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293 Pages·2008·0.92 MB·English

Proceedings
nd
22 IEEE International Parallel and
Distributed Processing Symposium
IPDPS2008AdvanceProgramAbstracts
Abstracts for both contributed papers and all workshops have been
compiledto allow authorsto checkaccuracyand sothat visitors to this
Websitemaypreviewthepaperstobepresentedattheconference. Full
proceedings of the conference will be published on a cdrom to be dis-
tributedtoregistrantsattheconference.
Summary of Contents
IEEEInternationalParallelandDistributedProcessingSymposium 1
Session1: Algorithms-Scheduling 2
Session2: Applications-GeneralApplications 5
Session3: Architecture-Input/Output 8
Session4: Software-RedundancyandFaults 11
Session5: Algorithms-NumericalAlgorithms 14
Session6: Applications-P2PSystemsArchitecture 17
Session7: Architecture-Multi-core 21
Session8: Software-ImplementingMessagePassing 24
Session9: Algorithms-P2PandOverlayNetworks 27
Session10: Applications-Grids 30
Session11: Architecture-Supercomputing/SIMD 34
PlenarySession: BestPapers 37
Session12: Algorithms-CommunicationAlgorithms 41
Session13: Applications-P2PStructure 44
Session14: Architecture-Power/SMT/ILP 47
Session15: Software-TuningandPerformance 50
Session16: Algorithms-Theory 53
Session17: Applications-P2PReliabilityandTrust 56
Session18: Architecture-Networks 59
Session19: Software-LanguageFeaturesandImplementation 63
Session20: Algorithms-FaultTolerance 66
Session21: Applications-Sensors 69
Session22: Applications-WebApplications 72
Session23: Software-ResourceManagementandScheduling 75
Session24: Algorithms-SensorNetworks 78
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Session25: Applications-AdditionalApplications 81
Session26: Applications-ApplicationsandtheCellProcessor 84
HeterogeneityinComputingWorkshop 87
WorkshoponParallelandDistributedReal-TimeSystems 94
ReconfigurableArchitecturesWorkshop 101
WorkshoponHigh-LevelParallelProgrammingModels&SupportiveEnvironments 122
WorkshoponJavaandComponentsforParallelism,Distribution andConcurrency 130
WorkshoponNatureInspiredDistributedComputing 136
WorkshoponHigh PerformanceComputationalBiology 146
AdvancesinParallelandDistributedComputingModels 152
CommunicationArchitectureforClusters 162
NSFNextGenerationSoftwareProgram 168
High-Performance,Power-AwareComputing 198
High PerformanceGridComputing 204
WorkshoponSystemManagementTechniques,Processes,andServices 211
WorkshoponParallelandDistributedScientificandEngineeringComputing 218
PerformanceModeling,Evaluation,andOptimisation ofUbiquitousComputingandNetworked
Systems 232
DependableParallel,DistributedandNetwork-CentricSystems 242
InternationalWorkshoponSecurityinSystemsandNetworks 250
InternationalWorkshoponHotTopicsinPeer-to-PeerSystems 256
WorkshoponLarge-Scale,VolatileDesktopGrids 263
WorkshoponMulti-ThreadedArchitecturesandApplications 271
WorkshoponParallelandDistributedComputinginFinance 277
WorkshoponLarge-ScaleParallelProcessing 284
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IEEE International Parallel & Distributed
Processing Symposium
IPDPS 2008
1
SESSION 1
Algorithms - Scheduling
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Efficient Resource Management Using Advance Reservations for Heterogeneous
Grids
ClarisCastillo,GeorgeN.RouskasandKhaledHarfoush
DepartmentofComputerScience
NorthCarolinaStateUniversity
Raleigh,NC27695
Email: {ccastil,rouskas,kaharfou}@ncsu.edu
Supportforadvance reservationsofresourcesplaysakeyroleinGridresourcemanagement asitenables thesystemto
meetuserexpectationswithrespecttotimerequirementsandtemporaldependenceofapplications, increasespredictability
of the system and enables co-allocation of resources. Despite these attractive features, adoption of advance reservations is
limitedmainlyduetothefactthatrelatedalgorithmsaretypicallycomplexandfailtoscaletolargeandloadedsystems. In
thisworkweconsidertwoaspectsofadvancereservations. First,weinvestigatetheimpactofheterogeneityonGridresource
management when advance reservations are supported. Second, we employ techniques from computational geometry to
developanefficientheterogeneity-awareschedulingalgorithm. OurmainfindingisthatGridsmaybenefitfromhighlevels
of resource heterogeneity, independently of the total system capacity. Our results show that our algorithm performs well
acrossseveraluserandsystemperformanceandovercomethelackofscalabilityandadaptabilityofexistingmechanisms.
Online Scheduling in Grids
UweSchwiegelshohn AndreiTchernykh
TechnischeUniversita¨tDortmund CICESEResearchCenter
RoboticsResearchInstitute 22860Ensenada,BajaCalifornia,Mexico
44221Dortmund, Germany [email protected]
[email protected]
RaminYahyapour
TechnischeUniversita¨tDortmund
ITandMediaCenter
44221Dortmund, Germany
[email protected]
ThispaperaddressesnonclairvoyantandnonpreemptiveonlinejobschedulinginGrids. Intheappliedbasicmodel, the
Gridsystemconsistsofalargenumberofidenticalprocessorsthataredividedintoseveralmachines. Jobsareindependent,
theyhaveafixeddegreeofparallelism,andtheyaresubmittedovertime.Further,ajobcanonlybeexecutedontheprocessors
belongingtothesamemachine. Itisourgoaltominimizethetotalmakespan. WeshowthattheperformanceofGareyand
Graham’slistschedulingalgorithmissignificantlyworseinGridsthaninmultiprocessors.ThenwepresentaGridscheduling
algorithmthatguaranteesacompetitivefactorof5. Thisalgorithmcanbeimplementedusinga“jobstealing”approachand
maybewellsuitedtoserveasastartingpointforGridschedulingalgorithmsinrealsystems.
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Scheduling with Storage Constraints
ErikSaule,Pierre-Franc¸oisDutotandGre´goryMounie´
LIG-MOAISTeam
GrenobleUniversite´s,France
{firstname.lastname}@imag.fr
Cumulative memory occupation is a quite intuitive but not so studied constraint in scheduling. The interest in such a
constraint is present in multi-System-on-Chip, embedded systems for storing instruction code, or in scientific computation
forstoringresults.Memoryoccupationseenasaconstraintisimpossibletosolvewithapproximationalgorithms.Webelieve
thattransformingtheconstraintintoasecondobjectivetooptimizehelpstodealwithsuchconstraints.
The problem addressed in this paper is to schedule tasks on identical processors in order to minimize both maximum
completion time and maximum cumulative memory occupation. For independent tasks, a family of algorithms with good
approximationratiosbasedonaPTASisgiven. Severalapproximationratiosareprovedtobeimpossibletoachievewithany
schedule. TheprecedenceconstrainedcaseisthenstudiedandafamilyofperformanceguaranteedalgorithmsbasedonList
Schedulingisproposed. Finally,optimizingthemeancompletiontimeasathirdobjectiveisalsostudiedandatri-objective
algorithmisgiven.
Portioned Static-Priority Scheduling on Multiprocessors
ShinpeiKatoandNobuyuki Yamasaki
SchoolofScienceforOpenandEnvironmentalSystems
KeioUniversity,Japan
{shinpei,yamasaki}@ny.ics.keio.ac.jp
Thispaperproposesanefficientreal-timeschedulingalgorithmformultiprocessorplatforms.Thealgorithmisaderivative
oftheRateMonotonic(RM)algorithm,withitsbasisontheportionedschedulingtechnique. Thetheoreticaldesignofthe
algorithm is well implementable for practical use. The schedulability of the algorithm is also analyzed to guarantee the
worst-case performance. The simulation results show that the algorithm achieves higher system utilizations, in which all
tasksmeetdeadlines,withasmallnumberofpreemptionscomparedtotraditionalalgorithms.
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SESSION 2
Applications - General Applications
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A Parallel Software Toolkit for Statistical 3-D Virus Reconstructions from Cryo
Electron Microscopy Images Using Computer Clusters with Multi-core
Shared-Memory Nodes
YiliZhengandPeterC.Doerschuk
SchoolofElectricalandComputer Engineering,PurdueUniversity
WestLafayette,IN47907-1285 USA
DepartmentofBiomedicalEngineeringandSchoolof
ElectricalandComputerEngineering,CornellUniversity
Ithaca,NY14853-5401 USA
A statistical approach for computing 3-D reconstructions of virus particles from cryo electron microscope images and
minimalpriorinformationhasbeendevelopedwhichcansolvearangeofspecificproblems. Thestatisticalapproachcauses
high computation and storage complexity in the software implementation. A parallel software toolkit is described which
allowstheconstructionofsoftwaretargetedatcommodityPCclusterswhichismodular,reusable,anduser-transparentwhile
atthesametimedeliveringnearlylinearspeeduponpracticalproblems.
Modeling and Predicting Application Performance on Parallel Computers Using
HPC Challenge Benchmarks
WaynePfeifferandNicholasJ.Wright
SanDiegoSupercomputerCenter,LaJollaCA92093-0505, USA
{pfeiffer,nwright}@sdsc.edu
Amethodispresentedformodelingapplicationperformanceonparallelcomputersintermsoftheperformanceofmicro-
kernelsfromtheHPCChallengebenchmarks. Specifically,theapplicationruntimeisexpressedasalinearcombinationof
inversespeedsandlatenciesfrommicrokernelsorsystemcharacteristics.Themodelparametersareobtainedbyanautomated
seriesofleastsquaresfitsusingbackwardeliminationtoensurestatisticalsignificance. Ifnecessary, outliersaredeletedto
ensure that the final fit is robust. Typically three or four terms appear in each model: at most one each for floating-point
speed, memorybandwidth, interconnectbandwidth, andinterconnectlatency. Suchmodelsallowpredictionofapplication
performanceonfuturecomputersfromeasier-to-makepredictionsofmicrokernelperformance.
ThemethodwasusedtobuildmodelsforfourbenchmarkproblemsinvolvingthePARATECandMILCscientificappli-
cations. Thesemodelsnotonlydescribeperformancewellonthetencomputersusedtobuildthemodels,butalsodoagood
jobofpredictingperformanceonthreeadditionalcomputerswithnewerdesignfeatures. Forthefourapplicationbenchmark
problems with six predictions each, the relative root mean squared error in the predicted run times varies between 13 and
16%.
ThemethodwasalsousedtobuildmodelsfortheHPLandG-FFTEbenchmarksinHPCC,includingfunctionaldepen-
dencesonproblemsizeandcorecountfromcomplexityanalysis. ThemodelforHPLpredictsperformanceevenbetterthan
theapplicationmodelsdo,whilethemodelforG-FFTEsystematicallyunderpredictsruntimes.
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Optimizations in Financial Engineering: The Least-Squares Monte Carlo
Method of Longstaff and Schwartz
AnamitraRoyChoudhury AlanKing
IBMIndiaResearchLab,Plot4 IBMT.J.WatsonResearchCenter,
Block-C,Institutional Area 10598Yorktown Heights,NY,USA
VasantKunj,NewDelhi,India
SunilKumar YogishSabharwal
Dept. ofElectricalEngineering, IBMIndiaResearchLab,Plot4
UniversityofSouthernCalifornia, Block-C,Institutional Area
LosAngeles,USA VasantKunj,NewDelhi,India
InthispaperweidentifyimportantopportunitiesforparallelizationintheLeast-SquaresMonteCarlo(LSM)algorithm,
due to Longstaff and Schwartz, for the pricing of American options. The LSM method can be divided into three phases:
Path-simulation, CalibrationandValuation. Wedescribehoweachofthesephasescanbeparallelized, withmorefocuson
theCalibrationphase,whichisinherentlymoredifficulttoparallelize.
We implemented these parallelization techniques on Blue Gene using the Quantlib open source financial engineering
package. We achieved up to factor of 9 speed-up for the Calibration phase and 18 for the complete LSM method on a 32
processorBG/Psystemusingmonomialbasisfunctions.
Massively Parallel Cosmological Simulations with ChaNGa
PritishJetley,FilippoGioachin,CelsoMendesandLaxmikantV.Kale´
Dep. ofComputerScience,UniversityofIllinois atUrbana-Champaign, USA
{pjetley2, gioachin,cmendes,kale}@uiuc.edu
ThomasQuinn
Dep. ofAstronomy,UniversityofWashington,USA
[email protected]
Cosmologicalsimulatorsareanimportantcomponentinthestudyoftheformationofgalaxiesandlargescalestructures,
and can help answer many important questions about the universe. Despite their utility, existing parallel simulators do not
scale effectively on modern machines containing thousands of processors. In this paper we present ChaNGa, a recently
released production simulator based on the Charm++ infrastructure. To achieve scalable performance, ChaNGa employs
variousoptimizationsthatmaximizetheoverlapbetweencom-putationandcommunication.Wepresentexperimentalresults
ofChaNGasimulationsonmachineswiththousandsofprocessors,includingtheIBMBlueGene/LandtheCrayXT3. The
paper goes on to highlight efforts toward even more effcient and scalable cosmological simulations. In particular, novel
loadbalancingschemesthatbasedecisionsoncertaincharacteristicsoftree-basedparticlecodesarediscussed. Further,the
multisteppingcapabilitiesofChaNGaarepresented,asaresolutionstotheloadimbalancethatsuchmultiphasesimulations
face. Weoutlinekeyrequirementsforaneffectivepracticalimplementationandconcludebydiscussingpreliminaryresults
fromsimulationsrunwithourmultiphaseloadbalancer.
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