsforPost-AnalyzerofDependenceProfiler . . . . . . . . 40
2.9 OverviewofProspector . . . . . . . . . . . . . . . . . . . . . . . . . . 42
III RELATEDWORK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.1 RelatedResearchonLoopProfiling . . . . . . . . . . . . . . . . . . . 44
v
3.2 RelatedResearchonData-DependenceProfiling . . . . . . . . . . . 45
3.2.1 Data-DependenceProfilingforSpeculativeParallelization . 47
3.2.2 ReducingTimeOverheadofDynamicAnalysis . . . . . . . . 47
3.2.3 LimitationsofPreviousCompressionTechniques . . . . . . . 48
3.3 RelatedResearchonDynamicSpeedupPrediction . . . . . . . . . . 48
3.4 RelatedResearchonPostAnalysisofDependenceProfiling . . . . . 50
3.4.1 ParallelismDiscoveryUsingDynamicAnalyses . . . . . . . 50
3.4.2 CodeTransformationtoAvoidDependences . . . . . . . . . 51
3.4.3 Bug-FixingAlgorithmsinConcurrentProgramming . . . . . 52
3.4.4 ParallelismVisualization . . . . . . . . . . . . . . . . . . . . . 53
3.5 ToolsforAssistingParallelization . . . . . . . . . . . . . . . . . . . . 54
3.5.1 IntelParallelAdvisor . . . . . . . . . . . . . . . . . . . . . . . 55
3.5.2 VectorFabrics’Pareon . . . . . . . . . . . . . . . . . . . . . . 56
3.5.3 RogueWave’sThreadSpotter . . . . . . . . . . . . . . . . . . 58
IV ANEFFICIENTLOOPPROFILER . . . . . . . . . . . . . . . . . . . . . . 59
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.2 AnEfficientLoop-ProfilingAlgorithm . . . . . . . . . . . . . . . . . 60
4.2.1 ReconstructingCFGsandLoopStructuresfromBinary . . . 61
4.2.2 InstrumentingLoop-BehaviorInstructions . . . . . . . . . . . 62
4.3 Challenges: CaseStudiesandSolutions . . . . . . . . . . . . . . . . . 64
4.4 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.5 ExperimentalResults . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.6 SummaryofThisChapter . . . . . . . . . . . . . . . . . . . . . . . . 69
V ANEFFICIENTDYNAMICDATA-DEPENDENCEPROFILER . . . . . 71
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.2 TheBaselineAlgorithm: ThePairwiseMethod . . . . . . . . . . . . 72
5.2.1 CheckingDataDependencesinaLoopNest . . . . . . . . . . 73
5.2.2 HandlingLoop-independentDependences . . . . . . . . . . 76
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5.2.3 DependencesinFunctionsandHandlingFunctionCalls . . . 78
5.2.4 ComputingData-DependenceDistance . . . . . . . . . . . . 79
5.2.5 SummaryofthePairwiseMethod . . . . . . . . . . . . . . . . 80
5.2.6 OptimizingtheMergeOperation: PC-setOptimization . . . 82
5.2.7 ProblemsofthePairwiseMethod . . . . . . . . . . . . . . . . 84
5.3 AMemory-EfficientAlgorithminSD3 . . . . . . . . . . . . . . . . . 85
5.3.1 OverviewoftheAlgorithm . . . . . . . . . . . . . . . . . . . 85
5.3.2 DynamicDetectionofStrides . . . . . . . . . . . . . . . . . . 86
5.3.3 Stride-BasedDependenceCheckingAlgorithm . . . . . . . . 88
5.3.4 OverviewoftheMemory-EfficientSD3 Algorithm . . . . . . 93
5.3.5 OptimizingStride-BasedDependenceChecking . . . . . . . 94
5.3.6 MergingStrideTablesforLoopNests . . . . . . . . . . . . . . 98
5.3.7 HandlingKilledAddressesinStrides . . . . . . . . . . . . . . 99
5.3.8 LossyCompressioninStrides . . . . . . . . . . . . . . . . . . 100
5.4 ReducingTimeOverheadbyParallelization . . . . . . . . . . . . . . 101
5.4.1 OverviewoftheAlgorithm . . . . . . . . . . . . . . . . . . . 101
5.4.2 AHybridParallelizationModelofSD3 . . . . . . . . . . . . . 101
5.4.3 EventDistributionforParallelProcessing . . . . . . . . . . . 103
5.4.4 StridesinParallelizedSD3 . . . . . . . . . . . . . . . . . . . . 105
5.4.5 DetailsoftheData-ParallelModel . . . . . . . . . . . . . . . 106
5.5 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.5.1 BasicArchitecture . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.5.2 ImplementationofAnalyzer . . . . . . . . . . . . . . . . . . . 110
5.5.3 ImplementationofTracers . . . . . . . . . . . . . . . . . . . . 111
5.6 ExperimentationResults . . . . . . . . . . . . . . . . . . . . . . . . . 113
5.6.1 ExperimentationMethodology . . . . . . . . . . . . . . . . . 113
5.6.2 MemoryOverheadofSD3 . . . . . . . . . . . . . . . . . . . . 115
5.6.3 TimeOverheadofSD3 . . . . . . . . . . . . . . . . . . . . . . 117
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5.6.4 InputSensitivityofData-DependenceProfiling . . . . . . . . 119
5.6.5 OpportunitiesforStrideCompression . . . . . . . . . . . . . 121
5.7 SummaryofThisChapter . . . . . . . . . . . . . . . . . . . . . . . . 122
VI ANEFFECTIVESPEEDUPPREDICTORFROMSERIALCODE . . . . 123
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
6.2 TheFront-endofParallelProphet: AnnotationandProfiling
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
6.2.1 AnnotatingSerialCode . . . . . . . . . . . . . . . . . . . . . . 125
6.2.2 IntervalProfilingtoBuildaProgramTree . . . . . . . . . . . 126
6.2.3 MemoryProfiling: BurdenFactors . . . . . . . . . . . . . . . 128
6.2.4 SummaryoftheProfiling . . . . . . . . . . . . . . . . . . . . . 130
6.3 TheBackendofParallelProphet: TheEmulators . . . . . . . . . . . 130
6.3.1 Fast-ForwardingEmulationAlgorithm . . . . . . . . . . . . . 131
6.3.2 ChallengesandLimitationsinFast-ForwardingMethod . . . 137
6.3.3 Program-Synthesis-BasedEmulationAlgorithm: BasicIdea . 138
6.3.4 ChallengesintheSynthesizer . . . . . . . . . . . . . . . . . . 140
6.3.5 SummaryoftheEmulations . . . . . . . . . . . . . . . . . . . 142
6.4 LightweightMemoryPerformanceModel . . . . . . . . . . . . . . . 143
6.4.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
6.4.2 ThePerformanceModel . . . . . . . . . . . . . . . . . . . . . 145
6.4.3 TheDefinitionoftheBurdenFactor . . . . . . . . . . . . . . . 146
6.4.4 MemoryAccessOverhead,w Prediction . . . . . . . . . . . . 147
t
6.4.5 DetailsoftheMicrobenchmarkforw Prediction . . . . . . . 149
t
6.4.6 SummaryoftheMemoryPerformanceModel . . . . . . . . . 154
6.5 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
6.5.1 ImplementationofAnnotationSystem . . . . . . . . . . . . . 155
6.5.2 ImplementationofIntervalProfiling . . . . . . . . . . . . . . 156
6.5.3 CompressionoftheProgramTree . . . . . . . . . . . . . . . . 158
6.6 ExperimentationResults . . . . . . . . . . . . . . . . . . . . . . . . . 159
viii
6.6.1 ExperimentationMethodologies . . . . . . . . . . . . . . . . 160
6.6.2 ValidationofthePredictionModel . . . . . . . . . . . . . . . 160
6.6.3 PredictionResultswithMemoryPerformanceModel . . . . 165
6.6.4 ASimpleVerificationfortheMemoryPerformanceModel . 167
6.6.5 DetailedCacheandMemoryBehaviorsoftheBenchmarks . 168
6.6.6 TheoverheadofParallelProphet . . . . . . . . . . . . . . . . 171
6.6.7 LimitationsofParallelProphet . . . . . . . . . . . . . . . . . 172
6.7 SummaryofThisChapter . . . . . . . . . . . . . . . . . . . . . . . . 173
VII ALGORITHMS TO EXTRACT PARALLELISM AND TRANSFORMA-
TIONADVICE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
7.1 AssumptionsforthePost-Analyzer . . . . . . . . . . . . . . . . . . . 174
7.2 OverviewofthePost-Analyzer . . . . . . . . . . . . . . . . . . . . . 175
7.2.1 FalseDependencesandPrivatization . . . . . . . . . . . . . . 176
7.2.2 ReductionsandMutexes . . . . . . . . . . . . . . . . . . . . . 177
7.2.3 Supportingconditionvariablesandbarriers . . . . . . . . . . 179
7.2.4 Pipelining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
7.2.5 OrderingofTransformations . . . . . . . . . . . . . . . . . . . 180
7.3 CaseStudiesofProspector . . . . . . . . . . . . . . . . . . . . . . . . 182
7.3.1 179.artinSPECCPU2000 . . . . . . . . . . . . . . . . . . . . . 182
7.3.2 SusaninMiBench . . . . . . . . . . . . . . . . . . . . . . . . . 185
7.3.3 DijkstrainMiBench . . . . . . . . . . . . . . . . . . . . . . . . 189
7.3.4 256.bzip2inSPECCPU2000 . . . . . . . . . . . . . . . . . . . 193
VIIICONCLUSIONSANDFUTURERESEARCHDIRECTIONS . . . . . . 198
8.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198
8.2 FutureResearchDirections . . . . . . . . . . . . . . . . . . . . . . . . 200
8.2.1 FutureworkforSD3 . . . . . . . . . . . . . . . . . . . . . . . . 200
8.2.2 FutureworkforParallelProphet . . . . . . . . . . . . . . . . 201
8.2.3 FutureworkforthePost-Analyzer . . . . . . . . . . . . . . . 202
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REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204
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