Lecture5-Threads - University of Notre Dame › ~cpoellab › teaching › cse30341 › Lecture5...Lecture Overview: Threads • Overview • Mul,core Programming • Mul,threading

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CSE30341Opera,ngSystemPrinciples

Lecture5–Processes/Threads

CSE30341–Opera,ngSystemPrinciples 2

Recap

•  Processes– Whatisaprocess?– Whatisinaprocesscontrolblock?–  Contraststack,heap,data,text.– Whatareprocessstates?– WhichqueuesareusedinanOS?– Whatdoestheschedulerdo?– Whatisacontextswitch?– Whatistheproducer/consumerproblem?– WhatisIPC?

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LectureOverview:Threads

•  Overview•  Mul,coreProgramming•  Mul,threadingModels•  ThreadLibraries•  ImplicitThreading•  ThreadingIssues•  Opera,ngSystemExamples


Defini,on

•  Process:groupresourcestogether•  Thread:en,tyscheduledforexecu,oninaprocess

•  “Singlesequen?alstreamofinstruc?onswithinaprocess”

•  “Lightweightprocess”

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ThreadofExecu,on

registers

code data files

stack registers registers registers

code data files

stackstackstack

thread thread

single-threaded process multithreaded process


Threadvs.Process

•  Threadshavetheirown:– ThreadID(TID)(comparetoPID)– Programcounter(PC)– Registerset– Stack

•  Threadscommonlyshare:– Codesec,on(text)– Datasec,on– Resources(files,signals,etc.)

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WhyThreads?•  Enablemul?-taskingwithinanapp–  Updatedisplay–  Fetchdata–  Spellchecking–  Answeranetworkrequest

•  Reducedcost(“lightweight”process)–  Processesareheavytocreate–  IPCforthreadscheaper/easierthanprocesses

•  Can“simplify”code&increaseefficiency•  Kernelsaregenerallymul,threaded(differentthreadsprovidedifferentOSservices)


Mul,-ThreadedServer

client

(1) request(2) create new

thread to servicethe request

(3) resume listeningfor additional

client requests

server thread

(threadpool)

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Benefits

•  Responsiveness–mayallowcon,nuedexecu,onifpartofprocessisblocked,especiallyimportantforuserinterfaces

•  ResourceSharing–threadsshareresourcesofprocess,easierthansharedmemoryormessagepassing

•  Economy–cheaperthanprocesscrea,on,threadswitchingloweroverheadthancontextswitching

•  Scalability–processcantakeadvantageofmul,processorarchitectures


Mul,coreSystems

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Mul,coreProgramming

•  Mul?coresystemspuengpressureonprogrammers;challengesinclude:–  Dividingac?vi?es(whichtaskstoparallelize)–  Balance(if/howtoparallelizetasks)–  DataspliNng(howtodividedata)–  Datadependency(threadsynchroniza,on)–  Tes?nganddebugging(howtotestdifferentexecu,onpaths)

•  Parallelismimpliesasystemcanperformmorethanonetasksimultaneously

•  Concurrencysupportsmorethanonetaskmakingprogress–  Singleprocessor/core,schedulerprovidingconcurrency


Concurrencyvs.Parallelism

■  Concurrent execution on single-core system

■  Parallelism on a multi-core system

T1 T2 T3 T4 T1 T2 T3 T4 T1single core

time

…

T1 T3 T1 T3 T1core 1

T2 T4 T2 T4 T2core 2

time

…

…

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Mul,coreProgramming

•  Typesofparallelism– Dataparallelism–distributessubsetsofthesamedataacrossmul,plecores,sameopera,ononeach

–  Taskparallelism–distribu,ngthreadsacrosscores,eachthreadperforminguniqueopera,on

•  As#ofthreadsgrows,sodoesarchitecturalsupportforthreading(“hyperthreading”)–  CPUshavecoresaswellashardwarethreads–  ConsiderOracleSPARCT4with8coresand8hardwarethreadspercore


Datavs.TaskParallelism

•  Countnumberof,meseachcharacterinalphabetoccurs

•  DataParallelism– Thread1doespage1-100– Thread2doespage100-200

•  TaskParallelism– Thread1doesleiersA-F,allpages– Thread2doesleiersG-L,allpages

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SingleandMul,threadedProcesses

registers

code data files

stack registers registers registers

code data files

stackstackstack

thread thread

single-threaded process multithreaded process


UserThreadsandKernelThreads•  Userthreads-managementdonebyuser-levelthreadslibrary•  Threeprimarythreadlibraries:

–  POSIXPthreads–  Win32threads–  Javathreads

•  Kernelthreads-SupportedbytheKernel,“schedulableen?ty”•  Examples–virtuallyallgeneralpurposeopera,ngsystems,

including:–  Windows–  Solaris–  Linux–  Tru64UNIX–  MacOSX

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Mul,threadingModels

•  Many-to-One

•  One-to-One

•  Many-to-Many


Many-to-One•  Manyuser-levelthreadsmappedto

singlekernelthread•  Onethreadblockingcausesallto

block•  Mul,plethreadsmaynotrunin

parallelonmul,coresystembecauseonlyonemaybeinkernelata,me

•  Fewsystemscurrentlyusethismodel

•  Examples:–  SolarisGreenThreads–  GNUPortableThreads

user thread

kernel threadk

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One-to-One

•  Eachuser-levelthreadmapstokernelthread•  Crea,ngauser-levelthreadcreatesakernelthread•  Moreconcurrencythanmany-to-one•  Numberofthreadsperprocesssome,mesrestrictedduetooverhead

•  Examples– WindowsNT/XP/2000–  Linux–  Solaris9andlater

user thread

kernel threadkkkk


Many-to-ManyModel•  Allowsmanyuserlevel

threadstobemappedtomanykernelthreads

•  Allowstheopera,ngsystemtocreateasufficientnumberofkernelthreads

•  Solarispriortoversion9

•  WindowsNT/2000withtheThreadFiberpackage

user thread

kernel threadkkk

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Two-levelModel

•  SimilartoM:M,exceptthatitallowsauserthreadtobeboundtokernelthread

•  Examples–  IRIX– HP-UX– Tru64UNIX– Solaris8andearlier

user thread

kernel threadkkk k


ThreadLibraries

•  ThreadlibraryprovidesprogrammerwithAPIforcrea,ngandmanagingthreads

•  Twoprimarywaysofimplemen,ng– Libraryen,relyinuserspace– Kernel-levellibrarysupportedbytheOS

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Pthreads

•  Maybeprovidedeitherasuser-levelorkernel-level

•  APOSIXstandard(IEEE1003.1c)APIforthreadcrea,onandsynchroniza,on

•  Specifica7on,notimplementa7on

•  APIspecifiesbehaviorofthethreadlibrary,implementa,onisuptodevelopmentofthelibrary

•  CommoninUNIXopera,ngsystems(Solaris,Linux,MacOSX)


PthreadsExample

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PthreadsExample(Cont.)


PthreadsCodeforJoining10Threads

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ImplicitThreading•  Growinginpopularityasnumbersofthreadsincrease,programcorrectnessmoredifficultwithexplicitthreads

•  Crea,onandmanagementofthreadsdonebycompilersandrun-,melibrariesratherthanprogrammers

•  Examples:–  ThreadPools–  OpenMP–  GrandCentralDispatch– MicrosooThreadingBuildingBlocks(TBB)


ThreadPools•  Createanumberofthreadsinapoolwheretheyawaitwork•  Advantages:

–  Usuallyslightlyfastertoservicearequestwithanexis,ngthreadthancreateanewthread

–  Allowsthenumberofthreadsintheapplica,on(s)tobeboundtothesizeofthepool–  Separa,ngtasktobeperformedfrommechanicsofcrea,ngtaskallowsdifferent

strategiesforrunningtask•  i.e.,taskscouldbescheduledtorunperiodically

•  WindowsAPI:ThreadPool.QueueUserWorkItem(newWaitCallback(ThreadProc));…sta,cvoidThreadProc(Objectstateinfo){…}

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OpenMP•  Setofcompilerdirec,vesandanAPI

forC,C++,FORTRAN•  Providessupportforparallel

programminginshared-memoryenvironments

•  Iden,fiesparallelregions–blocksofcodethatcanruninparallel

#pragma omp parallel Createasmanythreadsastherearecores

#pragma omp parallel for for(i=0;i<N;i++) {

c[i] = a[i] + b[i]; } Runforloopinparallel


GrandCentralDispatch

•  AppletechnologyforMacOSXandiOSopera,ngsystems•  ExtensionstoC,C++languages,API,andrun-,melibrary•  Allowsiden,fica,onofparallelsec,ons•  Managesmostofthedetailsofthreading•  Blockisin“^{}”-ˆ{ printf("I am a block"); } •  Blocksplacedindispatchqueue

–  Assignedtoavailablethreadinthreadpoolwhenremovedfromqueue•  Twotypesofdispatchqueues:

–  serial–blocksremovedinFIFOorder,queueisperprocess,calledmainqueue•  Programmerscancreateaddi,onalserialqueueswithinprogram

–  concurrent–removedinFIFOorderbutseveralmayberemovedata,me•  Threesystemwidequeueswithpriori,eslow,default,high

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ThreadingIssues:Seman,csoffork()andexec()

•  Doesfork()duplicateonlythecallingthreadorallthreads?– Whenisduplica,ngallthreadsareallybadidea?– SomeOSeshavetwoversionsoffork– POSIX:onlythecallingthread

•  Exec()usuallyworksasnormal–replacetherunningprocessincludingallthreads


ThreadingIssues:SignalHandling

•  SignalsareusedinUNIXsystemstono,fyaprocessthatapar,culareventhasoccurred.

•  Asignalhandlerisusedtoprocesssignals1.  Signalisgeneratedbypar,cularevent2.  Signalisdeliveredtoaprocess3.  Signalishandledbyoneoftwosignalhandlers:

1.  default2.  user-defined

•  Everysignalhasdefaulthandlerthatkernelrunswhenhandlingsignal–  User-definedsignalhandlercanoverridedefault–  Forsingle-threaded,signaldeliveredtoprocess

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ThreadingIssues:ThreadCancella,on

•  Terminating a thread before it has finished•  Thread to be canceled is target thread•  Two general approaches:

–  Asynchronous cancellation terminates the target thread immediately

–  Deferred cancellation allows the target thread to periodically check if it should be cancelled

•  Pthread code to create and cancel a thread:


ThreadCancella,on(Cont.)

•  Invokingthreadcancella,onrequestscancella,on,butactualcancella,ondependsonthreadstate–  pthread_setcancelstate() -> enable/disable –  Pthread_setcanceltype():

•  Ifthreadhascancella,ondisabled,cancella,onremainspendingun,lthreadenablesit

•  Defaulttypeisdeferred–  Cancella,ononlyoccurswhenthreadreachescancella?onpoint

•  pthread_testcancel() •  Asynchronous:terminateimmediately

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Thread-LocalStorage

•  Thread-localstorage(TLS)allowseachthreadtohaveitsowncopyofdata

•  Usefulwhenyoudonothavecontroloverthethreadcrea,onprocess(i.e.,whenusingathreadpool)

•  Differentfromlocalvariables–  Localvariablesvisibleonlyduringsinglefunc,oninvoca,on–  TLSvisibleacrossfunc,oninvoca,ons

•  Similartostaticdata–  TLSisuniquetoeachthread


LinuxThreads

■  Linuxreferstothemastasksratherthanthreads■  Threadcrea,onisdonethroughclone() systemcall■  clone() allowsachildtasktosharetheaddressspaceofthe

parenttask(process)●  Flagscontrolbehavior

flag meaning

CLONE_FS

CLONE_VM

CLONE_SIGHAND

CLONE_FILES

File-system information is shared.

The same memory space is shared.

Signal handlers are shared.

The set of open files is shared.

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Recap

•  Whatisathread?Whywouldoneuseathread?

•  Howdoesathreaddifferfromaprocess?•  Whatarepthreads?•  Whatisakernelthread?•  Howdoestaskparallelismdifferfromdataparallelism?

Documents

Lecture5-Threads - University of Notre Dame › ~cpoellab › teaching › cse30341 › Lecture5...Lecture Overview: Threads • Overview • Mul,core Programming • Mul,threading