最终硕士论文 高性能计算

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JavaV.S.PythoninAI

FinalMasterThesis–HighPerformanceComputing

MasterinInnovationandResearchinInformaticsFacultatd’inform`aticadeBarcelonaUniversitatPolit`ecnicadeCatalunya

Author:

MarcoM.AguadoAcevedo

marcoaguado95@

Supervisors:MarisaGilGómez

marisa.gil@

October28,2021

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10

Acknowledgements

Firstandforemost,IwouldliketousethissectiontothankprofessorMarisaGilfortheopportunityshegavetometoworkonthisinterestingproject.

IwouldalsoliketothankprofessorXavierforprovidingmeaccesstotheCTE-ARMsystemoftheBSCandforprovidingadviceregardingtheanalysisandtheexecutionprocess.

NotonlydoIwanttoexpressmygratitudetowardsthemfortheunconditionalsupport,butalsofortheirpatienceandkindness.

Icannotendthisnotewithoutthankingmywholefamily,especiallymyparentsandaunt,whosup-portedmethroughoutnotonlymyacademiccareerbutalsomylife.

Abstract

Java’spositionasanappealingprogramminglanguagefordevelopingAIsoftwareisnotclearinthecurrentresearcharea.Itiswell-stablishedthatotherprogramminglanguagessuchasPythonhavegrowninpopularityintheAIresearchcommunityasaneasytolearn,easytouseandsimplecodetooloverthemorestrictandcomplexlanguagessuchasJava.Also,withtheincreaseinthenumberofprocessorswithARMarchitectureusedforserverpurposesduetotheirbetterenergyefficiency,theinterestintheirperformanceforspecificresearchareasalsoincreases.Thisthesisaimstoprovideanobjectiveandup-to-dateviewofJava’scurrentsituationinthisareabycomparingitwithPythonbothintermsofthecodingprocessandtheirperformance.

TotestifJavacouldprovemorevaluetoresearchersthanPython,asetofrepresentativeAIalgorithmswereimplementedinbothlanguagesusingthenowadaysmostusedlibraries:WekaforJavaandScikit-learn,PandasandNumpyforPythonandexecutedintwodifferentmachinesfromtheMareNostrum4system:TheIntelmachinewithx86architectureprocessorsandtheCTE-ARMmachinewithARMarchitectureprocessorsusing4differentdatasetswithitsowncharacteristicsandsizes.

TheresultsobtainedshowthatJavaperformsworsethanPythoninalmostallthetestsbyhavinghigherexecutiontimesandworsememorymanagementwhiletheimpactofusingamachinewithanARMprocessorisminimalalthoughingeneralitperformsbetterthanx86processor,especiallywhencomparingJava’sperformanceinbothmachines.

TheseresultssuggestthatJavadoesnotprovideenoughvaluetobecauseamoresuitableprogramminglanguagethanPythonforAIresearchersastheobtainedperformancedoesnotcompensatetheextraeffortbothinlearningtimeandcodingtimeJavarequirestoimplementprogramsthatsolvetheexactsameproblems.

Keywords:Java,Python,ArtificialIntelligence,ARM,performance,code

Index

Introduction 8

Stateoftheart 8

Proposal 9

Objectives 10

Methodology 11

StudyoftheexistingandmostrepresentativealgorithmsintheAI 11

Regression 11

Classification 12

StudyoftheavailableHardwaresystems 16

StudyofJavaandPythonprograminglanguages 16

Java 16

Python 19

Studyofthecharacteristicsandmetricstocompare 20

Code 20

Performance 21

StudyandresearchofexistingAIlibraries 21

Python 21

Java 24

Studyandresearchofpossiblemonitoringtools 25

Python 25

Java 26

Systemprovidedtools 26

Implementationofthecodeandpreparationofdata 28

Implementationofthealgorithms 28

Datasetselectionandpreprocessing 28

Experimentaldesignandset-up 33

Filesystemstructure 33

Compilation 35

Execution 36

Datacollection 37

Keyresultsobtainedinthestudy 38

Thecode 38

Theperformance 40

JavaandPythoninx86 40

Javainx86andARM 43

Pythoninx86andARM 46

JavaandPythoninARM 49

Conclusions 52

Futurework 53

Multithreading 53

Usingmorefeaturesofthelibrariesprovidedalgorithms 53

BSCtools 53

Furtherstudyofthecodingprocess 53

Otherprogramminglanguages 53

References 54

Listoffigures

Figure1:Logisticfunctionandgraphicalinterpretation 12

Figure2:HyperplanegeneratedbyaSVM 13

Figure3:DecisionTreerepresentingeachofthenodes 13

Figure4:GraphicalexampleofaKNNvotingprocess 14

Figure5:NaïveBayesrepresentationwherethealgorithmfindscommonfeatures 15

Figure6:Randomforestcomposedby9decisiontreesvotingtheclassresult. 15

Figure7:Javaarchitecturestructure 17

Figure8:JavaVirtualMachinecomponents 18

Figure9:Pythonexecutionprocess 20

Figure10:NumPylogo 22

Figure11:Pandaslogo 22

Figure12:Pandasdatastructures 23

Figure13:Scikit-Learnlogo 23

Figure14:WekaGUI 24

Figure15:Pythonprofilerexample 25

Figure16:JMXinterface 26

Figure17:SLURMlogo 27

Figure18:Sacctstatistics 27

Figure19:CSVrepresentationofadataset 31

Figure20 32

Figure21:Arfffilestructureexample 32

Figure22:GraphicalrepresentationofJavaandPython’sexecutiontimeinMareNostrum4Intel

machine 41

Figure23:GraphicalrepresentationofJavaandPython’sCPUefficiencyinMareNostrum4Intel

machine 41

Figure24:GraphicalrepresentationofJavaandPython’smemoryconsumptioninMareNostrum4

Intelmachine 43

Figure25:GraphicalrepresentationofJava’sexecutiontimeinbothMareNostrum4IntelandCTE-

ARMmachines 44

Figure26:GraphicalrepresentationofJava’sCPUefficiencyinbothMareNostrum4IntelandCTE-

ARMmachines 44

Figure27:GraphicalrepresentationofJava’smemoryconsumptioninbothMareNostrum4Inteland

CTE-ARMmachinesexcludingLinearregression 45

Figure28:GraphicalrepresentationofJava’sLinearregressionmemoryconsumptioninboth

MareNostrum4IntelandCTE-ARMmachines 46

Figure29:GraphicalrepresentationofPython’sExecutiontimeinbothMareNostrum4IntelandCTE-

ARMmachines 47

Figure30:GraphicalrepresentationofPython’sCPUefficiencyinbothMareNostrum4IntelandCTE-

ARMmachines 47

Figure31:GraphicalrepresentationofPython’sLinearregressionmemoryconsumptioninboth

MareNostrum4IntelandCTE-ARMmachines 48

Figure32:GraphicalrepresentationofPython’sNaïveBayesmemoryconsumptioninboth

MareNostrum4IntelandCTE-ARMmachines 48

Figure33:GraphicalrepresentationofJavaandPython’sexecutiontimeinMareNostrum4CTE-ARM

machine 49

Figure34:GraphicalrepresentationofJavaandPython’sCPUefficiencyinMareNostrum4CTE-ARM

machine 50

Figure35:GraphicalrepresentationofJavaandPython’sLinearregressionmemoryconsumptionin

MareNostrum4CTE-ARMmachine 51

Figure36:GraphicalrepresentationofJavaandPython’sNaïveBayesmemoryconsumptionin

MareNostrum4CTE-ARMmachine 51

Introduction

Stateoftheart

Overtheyears,theAItechnologieshavetakenabiggerpartinourlives.FromlanguagerecognitiontothemostadvancedautonomouscarsarepossiblethankstotherecentresearchofnewandmoreefficientAIalgorithms.ArtificialIntelligenceisapopulartopicin21stcentury,itisoneoftheadvancedresearchfieldsofcomputersciences.TheworkofAIbeganformallysoonafterWorldWarII,andtheword”ArtificialIntelligence”wasputforwardbyStanfordprofessorJohnMcCarthyin1956onDartmouthworkshop.TheareaofAIresearchgoesfurtherstill,itaimsnotonlyexistsinthetheoriesbutalsotocreateentities,whichincludesasignificantvarietyofsubfield,scalingfromuniversal(studyingandfeeling)toparticular,suchasplayingchess,diagnosingdiseases,writingpoetry,drivingvehiclesandproofofmathematicaltheorems.WiththetechnologiesofAIevolvingswiftly,itscapabilitiesarebeingappliedinallaspectsofsociety.

SelectingtheappropriateprograminglanguageisanimportanttaskforAIconstruction.Therearediverseandgeneraloptionssuchaspython,C++andLISP.CurrentlyPythonisoneofthemostpopularprogramminglanguagesforAIdevelopersduetothesimplicityofitscodeandthepowerfulAIlibrariesdevelopedbythecommunitysuchasPandas,NumpyorScikit-learn.Nevertheless,JavaplaysanimportantroleinAIprogramming.Javaisthemostwidelyusedprogramminglanguageintheworld,itisobject-orientedandscalable,whichisnecessaryforAIprojects.GiganticJavacodebasesareexploitedbypublicandprivateorganizationsandsectors,andtheJavaVirtualMachineasacomputeenvironmentisheavilyrelied(Georges,etal.,2006).JavaVirtualMachineallowsdeveloperstobuildasingleapplicationversionthatcouldrunonallJava-enabledcomputingplatforms.Maintainability,portabilityandtransparencyarethethreemainadvantagesofJavaprogramminglanguage.

However,therearetwoshortcomingsofWekacomparedwithPython.First,Weka'spre-processingandresultoutputaremoredifficult.Althoughitisconvenientforbeginnerstoprocessdatawithalittlefilter,itiseasiertowriteprogramslikePythonwhenprocessinglargeamountsofdata(Caia,etal.,2005).Similarly,althoughtheresultscanberunoutbypressing"Start"intheclassification,itismoretroublesomeforWekatomaketheresultsleadtotheformatorthenextapplication.Second,thePythonpackageisbooming.AlthoughWekaalsohasalotofpackages,butacloserlookwillrevealthatmostofthemareoldandhavenotbeenupdated.TheWekasuitewritteninJavaisalsodifficulttorewriteandcompile.Incontrast,thedevelopmentofPythonisflourishing.MostoflateresearchcouldberepackagedintoPythonpackagesforpeopletodownloadanduse,andtherearecountlessdevelopersstudyingPython.

Previousstudieshaveproventhatingeneralpurposeprograms,javaistheleastmemoryefficientprogramminglanguage(Prechel,s.f.)althoughtheonesfoundaremorethan5yearsold.Ifresearchwasmadeofthearticlesand/orresearchpapersrelatedtotheroleofJavaintheAIworldwritteninthelast4years,onewouldfindthattherearealmostnone.

IntheARMenvironment,JavahasastretchrelationshipwiththeARMarchitectureasJavawas,untilrecentyears,themainprogramminglanguagesforAndroiddevelopersand,therefore,fortheARMarchitectureusedinthiskindofdeviceswiththeAndroidoperatingsystem.SincethemainobjectivebehindAndroiddevelopmentwastocreateaplatform-independentapplicationenvironmentthat

canrunoneverydevice,Javawastheperfectcandidatetobethemainprogramminglanguageasitalreadyhasthisquality.

However,therearenotmuchinformationabouttheperformanceofJavaandtheJVMintheservermarketfortheARMprocessors.ThankstotheinclusionofARMprocessorsinMACsystemsonNovember10,2020,somebenchmarkshavebeenmadetotesttheperformanceofthesesystemsalthoughnoneofthemtestspecificallyjava-basedapplications.TheonlybenchmarktestingtheperformanceofaJavaapplicationonbothARMandx86architecturefoundwhenthisprojectwasbeingmadeshowedthatARMversionconsumedmorememorybuthadingeneralabetterperformance(Voitylov,2021).

PythonhaveevenlesspresenceintheARMarchitectureenvironment.CurrentresearcharemadeonFPGAsandthefewresultsprovidedbythecommunitysuggestthatPythonhavehugepotentialforthiskindofenvironment(Schmidt,etal.,2017)withsomestatingthata30xperformancewasobtainedovertheexistingCcodebutthelackofimplementationofcertainlibrariesdonotallowresearcherstotestnewbenchmarks.

Proposal

AsmoreproductsandfeaturesaredevelopedbasedonAItechnologyitisimportanttohaveagoodbasefromwhichconstructthesystem’sstructure.Therefore,havingagoodprogramlanguagefromwhichdevelopasolutionisessential.Asitwasstatedbefore,Pythonisthego-toprogramlanguagenowadaysfordevelopingAisoftwareduetotheeasycodingandthatanyonecanquicklylearnitbutitisalsoimportanttoalsotakeincountotherexistingoptionsasabaseprogramlanguage,inthiscase,Java.

Physics,mathematicians,andengineersarethemainpeopledevelopingnewAIalgorithmsandsolutionsastheyaretheyhaveadeepcomprehensionofthebasesbehindtheAIalgorithmsandthestatisticalcomponentofthem.Forthem,Pythonisanappealinglanguageprogramfromwhichdeveloptheirsolutionsasitdoesn’trequirealotoftimetolearnitssyntaxandthefinalcodeproducedisshortingeneral.ThatiswhymanyAIlibrariessuchasNumPyofSklearnaredevelopedforPython.JavaontheotherhandhasmuchlessresearchbehindinregardtoAIlibrariesbuttheresomeofthemlikeWEKAthatarestillusedandsimplifiesthejoboftheprogrammerwithitsAPI.ThisisthereasonthatthisprojectalsoaimstoanalyzethecharacteristicsofbothlanguagesfromthepointofviewofitscodeandspecificallyforthedevelopmentofAIprograms.

Performanceisanotherkeyfactorwhencomparingprogramlanguages.BothJavaandPythonhavetheirowncharacteristicswhichintheendproducedifferentresultsontheirperformance(Destefanis,etal.,2016).BothJavaandPythonarehighlevelprogramminglanguagesandsoitishardorevennotpossibletoaccessthehardwaredirectlytooptimizecodesotheybothdependonitsvirtualmachineandthecodeimplementationtodetermineitsfinalperformance.Oneofthemostremarkableaspectisthatmostofthetimes,PythonisinterpretingbytecodeandexecutingitlocallywhileJavacompilestoan“IntermediateLanguage”andtheJavaVirtualMachinereadsthebytecodeandjust-in-timecompilesittomachinecode.This,togetherwiththefactthatJavaisJIT-Compiledenablesoptimizationstobemadeatruntime.ThisJIToptimizerwillseewhichpartsoftheapplicationarebeingexecutedalargenumberoftimesandwillthenmakeoptimizationstothosebitsofcode,byreplacingthemwithmoreefficientversions.ItisalsoimportanttokeepinmindthatJavaisstrongly-typedlanguagesotheoptimizercanmakemanymoreassumptionsaboutthecode.

WiththesepremisesitwouldbeinterestingtotesttheoptimizationsJavaprovidestohaveanobjectiveperspectiveofitsperformanceandinparticularforAIalgorithms.

ItisalsoimportanttotakeincountandanalyzeotherarchitecturessuchasARM,whichisincreasingitsuseintheserver,HPCandeveninpersonalcomputers.ARMisthemainprocessorarchitectureintheFUGAKUSupercomputer,thenumber1oftheTop500supercomputersintheworld(Anon.,s.f.).TheBarcelonaSupercomputingCentrealsoincludesanARMmachineandtheArm-BSCCentreofExcellencewhichaimtoincreasethecollaborationbetweentheBSCandARMforresearchpurposes.Therefore,thisisanexcellentopportunitytoalsotakeincountthearchitectureasafactorwhencomparingbothlanguages.

Objectives

TheaimofthisprojectistodetermineifJavastillhasaplaceintheAIworldasarelevantprograminglanguagebycomparingittothenowadaysmostpopularlanguageintheAIsoftwaredevelopment,Python.Also,thisthesistriestoprovideandobjectiveanalysisoftheARMarchitecturewithAIalgorithmsasthecurrentstateoftheartisquitelackinginthisregard.

TheultimategoaloftheresultsisthattheyshouldprovideafirstapproachoftheactualperformanceofJavaandPythonusinguptodatetechnologiesandbeapossiblestartingpointforfutureresearchregardingnewtechniquestoincreaseJavaperformanceforAIalgorithms.Moreover,theoutputcouldhighlightsomepossibleunknownorunclearareaswhereJavacouldprovetobeamoreappealingtoolforresearchers.

Theobjectivestackledinthestudyare:

EvaluatethecodingprocessfortheimplementationofmostrepresentativealgorithmsinJavaandPython.

Evaluatethetotallengthofcodeproducedintheimplementationofthealgorithms.

EvaluatetheperformanceoftheimplementedalgorithmsinJavaandPythoninamachinewithx86architecture.

EvaluatetheperformanceoftheimplementedalgorithmsinJavaandPythoninamachinewithARMarchitecture.

Evaluatetheimpactofthearchitecturebyanalyzingtheperformanceofthealgorithmsimplementedinthesameprograminglanguagebutinamachinewithdifferentarchitecture.

AnalyzethecurrentstateofJavaontheAIworldbasedonthepreviousevaluations.

Methodology

Thissectioncontainstheresearchandanalysismadeoverthedifferentelementsthatwillmakepartoftheprojectaswellasthemetricsandcharacteristicthatwillbeusedtopresentresults.

StudyoftheexistingandmostrepresentativealgorithmsintheAI

AIenvironmentcontainsmanydifferentareasofstudyandtherefor,astudyofthemostrepresentativealgorithmswasmade.Thealgorithmsselectedcanbeclassifiedintotwocategories:RegressionandClassification.

Regression

Regressionalgorithm’sroleistopredicttheoutputvaluesofacertainproblembasedoninputfeaturesfromthedatafedinthesystem.Inordertodoso,thealgorithmbuildsamodelbasedonthefeaturesofthetrainingdataandthenusingthemodeltopredictthevaluefornewdata.

Linearregression

Thelinearregressionalgorithmisoneofthemost-usedregressionalgorithmsinMachineLearning.Asignificantvariableoragroupofthemfromthedatasetischosentopredictanoutputvariable.Therearethreetypesofregressionalgorithms:SimpleLinearregression,MultilinearregressionandtheNon-linearRegression.InthisworktheMultilinearregressiontypewillbeusedasitismorerepresentativethantheNon-linearregression(Yan&Su,s.f.)andhasmorecomputationalcomplexitythantheLinearregression.

Multilinearregressionisusedtoestimatetherelationshipbetweentwoormoreindependentvariablesandonedependentvariable.Thealgorithmanalysesthedatafromatrainingsetandcreatesamodelwiththefollowingformula:

𝑦=𝛽0+𝑋1𝛽1+⋯+𝑋𝑛𝛽𝑛+𝜀

Where:

y=thepredictedvalueofthedependentvariable

𝛽0=they-intercept

𝑋1𝛽1=theregressioncoefficientofthefirstindependentvariable(a.k.a.theeffectthatincreasingthevalueoftheindependentvariablehasonthepredictedyvalue)

𝑋𝑛𝛽𝑛=theregressioncoefficientofthelastindependentvariable

ε=modelerror(a.k.a.howmuchvariationthereisinourestimateofy)

Classification

Classificationisdefinedastheprocessofrecognition,understanding,andgroupingofobjectsandideasintopre-setcategories.Classificationalgorithmsusedinmachinelearningutilizeinputtrainingdataforthepurposeofpredictingtheprobabilitythatthedatathatfollowswillfallintooneofthepredeterminedcategories.Fromtheexistingclassificationalgorithms,thefollowingoneswereanalysed:

Logisticregression

LogisticregressionisamathematicalmodelingapproachthatcanbeusedtodescribetherelationshipofseveralindependentvariablesX1,X2,...,Xktoadependentvariableand/orpredictitsvalue.Todoso,itusesalogisticfunction,whichdescribesthemathematicalformonwhichthelogisticmodelisbased.Thefactthatthelogisticfunctionf(z)rangesbetween0and1istheprimaryreasonthelogisticmodelissopopular.Themodelisdesignedtodescribeaprobability,whichisalwayssomenumberbetween0and1.

Figure1:Logisticfunctionandgraphicalinterpretation

SupportVectorMachine

SupportVectorMachine(SVM)isoneofthemostextensivelyusedsupervisedmachinelearningalgorithmsinthefieldoftextclassification.Givenasetofpointsof2typesinNdimensionalplace,SVMgeneratesa(N—1)dimensionalhyperplanetoseparatethosepointsinto2groups.Itcomputesthelinearseparationsurfacewithamaximummarginforagiventrainingset.Whenalinearseparationsurfacedoesnotexist,forexample,inthepresenceofnoisydata,SVMsalgorithmswithaslackvariableareappropriate.ThisClassifierattemptstopartitionthedataspacewiththeuseoflinearornon-lineardelineationsbetweenthedifferentclasses.Thebesthyperplanewouldbethe

onewiththelargestpositivevectorsandseparatingmostofthedatanodes.Thisisanextremelypowerfulclassificationmachinethatcanbeappliedtoawiderangeofdatanormalizationproblems.

Figure2:HyperplanegeneratedbyaSVM

DecisionTrees

Adecisiontreeisadecisionsupporttoolthatusesatree-likegraphormodelofdecisionsandtheirpossibleconsequences,includingchance-eventoutcomes,resourcecosts,andutility.InaDecisiontree,therearethreetypesofnodes,whicharetheRootNode,theDecisionNodeandtheLeaforTerminalNode.TheRootnoderepresentstheentirepopulationorsampleandthisfurthergetsdividedintotwoormorehomogeneoussets,theDecisionnodesareusedtomakeanydecisionandhavemultiplebranches,whereasTerminalnodesaretheoutputofthosedecisionsanddonotcontainanyfurtherbranches.

Figure3:DecisionTreerepresentingeachofthenodes

Inordertopredictaclasslabelforarecord,thealgorithmstartsfromtherootofthetree.Itcomparesthevaluesoftherootattributewiththerecord’sattribute,and,onthebasisofcomparison,itfollowsthebranchcorrespondingtothatvalueandjumptothenextnode.Morespecifically,thecompleteprocesscanbedividedinthefollowingsteps:

Beginthetreewiththerootnode,saysS,whichcontainsthecompletedataset.

FindthebestattributeinthedatasetusingAttributeSelectionMeasure(ASM).

DividetheSintosubsetsthatcontainspossiblevaluesforthebestattributes.

Generatethedecisiontreenode,whichcontainsthebestattribute.

Recursivelymakenewdecisiontreesusingthesubsetsofthedatasetcreatedinstep-3.Continuethisprocessuntilastageisreachedwhereyoucannotfurtherclassifythenodesandcalledthefinalnodeasaleafnode.

k-NearestNeighbor

k-NearestNeighborisamethodthatsimplysearchestheclosestobservationstotheoneyouaretryingtopredictandclassifiesthepointofinterestbasedonmostofthedataaroundit.Itisasupervisedalgorithmsothetrainingdatasetislabeled,withtheclassorexpectedresultgiven"arow"ofdata.Itisalsoinstancebasedsoitdoesnotexplicitlylearnamodel(suchasLogisticRegressionordecisiontrees).Insteaditmemorizesthetraininginstancesthatareusedasa“knowledgebase”forthepredictionphase.

Figure4:GraphicalexampleofaKNNvotingprocess

NaïveBayes

ANaïveBayesclassifierisaprobabilisticclassifierbasedonBayestheorem.Itisaconditionalprobabilitymodel.Themodeliscallednaiveasitoperatesontheassumptionthatalltheinputdatavaluesareunrelatedtoeachother.Whilethiscannottakeplaceintherealworld,thissimple

algorithmcanbeappliedtoamultitudeofnormalizeddataflowstopredictresultswithagreatdegreeofaccuracy.

Thereare3maintypesofNaiveBayesalgorithms:

Gaussian:Itisusedinclassificationanditassumesthatfeaturesfollowanormaldistribution.

Multinomial:Thisoneisusedfordiscretecounts.

Bernoulliorbinomial:Thebinomialmodelisusefulifthefeaturevectorsarebinary.

Figure5:NaïveBayesrepresentationwherethealgorithmfindscommonfeatures.

Randomforest

TheRandomForestalgorithmconsistsofalargenumberofindividualdecisiontreesthatoperateasanensemble.Thealgorithmestablishestheoutcomebasedonthepredictionsofthedecisiontrees.Eachindividualtreeintherandomforestspitsoutaclasspredictionandtheclasswiththemostvotesbecomethemodelprediction.

Figure6:Randomforestcomposedby9decisiontreesvotingtheclassresult.

Arandomforesteradicatesthelimitationsofadecisiontreealgorithm.Itreducestheoverfittingofdatasetsandincreasesprecision.

StudyoftheavailableHardwaresystems

BSChasprovidedtwodifferentaccountsinordertofulfillthisproject.

AnaccountforMarensotrum4withanx86architecture.Eachcomputingmodulehasthefollowingcharacteristics:

2xIntelXeonPlatinum816024Cat2.1GHz

216nodeswith12x32GBDDR4-2667DIMMS(8GB/core)

3240nodeswith12x8GBDDR4-2667DIMMS(2GB/core)

Interconnectionnetworks:

100GbIntelOmni-PathFull-FatTree

10GbEthernet

OperatingSystem:SUSELinuxEnterpriseServer12SP2AnaccountfortheMareNostrum4CTEARMmachine

A64FXCPU(1Armv8.2-A+SVEchip)@2.20GHz(groupingthecoresin4CMG-CoreMemorygroup-with12cores/CMGandanadditionalassistantcoreperCMGfortheOperatingsystem,addingatotalof48cores+4system-corespernode.TheARMv8.2-AcoreshaveavailabletheScalableVectorExtension(SVE)SIMDinstructionsetupto512-bitvectorimplementation.

32GBofmainmemoryHBM2

TofuDnetwork

SinglePortInfinibandEDR

Theoperatingsystemis,RedHatEnterpriseLinuxServer8.1

StudyofJavaandPythonprograminglanguages

Java

Javaisahigh-level,object-oriented,general-purposeprogramminglanguagewidelyusedinpersonalcomputers,datacenters,gameconsoles,supercomputers,mobilephonesandtheInternetapplications.Itwasoriginallydevelopedin1991byJamesGosling,aCanadiancomputerscientist,atwhatwasthenSunMicrosystems,intheU.S.stateofCalifornia.Today,afterdecadesofeffect,Javahasbeendevelopedintoafullyfunctional,multipurpose,andpowerfullanguagesuitableforbothindividualandenterpriseusers.

Figure7:Javaarchitecturestructure

ThemaincomponentsofJavaare:TheJavaVirtualMachine(JVM),theJavaRuntimeEnvironment(JRE)andtheJavaDevelopmentKit(JDK).

TheJavaVirtualMachine(JVM)istheruntimeengineoftheJavaPlatform,whichallowsanyprogramwritteninJavaorotherlanguagecompiledintoJavabytecodetorunonanycomputerthathasanativeJVM.Itprovidesthefunctionalityofimportantsubsystemssuchasgarbagecollection,memorymanagementandsecurity.JVMisplatform-independentanditcanbecustomizedandconfiguredusingaVirtualinterfaceitprovideswhichisnotmachine-dependentandisalsoindependentoftheoperatingsystem.

Figure8:JavaVirtualMachinecomponents

TheJavavirtualmachineThemainsubsystemsare:

ClassLoader:ThisisthesubsystemofJVMusedtoloadclassfiles.Wheneverauserrunajavaprogram,classloaderloadsitfirst.

JVMmemorysubsystem:T