Compile Time Task and Resource Allocation of Concurrent Applications to Multiprocessor Systems

203 Pages·2009·1.65 MB·English

Compile Time Task and Resource Allocation of
Concurrent Applications to Multiprocessor Systems
Nadathur Rajagopalan Satish
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2009-19
http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-19.html
January 29, 2009
Copyright 2009, by the author(s).
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CompileTimeTaskandResourceAllocationofConcurrentApplicationsto
MultiprocessorPlatforms
by
NadathurRajagopalanSatish
B.Tech. (IITKharagpur)2003
Adissertationsubmittedinpartialsatisfactionofthe
requirementsforthedegreeof
DoctorofPhilosophy
in
Engineering-ElectricalEngineeringandComputerSciences
inthe
GRADUATEDIVISION
ofthe
UNIVERSITYOFCALIFORNIA,BERKELEY
Committeeincharge:
ProfessorKurtKeutzer,Chair
ProfessorJohnWawrzynek
ProfessorAlperAtamtu¨rk
Spring2009
ThedissertationofNadathurRajagopalanSatishisapproved:
Chair Date
Date
Date
UniversityofCalifornia,Berkeley
Spring2009
CompileTimeTaskandResourceAllocationofConcurrentApplicationsto
MultiprocessorPlatforms
Copyright2009
by
NadathurRajagopalanSatish
1
Abstract
CompileTimeTaskandResourceAllocationofConcurrentApplicationsto
MultiprocessorPlatforms
by
NadathurRajagopalanSatish
DoctorofPhilosophyinEngineering-ElectricalEngineeringandComputerSciences
UniversityofCalifornia,Berkeley
ProfessorKurtKeutzer,Chair
Single-chipmultiprocessorsarenowcommonlypresentinbothembeddedanddesktopsystems. A
keychallengebeforeaprogrammerofmodernsystemsistoproductivelyprogramthemultiproces-
sordevicespresentinthesesystemsandutilizetheparallelismavailableinthem. Thismotivatesthe
developmentofautomatedtoolsforparallelapplicationdevelopment. Animportantstepinsuchan
automatedflowisallocatingandschedulingtheconcurrenttaskstotheprocessingandcommunica-
tionresourcesinthearchitecture. Whentheapplicationworkloadandexecutionprofilesareknown
orcanbeestimatedatcompiletime,thenwecanperformtheallocationandschedulingstaticallyat
compiletime. Manyapplicationsinsignalprocessingandnetworkingcanbescheduledatcompile
time. Compile time scheduling incurs minimal overhead while running the application. It is also
relevanttorapiddesign-spaceexplorationofmicro-architectures.
Scheduling problems that arise in realistic application deployment and design space explo-
ration frameworks can encompass a variety of objectives and constraints. In order for scheduling
techniquestobeusefulforrealisticexplorationframeworks,theymustthereforebesufficientlyex-
tensibletobeappliedtoarangeofproblems. Atthesametime,theymustbecapableofproducing
high quality solutions to different scheduling problems. Further, such techniques must be compu-
tationallyefficient, especiallywhentheyareusedtoevaluatemanymicro-architecturesinadesign
spaceexplorationframework.
Thefocusofthisdissertationistoprovideguidanceinchoosingschedulingmethodsthatbest
trade-off the flexibility with solution time and quality of the resulting schedule. We investigate
andevaluaterepresentativesofthreebroadclassesofschedulingmethods: heuristics, evolutionary
2
algorithms and constraint programming. In order to evaluate these techniques, we consider three
practical task-level scheduling problems: task allocation and scheduling onto multiprocessors, re-
source allocation and scheduling data transfers between CPU and GPU memories, and scheduling
applications with variable task execution times and dependencies onto multiprocessors. We use
applications from the networking, media and machine learning domains to benchmark our tech-
niques. The above three scheduling problems, while all arising from practical mapping concerns,
requiredifferentmodels,havedifferingconstraintsandoptimizefordifferentobjectives. Thediver-
sity of these problems gives us a base for studying the extensibility of scheduling methods. It also
helpsprovideamoreholisticviewofthemeritsofdifferentschedulingapproachesintermsoftheir
efficiencyandqualityofsolutionsproducedongeneralschedulingproblems.
ProfessorKurtKeutzer
DissertationCommitteeChair
i
Dedicatedtomyfamily.
ii
Contents
ListofFigures v
ListofTables viii
1 Introduction 1
1.1 Challengesintheuseofsinglechipmultiprocessors . . . . . . . . . . . . . . . . . 2
1.2 Bridgingtheimplementationgap . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Themappingstep . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.1 Complexityoftheschedulingproblem . . . . . . . . . . . . . . . . . . . . 7
1.3.2 Thecaseforcompile-timescheduling . . . . . . . . . . . . . . . . . . . . 8
1.3.3 MethodsforCompile-timeScheduling . . . . . . . . . . . . . . . . . . . . 9
1.4 Applicationofcompile-timemethodstopracticalschedulingproblems . . . . . . . 12
1.5 Contributionsofthedissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2 StaticTaskAllocationandScheduling 15
2.1 MappingstreamingapplicationsontosoftmultiprocessorsystemsonFPGAs . . . 15
2.1.1 ExamplesofStreamingApplications. . . . . . . . . . . . . . . . . . . . . 16
2.1.2 TheMappingandSchedulingProblem. . . . . . . . . . . . . . . . . . . . 17
2.1.3 SoftMultiprocessorSystemsonFPGAs . . . . . . . . . . . . . . . . . . . 18
2.1.4 Theneedforautomatedmapping . . . . . . . . . . . . . . . . . . . . . . 20
2.2 AutomatedTaskAllocationandScheduling . . . . . . . . . . . . . . . . . . . . . 20
2.2.1 StaticModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.2.2 OptimizationProblem . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.3 TechniquesforStaticTaskAllocationandScheduling . . . . . . . . . . . . . . . . 28
2.3.1 HeuristicMethods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.3.2 SimulatedAnnealing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.3.3 ConstraintOptimizationMethods . . . . . . . . . . . . . . . . . . . . . . 35
2.3.4 Lowerbounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.4 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.4.1 Benchmarks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.4.2 Comparisonsonregularprocessorarchitectures . . . . . . . . . . . . . . . 42
2.4.3 Comparisononrealisticarchitecturalmodels . . . . . . . . . . . . . . . . 46
2.4.4 Throughputestimationusingmakespan . . . . . . . . . . . . . . . . . . . 48
iii
2.5 Comparingdifferentoptimizationapproaches . . . . . . . . . . . . . . . . . . . . 50
3 ResourceallocationandcommunicationschedulingonCPU/GPUsystems 52
3.1 MappingapplicationswithlargedatasetsontoCPU-GPUsystems . . . . . . . . . 52
3.1.1 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.1.2 CPU/GPUsystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.1.3 Themappingstep . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.2 TaskandDatatransferschedulingtominimizedatatransfers . . . . . . . . . . . . 61
3.2.1 StaticModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.2.2 OptimizationProblem . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.3 TechniquesforStaticOptimization . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.3.1 PreviousWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.3.2 ExactMILPformulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
3.3.3 Decomposition-basedApproaches . . . . . . . . . . . . . . . . . . . . . . 75
3.3.4 Datatransferschedulinggivenataskorder . . . . . . . . . . . . . . . . . 75
3.3.5 Findingagoodtaskordering . . . . . . . . . . . . . . . . . . . . . . . . . 77
3.4 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
3.5 ChoiceofOptimizationMethod . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4 StatisticalModelsandAnalysisforTaskScheduling 89
4.1 Variabilityinapplicationexecutiontimes . . . . . . . . . . . . . . . . . . . . . . 91
4.1.1 IPv4packetforwardingonasoftmultiprocessorsystem . . . . . . . . . . 92
4.1.2 H.264videodecodingoncommercialmulti-coreplatforms . . . . . . . . . 96
4.2 StatisticalModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
4.2.1 ApplicationTaskGraph . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
4.2.2 ArchitectureModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4.2.3 PerformanceModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4.2.4 OptimizationProblem . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.3 StatisticalPerformanceAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
4.3.1 Validallocationandschedule. . . . . . . . . . . . . . . . . . . . . . . . . 107
4.3.2 Formulationoftheperformanceanalysisproblem . . . . . . . . . . . . . . 108
4.3.3 Typesofperformanceanalysis . . . . . . . . . . . . . . . . . . . . . . . . 110
4.3.4 ComparisonofStatisticaltoStaticAnalysis . . . . . . . . . . . . . . . . . 115
4.4 Theneedforgeneralizedstatisticalmodelsandanalysis . . . . . . . . . . . . . . . 119
4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5 StatisticalOptimization 122
5.1 Statisticaltaskallocationandschedulingontomultiprocessors . . . . . . . . . . . 122
5.2 TechniquesforStatisticalOptimization. . . . . . . . . . . . . . . . . . . . . . . . 123
5.2.1 StatisticalDynamicListScheduling . . . . . . . . . . . . . . . . . . . . . 124
5.2.2 SimulatedAnnealing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
5.2.3 DeterministicOptimizationApproaches . . . . . . . . . . . . . . . . . . . 134
5.3 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
5.4 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
5.4.1 Benchmarks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136