Visit to download the full and correct content document: https://ebooksecure.com/download/data-prefetching-techniques-in-computer-systems -ebook-pdf/
DataPrefetchingTechniques inComputerSystems
This page intentionally left blank
SchoolofComputerScience, InstituteforResearchinFundamentalSciences(IPM), Tehran,Iran
HAMIDSARBAZI-AZAD
SharifUniversityofTechnology,and InstituteforResearchinFundamentalSciences(IPM), Tehran,Iran
AcademicPressisanimprintofElsevier
50HampshireStreet,5thFloor,Cambridge,MA02139,UnitedStates 525BStreet,Suite1650,SanDiego,CA92101,UnitedStates
TheBoulevard,LangfordLane,Kidlington,OxfordOX51GB,UnitedKingdom 125LondonWall,London,EC2Y5AS,UnitedKingdom
Firstedition2022
Copyright©2022ElsevierInc.Allrightsreserved.
Nopartofthispublicationmaybereproducedortransmittedinanyformorbyanymeans,electronic ormechanical,includingphotocopying,recording,oranyinformationstorageandretrievalsystem, withoutpermissioninwritingfromthepublisher.Detailsonhowtoseekpermission,further informationaboutthePublisher’spermissionspoliciesandourarrangementswithorganizationssuch astheCopyrightClearanceCenterandtheCopyrightLicensingAgency,canbefoundatourwebsite: www.elsevier.com/permissions
Thisbookandtheindividualcontributionscontainedinitareprotectedundercopyrightbythe Publisher (otherthanasmaybenotedherein).
Notices
Knowledgeandbestpracticeinthisfieldareconstantlychanging.Asnewresearchandexperience broadenourunderstanding,changesinresearchmethods,professionalpractices,ormedical treatmentmaybecomenecessary.
Practitionersandresearchersmustalwaysrelyontheirownexperienceandknowledgeinevaluating andusinganyinformation,methods,compounds,orexperimentsdescribedherein.Inusingsuch informationormethodstheyshouldbemindfuloftheirownsafetyandthesafetyofothers,including partiesforwhomtheyhaveaprofessionalresponsibility.
Tothefullestextentofthelaw,neitherthePublishernortheauthors,contributors,oreditors,assume anyliabilityforanyinjuryand/ordamagetopersonsorpropertyasamatterofproductsliability, negligenceorotherwise,orfromanyuseoroperationofanymethods,products,instructions,orideas containedinthematerialherein.
ISBN:978-0-323-85119-0 ISSN:0065-2458
ForinformationonallAcademicPresspublications visitourwebsiteat https://www.elsevier.com/books-and-journals
Publisher: Zoe Kruze
DevelopmentalEditor: CindyAngelitaGardose
ProductionProjectManager: JamesSelvam
CoverDesigner: GregHarris
TypesetbySTRAIVE,India
4.Beyondspatialortemporalprefetching43
PejmanLotfi-KamranandHamidSarbazi-Azad
1. Introduction43
2. Spatiotemporalmemorystreaming(STeMS)44
3. Summary50 References50
Abouttheauthors52
5.State-of-the-artdataprefetchers55
MehranShakerinava,FatemehGolshan,AliAnsari,PejmanLotfi-Kamran, andHamidSarbazi-Azad
1. DOMINO temporaldataprefetcher55
2. BINGO spatialdataprefetcher57
3. Multi-lookaheadoffsetprefetcher59
4. Runaheadmetadata62
5. Summary64 References64
Abouttheauthors65
6.Evaluationofdataprefetchers69
MehranShakerinava,FatemehGolshan,AliAnsari,PejmanLotfi-Kamran, andHamidSarbazi-Azad
1. Introduction70
2. Spatialprefetching70
3. Temporalprefetching73
4. Spatio-temporalprefetching77
5. Offsetprefetching82
6. Multi-degreeprefetchingwithpairwise-correlatingprefetchers83
7. Summary85 References85
Abouttheauthors87
AliAnsari
SharifUniversityofTechnology,Tehran,Iran
FatemehGolshan
SharifUniversityofTechnology,Tehran,Iran
PejmanLotfi-Kamran
SchoolofComputerScience,InstituteforResearchinFundamentalSciences(IPM),Tehran, Iran
HamidSarbazi-Azad
SharifUniversityofTechnology,andInstituteforResearchinFundamentalSciences(IPM), Tehran,Iran
MehranShakerinava
SharifUniversityofTechnology,Tehran,Iran
This page intentionally left blank
AdvancesinComputers,theoldestseriestothechronicleoftherapid evolutionofcomputing,annuallypublishesseveralvolumes,eachcomprisingtypicallyfourtoeightchapters,describingnewfindingsanddevelopmentsinthetheoryandapplicationsofcomputing.
This125thvolumeisathematiconeentitled“DataPrefetching TechniquesinComputerSystems”inspiredbyrecentworkindataprefetching techniquesproposedforadvancedprocessorsandusedincomputersystems. Thisvolumecomprisessixchapters.
Whileprocessorperformancehasbeenrapidlyimprovingduringthepast decades,themaingoalofdesignerswastoincreasethecapacityofthemain memoryratherthanitsspeed.Therefore,thereemergedaconsiderablegap, calledmemorywall,betweenthespeedoftheprocessorandmainmemory.
Totacklethememorywallproblem,ahigh-speedlow-capacitybuffercalled cachememoryisusedbetweentheprocessorandthemainmemorytoremedytheirperformancegap.Whenaprocessorrequiresapieceofdata,ifitis inthecache(calledahit),itcanbedeliveredtotheprocessorveryquickly. Otherwise,whichiscalledamiss,thedatahastobeaccessedinthemain memory.Insuchcases,cachememoryisnotusefulandevenincreases theaccesslatencyaswefirstneedtosearchthecacheandthenaccessthe mainmemory.Asthemainmemorylatencyisconsiderableforaprocessor, loweringtherequestsservedbythememoryisdesirable.Dataprefetchingis awaytoimprovetheeffectivenessofcachesandreducestheperformance gapbetweentheprocessorandthemainmemorybyloweringthestall cyclesseenbytheprocessorwhenservingmainmemoryrequests.Adata prefetcherpredictsthatapieceofdataisgoingtobeusedbytheprocessor. Incasethepredicteddataisnotinthecache,thedataprefetcherbringsitinto thecacheinadvance.
Themaintaskofadataprefetcheristopredictfuturememoryaccesses. Fortunately,thememoryaccessesofaprocessorusuallyareregularand followpatterns.Bylearningaccesspatterns,adataprefetchercanmake correctpredictions.Basedondifferentaccesspatternsthatadataprefetcher canlearn,differenttypesofdataprefetchersmaybedesignedandused.This bookintroducesseveralimportanttypesofdataprefetchersanddiscusses howtheyperformdataprefetching,whytheyworkasintended,whenthey areeffective,andwhentheyarenot.
Inthisbook,wefirstdiscussthebasicdefinitionsandfundamentalconceptsofdataprefetchingandthenstudyclassichardwaredataprefetchers,as wellasrecentadvanceddataprefetchers.Wedescribetheoperationsofevery dataprefetcherindetailandshedlightonitsdesigntrade-offs.
Thesixchaptersofthebookareorganizedasfollows: Chapter1,an introductiontodataprefetchingisgiven.Differentprefetchersaredefined andmanyrecentprefetchersarecategorizedaswell. Chapter2 discusses spatialprefetchingtechniquesandintroducesSMSandVLDPtechniques indetail. Chapter3 discussestemporaldataprefetchingandexplains STMSandISB.Techniquesthatcannotbeclassifiedunderonlyspatial andtemporaldataprefetchersareconsideredin Chapter4. Chapter5 introducessomerecentlyproposeddataprefetchers.Finally, Chapter6 evaluates theeffectivenessofdifferentprefetchersundervariousworkingconditions.
Wehopethatthereaderswillfindthisvolumeinterestinganduseful forteaching,research,anddesigningdataprefetchers.Wewelcomeany feedbackandsuggestionsrelatedtothebook.
PEJMAN LOTFI-KAMRAN
SchoolofComputerScience
InstituteforResearchinFundamentalSciences(IPM)
Tehran,Iran
HAMID SARBAZI-AZAD
SharifUniversityofTechnology, and
InstituteforResearchinFundamentalSciences(IPM), Tehran,Iran
AMT accessmaptable
BOP best-offsetprefetcher
CPU centralprocessingunit
DHB deltahistorybuffer
DPC dataprefetchingchampionship
DPT deltapredictiontable
DRAM dynamicrandomaccessmemory
DSS decisionsupportsystem
EIT enhancedindextable
GPU graphicsprocessingunit
IPC instructionspercycle
ISB irregularstreambuffer
L1D level1data
LLC last-levelcache
LRU leastrecentlyused
MLOP multi-lookaheadoffsetprefetcher
NVM non-volatilememory
OLTP onlinetransactionprocessing
OoO outoforder
OPT offsetpredictiontable
OS operatingsystem
PAS physicaladdressspace
PC programcounter
PHT patternhistorytable
PSAM physical-to-structuraladdressmapping
PST patternssequencetable
RMD runaheadmetadata
RMOB regionmissorderbuffer
ROB reorderbuffer
RRT recentrequeststable
SAS structuraladdressspace
SMS spatialmemorystreaming
SP sandboxprefetcher
SPAM structural-to-physicaladdressmapping
SPU sandboxprefetchunit
STeMS spatio-temporalmemorystreaming
STMS sampledtemporalmemorystreaming
TLB translationlookasidebuffer
TPC transactionprocessingperformancecouncil
VLDP variablelengthdeltaprefetcher
This page intentionally left blank
’slawtosignificantly improvethespeedofprocessors.Unlikeprocessors,withthemainmemory,thefocus ofimprovementhasbeencapacityandnotaccesstime.Hence,thereisalargegap betweenthespeedofprocessorsandmainmemory.Tohidethegap,ahierarchyof cacheshasbeenusedbetweenaprocessorandmainmemory.Whilecachesproved tobequiteeffective,theireffectivenessdirectlydependsonhowmanytimesa requestedpieceofdatacanbefoundinthecache.Duetothecomplexityofdataaccess patterns,onmanyoccasions,therequestedpieceofdatacannotbefoundinthecache hierarchy,whichexposeslargedelaystoprocessorsandsignificantlydegradestheirperformance.Dataprefetchingisthescienceandartofpredicting,inadvance,whatpieces ofdataaprocessorneedsandbringingthemintothecachebeforetheprocessor requeststhem.Inthischapter,wejustifytheimportanceofdataprefetching,lookat ataxonomyofdataprefetching,andbrieflydiscusssomesimplewaystodoprefetching.
AdvancesinComputers,Volume125Copyright # 2022ElsevierInc.
ISSN0065-2458Allrightsreserved.
https://doi.org/10.1016/bs.adcom.2021.11.003