advanced analy9cs & graph ... - the oracle big data€¦ · • oracle advanced analy9cs in...

OracleBIWASummit2017

AdvancedAnaly9cs&Graph:TransparentlytakingadvantageofHardwareinnova9onsintheCloud

BradCarlile,Sr.Direc9onSolu9onsArchitectureEngineeringOracleAddi$onalinfoonProofpoints:hLp://blogs.oracle.com/bestperf

Copyright©2017,Oracleand/oritsaffiliates.Allrightsreserved.| OracleBIWASummit2017

ModernAnaly9csisaboutYourData&ExternalData

• Analy9csisnowyourdataPLUSexternaldata– OracleDatabaseIn-MemoryFastestonSPARC

– Con9nuouslyanalyzingsamedataindifferentways–

Fastestifstoredatain-memory

• Manywaystoaccessexternaldata

– Socialnetworks,publicdata:sen9ments,weather,

traffic,events,ra9ngs,trends,IoTsensors,behaviors,…

– LotsofETL/filteringneededtofindusefuldata– BigDataSQL

Confiden9al–OracleInternal/Restricted/HighlyRestricted2

Be#erinforma,onwhenyoucananalyzeallrelevantdata

Weather

Tweets

ExternalData

Order

Customer

YourData

SensorsSocial


OracleDatabaseOp9mizedforOLTPTransac9ons

• OLTPDatabasewereop9mizedfortransac9onsand/orevents

– Eachtransac9on/eventhasmanyfeatures

– Demographics,social,geography,behaviors,…

• Asingletransac,on’sfeaturesarestoredtogetherinrowformemory– Why?BeLercomputa9onalandmemoryefficiencywhenworkingatransac9on

• …butAnalysiswantstolookatspecificfeaturesacrossmanytransac$ons


SQLhandlesdatamanipula,onandmanagementofinforma,on

Order F1 F2 … F50

Customer F1 F2 … F100


OracleDatabasealsoOp9mizedforAnaly9cQueries

• ColumnStoreisop9malforAnaly9cs– Analy9csLooksacrosstransac9onsatspecificcolumns

– DatabasecolumnsareMachineLearningfeatures

• Columnarfastercomputa,onalandbe#ermemoryefficiencywhenanalyzingfeatures

• Canalsoexploitdatacharacteris9cs>20xbenefit•  Storageop9miza9ons–datarepeatedlyanalyzed

– Dic9onary+layeredcompressionmeans10’sTBofdatafit

–  intoTB’sofserverstorage•  Processingop9miza9ons

– VectorProcessingofDic9onary-encodedColumns,BloomFilterjoinprocessing,operatorpushdown,metadataop9miza9ons

4

SQLop,mizedforanaly,cqueries

100’sofFeatures

BigFeatureRevolu3onNumberoffeaturescollectedforeachTransac$on/eventareexploding:10’sto100’s


TheBasicAnaly9csFlow

• Datacancomefrommanysources

– Databases,NoSQL,csv,feeds…

• Weneedtoprepareit

– DataMungingofallsorts!

• Analyzethedata– Findthe“rightway”toanalyzeit

• ML,Graph,SQL…


Alotof,mespentinDataMunging

DataMunging

RESULTS

Analy,cs

NoSQL,Search,…

StreamingKaTa,Storm…

Databases


Con9nuousAnaly9csCycleResultsOmenEnrichTransac9onsAnaly,csismorethanonepipelinestream

• Con9nuousitera9onsaroundthedataanaly9cswheel

– Save,catalog,andre-useallthings:data,SQL,code,andanaly9cs

•  In-memoryadvantagesateachstage

– SPARC’sDAX&leadingbandwidthiskey

• Manysourcesofdata

– Internalproprietary,publicdata,externalstreaming,archives

OracleConfiden9al–HighlyRestricted6

StreamingKaTa,Storm,…

EnhanceTransac3ons

Reports

NoSQL

ETL(SQL)

ETL(SQL)

In-Memory

ML&Graph(FP)

Results(SQL)

FeatureExtract,Generate&Transform

(SQL)

Databases


0.8x

1.0x

1.2x

1.4x

1.6x

1.8x

2.0x

2012 2013 2014 2015 2016

CorePerform

ance

vs.x86E5v2

x86:flatforfourgenera,ons

SPARC’sInnova9onsareCon9nuallyIncreasingCorePerformance

•  SPARChasfastercoresforDatabase,Java,Apps,...•  SPARChasmorecoresperchipwhichalsodrivesefficiency

7

x86percoreperformanceisstalled,Intelhasrunoutofsteam

SPARCM7 S7

SPARCS7

SPARC:DB

&Java

S7

"percore=(serverperformance)/(servercorecount)"


0.8x

1.0x

1.2x

1.4x

1.6x

1.8x

2.0x

2012 2013 2014 2015 2016

CorePerform

ance

vs.x86E5v2

JavaOLTPMemGB/s

SPARCS7is1.6xto2.1xfasterthanx86

•  It’smoreimportanthowyouusetransistors,thanthenumbertransistorsyoumake(Moore’sLaw)

8

x86percoreperformanceisstalled,Intelhasrunoutofsteam

x86indashedlines

SPARCins

olidlines

E5E5v2

E5v3 E5v4

SPARCT5

SPARCM7S7

SPARCS7

"percore=(serverperformance)/(servercorecount)""percore=(serverperformance)/(servercorecount)"


ML-MachineLearning• Automa3callysimingthroughmanyfeatures&data

– tofindpreviouslyhiddenpaLerns,– todiscovervaluablenewinsightsandmakepredic9ons

•  Examples:

• Idmostimportantfactors(ANributeImportance)• Predictcustomerbehaviors(Classifica$on)

• Predictores9mateavalue(Regression)

• Segmentapopula9on(Clustering)

• Findfraudulentor“rareevents”(AnomalyDetec$on)

• Determineco-occurringitemsina“baskets”(Associa$ons)

• Findprofilesoftargetedpeopleoritems(DecisionTrees)

9

A1A2A3A4A5


MachineLearning(ML):Predic9onvs.TrainingPredic9oncri9calforReal-9mescoringenginesSPARC3x-5xfasteratPredic,on&2xfasteratTraining

Predic9on/Scoring

Results

Trainononeserver,thenmovemodeltopredic$onserver(ex:StubHub)OXen70:30splitto

validatemodelbeforemovingmodelto

produc$on

Model

Every4weeksTraining/Learning

Real-TimeScoring:LargeProduc9onDatatoEvaluate

*periodicallyupdatestomodels:quarterly,monthly,weekly,nightly

10

MLScore/Predic,on

MLLearn/Train

%ofac9vity MostData *periodic

SPARCcore

advantagevsx86

3xto5x

percoreUpto2x

percore

Predic$onrequireshighly-efficientserverdesign

Train70%Valid30%


OracleDatabaseAdvancedAnaly9csOp9onMachineLearningonSPARC

•  OracleAdvancedAnaly9csinOracleDatabase12.2–  SPARCM7fasterpercoreontraining64-bitfloa9ngpointintensive

–  In-memory640millionrecords,AirlineOn-9medataset

MLtrainingSPARCM7upto2.0xfasterpercorethanx86

SGD(Stochas9cGradientDescent),IPM(InteriorPointMethod)

Training:Crea3ngModelfromdata

A#ri-butes

X5-44-chip

T7-44-chip

SPARCperchipAdvantage

SPARCpercoreAdvantage

Supervised

SVMIPMSolver 900 1442s 404s 3.6x 2.0xGLMClassifica9on 900 331s 154s 2.1x 1.2xSVMSGDSolver 9000 157s 84s 1.9x 1.1xGLMRegression 900 78s 55s 1.4x 0.8x

ClusterModelExpecta9onMaximiza9on 9000 763s 455s 1.7x 0.9xK-Means 9000 232s 161s 1.4x 0.8x

11

Predict Train


OracleDatabaseAdvancedAnaly9csOp9onMachineLearningonSPARC

•  OracleAdvancedAnaly9csinOracleDatabase12.2–  SPARCM7muchfasterpercoreonscoringbandwidth-intensive

–  In-memory1Billionrecords,AirlineOn-9medataset

MLpredic,onSPARC3.0x-4.8xfasterpercorethanx86

Predic,onUsingmodelon1Brecords

A#ri-butes

X5-44-chip

T7-44-chip

SPARCperchipAdvantage


Supervised

SVMIPMSolver 900 206s 24s 8.6x 4.8xGLMRegression 900 166s 25s 6.6x 3.7xGLMClassifica9on 900 156s 25s 6.2x 3.5xSVMSGDSolver 9000 132s 24s 5.5x 3.1x

ClusterModelK-Means 9000 222s 35s 6.3x 3.6xExpecta9onMaximiza9on 9000 243s 40s 6.1x 3.4x

12


Predict Train

SGD(Stochas9cGradientDescent),IPM(InteriorPointMethod)


GraphAnalysis

• Graphseverywhere:– Facebook(friendsoffriends),TwiLer,LinkedIn,etc…

• Mostdatahasinter-rela9onshipsthatcontaininsights

•  Twomajortypesofgraphalgorithms

– Computa9onalGraphAnaly9cs:Analysisofen9reGraph•  InfluencerID,communitydetect,paLermachine,recommenda9ons

– GraphPaLernMatching

•  Queriesthatfindsub-graphsfi|ngrela9onshippaLerns

13

Inter-rela,onshipsbetweendataandnetworksaregrowinginimportance

spouse

friend

friend

Structurefinding,rankingcommuni9es,pathfinding

friend


Computa9onalGraphAlgorithms:PageRank&Single-SourceShortestPath(SSSP)

• Graphcomputa9onsacceleratedbySPARC’smemorybandwidth– Bellman-Ford/SSSP(single-sourceshortestpath)–op9malrouteorconnec9on

– PageRank-measuringwebsiteimportance

Graph:SPARCM7upto1.5xfasterpercorethanx86GraphAlgorithm Workload

Size4-chip

X86E5v34-chip

SPARCT7-4SPARCperchipAdvantage


SSSP

Bellman-Ford

448Mver9ces,17.2Bedges 39.2s 14.7s 2.7x 1.5x233Mver9ces,8.6Bedges 21.3s 8.5s 2.5x 1.4x

PageRank448Mver9ces,17.2Bedges 136.7s 62.6s 2.2x 1.2x233Mver9ces,8.6Bedges 72.1s 27.6s 2.6x 1.5x

14


OracleDB

OracleDatabaseIn-Memory&SPARCDAX

• OLTPusesprovenrowformat

• Analy9csusein-memoryColumn•  10xfasteranaly9csduetosomware•  Columnarcompressionmeanshugedatabasescannowfitin-memory

• OracledatabasestoresBOTHrowandcolumnformats

•  Simultaneouslyac9veandtransac9onallyconsistent

• EvenfasterwithSPARCDAX(DataAccelerator)HWinnova9on

•  Addi9onal10xto20xfasterforAnaly9cs

15

RowFormat ColumnFormat

CachedOLTP

Transac9onsWholeDBInMemory

Compressed

WholeDBInMemory

CompressedWholeDB

OnDisk/Flash

Rowstore


SPARC’sMul9-genera9onalLeapfroginPerformance

•  IntegratedOffload

– DataAnaly9csAccelera9on(DAX)– Encryp9on&Security

•  It’smoreimportanthowyouusetransistors,thanMoore’sLaw(thenumbertransistorsyoumake)

RadicalInnova,on:IntegratedOffloadoffers10xfasterperformance!

OthervendorsfocusedonDetachedGPUs•  DetachedGPUsarepoorlydesignedforSQL•  SlowGPUinterconnec$onrobsperformance

Memory

HalfBW

Memory

X86IBMPower

100%U,lizedNOOFFLOAD!NOOPENCores!

Band-Width

SPARCS7

DAXOFFLO

AD O

FFLOADDAX

16


SPARCDrama9callyFasterIn-MemorySQLAnaly9cs

•  SPARCS79.4xfasterpercorethanx86E5v4– SPARCS7-2422query/mvs2-chipx8656query/m

•  S7-2(18-corestotal),2-chipx86E5v4(20-corestotal)

– x86percoreperformanceflatfor4genera9ons

• DAXoffloadsIn-MemoryScans

– DAXoffloadallowscorestoincreasethroughput

•  160GBinmemoryrepresents1TBDBondisk•  6.2xcompressionwithOracle12.2In-memory

17

SPARCDAX(SoowareonSilicon)ismul,genera,onalleadinperformance

In-MemorySQLAnaly,cs

9.4xfasterSPARCcoreadvantage

E5v4x86

SPARCOffloadDAXfrees

Coresforotherprocessing

6xCompression

6xIMcapacity


Real-TimeEnterprise:SimultaneousOLTP&In-memorySPARCS7:Fasteranaly,cs,fasterOLTP,andbe#erresponse,me(percore)

Analy,cs:SPARCS75.12xfasterpercoreOLTP: SPARCS72.04xfasterpercore

Analy,cs47

Queriespermin

SPARCS7-216cores

VS

OLTPPerformanceThroughput

216k

Transac,onspersec

Analy,cs

107Queriespermin

OLTPPerformanceResponseTime

~8ms

Throughput

196kTransac,ons

persec

ResponseTime

~9ms

x86E5v336cores

SixteenSPARCS7coressameloadas

FiUy-sixx86cores

SF1050=1.05TBDataWarehouseOracle12c12.1.0.2.2 18



GraphicProcessingUnits(GPUs)GPUsOnlyHelpVeryComputeIntensiveAlgorithmsComplexML/Sta3s3csoUennotfastonGPUs…“Canonlycomputeasfastasmovedata”• DetachedGPUshavelimitedbandwidth

– GPUsfitforlotsofsimplegraphics•  Graphicshave9nydatamovement:Coords&texturesin,videoframeout

– BigDataMLmuchmoredemanding:GPUshavelimitedapplica9ons• MLLearning&Scoring

– SomeMLLearningcanbecomputeintensive,butonlydoneini9allytocreateModel

•  NvidiaMathLibrariesonlyhaveBLAS3rou9nes– BLAS2andBLAS1doNOThavecomputeintensitythereforearenotinlibrary

– ComplicatedMLhasmixofBLAS1,BLAS2,BLAS3Bandwidthlinestoscale

GPUcomputeoUenstalledbylackofbandwidth

Memory

Chip

Local169GB/s

Local57GB/s

E5v422core

Local57GB/s

E5v422core

GPU GPU

CurrentPCIe FutureNVlink

SPARCM732core


SQListhePowerfulLanguageforDataScien9sts

•  SQLapowerful,concise,expressive,thatenablesrapiddevelopment

– Decadesofadvancedalgorithmsop9mizingSQL

– CanalsowriteSQLinDSL(DomainSpecificLanguages)forSpark,Python,R,Scala,etc…

•  Examplesofuses

– ApacheSparkSQL(67%growthinSQLusers)– JavaStreams-DSLSQLforJava

•  UsedbymanycustomersaswellasOracleCoherence

– ApacheEclipse–GoldmanSachsCollec9ons

– ApacheHadoopHive– OracleDatabaseAnaly9cs&OLTP

20

SQLprovideETLandQuerypowerforAnaly,cs,MachineLearning(ML),andGraph

BecauseSQLissoimportantOracleputSQLinSilicon

intheSPARCprocessor(DAX)

SPARCisOver10xFasterthan86BecauseofUniqueDesign


SQL&theManyFlavorsofDSL–Allpoten9alsforDAX

•  SQL:– SELECTcount(*)frompersonWHEREci$zen.age>18

• ApacheSparkSQL(DSL)– valvoters:Int=ci$zen.filter($”ci$zen.age”>18).count()

•  JavaStreams(DSL)

– intvoters=arrayList.parallelStream().filter(ci9zen->ci9zen.olderThan(18)).count();

• GoldmanSachsCollec9ons–ApacheEclipse(DSL)

– intvoters=fastList.count(ci9zen->ci9zen.olderThan(18));

ORACLECONFIDENTIAL-HighlyRestricted

SQLcanbewri#eninDomainSpecificLanguages(akaLanguageIntegratedQueries)

ApacheSparkfeedsbothformatsintoSQLop3mizerJoinscanbewriNen&op$mized


SPARCDAXIntegratedwithJavaJDK8Streams

•  JavaStreamsprovideSQL-stylecodeinJava– JavaStreamsisaperfectfitforDAXaccelera9on

• DAX&JavaStreams– IntegerStreamfilter,allMatch,anyMatch,noneMatch,map(ternaryoperator),toArray&countfunc9ons

– SimplecodechangestouseDAX•  importcom.Oracle.Streaminsteadofimportjava.u9l.Stream

•  DaxIntStreaminsteadofIntStream

• Publicaccess:hLp://SWiSdev.oracle.com/DAX


DAXacceleratesJavaStreams:3.6xto21.8xfaster

3.6x 4.0x 4.1x

10.7x

21.8x

Outlier %9le Top-N Filter allMatch

SpeedupwithDAX10MillionRows

JavaStreamAPI:filter2_data.parallelStream().filter(w->w.temp<100).count()


SPARCDAXAccelera9ngApacheSpark2.1.0

• Proof-of-ConceptPrototypeonSPARCS7:ApacheSpark2.1POC:DAX&in-memorycolumnar

– 2-chipSPARCS7DAX:9.8Billionrows/secon2-predicatescan•  SPARCS7-2(16totalcores) 0.061sec•  2-chipx86(20totalcores)0.760seclatestgenera$onE5v4Broadwell

– TheseadvantagesareoverandaboveTungsten’simprovements

•  SPARCDAXoffloadscri9calfunc9ons

– SQL:filtering,dic9onaryencoding,1&2predicate,joinprocessing,addi9onalcompression,etc.

•  LibdaxopenAPI:hLp://SWiSdev.oracle.com/DAX

23

SparkSQLonSPARCS7-2DAXover15.6xfasterpercorethanx86

x86 SPARCDAX

15.6xfasterpercorethanx86

SparkOn

SPARCS7


SPARCAdvantagesforEveryFormofAnaly9cs

Analy3cs In-memoryColumnarSW&SoUwareinSilicon(SPARCDAX)

SPARC’sefficientCPU&memorydesign

DatabaseAnaly,csAnaly$cSQLQueriesfordatabase ✔ ✔JavaAnaly,csJavaCodethatprobesdataSQL-likecode ✔ ✔MachineLearning(ML)Automa$callyfindinghiddenpaNerns&correla$ons ✔ ✔GraphAutoma$callyfindpaNernsinSocialnetworks,etc. ✔NoSQLFastaccesstosemi-structured&unstructureddata ✔Spa,alFastthroughputongeometricdata ✔

24

SPARCis1.3xtoover10xfasterpercoreonAnaly,csthanx86

Featuregenera$on&Selec$onforMLisoXenSQL


Java&DatabaseareSPARC’sDesignTargets

• OraclebelievestheEnterpriserequiresdeepinnova3onstomakeserverssignificantlybe#erforCloudandAnaly,cs

– SPARC,DB,&Javaco-engineeredtocreateuniquechipinnova9ons• Breakthroughsof70%to10xormorebyworkingacrossthestackboundaries

– Oraclerevolu9onizingtheCloud• Drama9cinnova9onsinsecurity,analy9cs,andefficientVMs

SPARCdras3callyreduceshardware&licensingcost

25

Innova,onsdifferen,ateSPARCfromthegenericcompu,ng


SPECjbb2015Mul9JVMSPARCS7

•  Innova9onsinSPARCprocessordesigncon9nuetoimproveJavaandJVMperformance

– x86performanceisflatgenera9ontogenera9on

•  TradeoffbetweentuningforMaxjOpsorCritjOpsonx86

26

SPARCS7core1.5xto1.9xfasterthanx86E5v4coreonMaxjOps/core

0

1,000

2,000

3,000

4,000

5,000

6,000

CritjOps/core

MaxjOps/core

x86E5v4 x86E5v3 Processor chip,

coreMaxjOPS

CritjOPS

SPARCS7-2 4.27GHzSPARCS7 2,16 65,790 35,812

IBMS812LC 2.9GHzPower810c 1,10 44,883 13,032

HuaweiRH2288Hv3 2.2GHzE5v422c 2,44 121,381 38,595

CiscoC220 2.2GHzE5v422c 2,44 94,667 71,951HuaweiRH2288Hv3 2.3GHzE5v318c 2,36 98,673 28,824

Lenovox240M5 2.3GHzE5v318c 2,36 80,889 43,654


Java(JVM)istheLanguageoftheCloud&FOSS

JVM=JavaVirtualMachine-Run$me


Java&theunderlyingJVMisthedesigntargetforSPARCBigData&DevOps Descrip,on Wri#entoOracleFusion EnterpriseApps Java

ApacheSpark In-memoryAnaltyics JVM

ApacheCassandra NoSQLdatabase Java

ApacheHadoop DisktodiskMapReduce Java

ApacheHDFS filesystem Java

ApacheLucene DoctextIndexing Java

ApacheSolr text/doc/websearch Java

Jersey/Grizzly(REST) RESTInterface Java

ApacheHive SQLonHDFS Java

Neo4j Graph Java

Scala Language JVM

BigData&DevOps Descrip,on Wri#entoAkka(REST) RESTforBigData JVM

ApacheAccumulo key/valuestore Java

ApacheFlink Batch&StreamProcessing Java(JVM)

ApacheKaÄa StreamingMessageBroker JVM

Apachelog4j Logeventmanager Java

ApacheSamza Streamprocessing JVM

ApacheStorm Streamprocessing Java&Clojure

ApacheYarn Clusterjobmanager Java

ApacheZookeeper Config&groupservices Java

H20.ai MLAppslibraries Java,Python,R

ApacheHbase NoSQLdatabase Java


SPARCcoreisFastestforFOSS–x86muchslower

•  FOSS(Free&Open-SourceSomware)– ApacheSpark SPARCDAX>15xfaster

– ApacheCassandra SPARCcore1.7xfaster

– ApacheHadoop SPARCcore1.6xfaster

•  SPARCAdvantages:– SPARCfastestatJVM&highestefficiency,GCimprovements

– Chipdesigninnova9onseachgenera9on– SPARCscaleswithmul9-threading:SPARC8threadsvs.x862-threads

– SPARChas2xgreatermemorybandwidth

1.6xto2.0xfasterpercoremeansyouneedspendlotsmoreonx86thanSPARC

28

MuchofFOSSrunsonJAVAorJVM


Oracle&FOSS:Transac9ons&Analy9csTransac,onsnowgeungenrichedwithliveAnaly,cs

•  SPARCprocessorsaccelerateeverytypeofTransac9onal&Analy9calworkload–  Hadoopcomponents:Hive(SQL),HBase(NoSQL),Mahout(ML),Giraph(Graph),…

– ML=machinelearning/sta9s9cs


Transac,ons SQLQueryAnaly,cs NoSQL ML

MachineLearningGraph

Analy,cs Streaming

Fusion&OracleDB

Oracle12cIn-memory

OracleNoSQLOracleAdvanced

Analy9csOracleSpa9al

&GraphOracleStream

Analy9cs

OracleBigDataSQL(mergedatafromvarioussources)

DevOps&Database

SparkSQLCassandraNoSQL

&MongoDBSparkMLlib SparkGraphX

SparkStreaming&KaÄa…

Oracle

FOSS


CassandraNoSQL2.2.6(YahooCloudServingBenchmark)

SPARCcoreis1.7xfasterthanE5v3core

•  SPARCS71.7xfasterpercorethanE5v3Haswell– SPARC’sadvantagesonJava/JVMbenefitCassandra

•  YCSB–YahooCloudServingBenchmark–300M

– MixedLoadWorkloadA(50%Read/50%Update)

30

Cassandra Chip,core Processor AveOps/s KOps/spercore


SPARCS7-2 2,16 4.27SPARCS7 66,955 4.2K 1.7xE5v3Haswell 2,36 2.3E5v3 90,184 2.5K 1.0x


Oracle’sNoSQLonYCSB(YahooCloudServingBenchmark)

• OracleNoSQL4.0•  SPARCS71.9xfasterpercorethanE5v4Broadwell

– E5v3HaswellcoresameperformanceasE5v4Broadwellcore

– YCSB(YahooCloudServingBenchmark)

• MixedLoad:95%Read/5%Update

SPARCS7coreis1.9xfasterpercorethanE5v4(Broadwell)

NoSQLYCSB Chip,core Processor Ops/s KOps/s

percoreSPARCpercoreAdvantage

NoSQLVersion

SPARCS7-2 2,16 4.27SPARCS7 340,766 21.3k 1.9x 4.02-chipE5v4Broadwell 2,44 2.2E5v4 502,273 11.4k baseline 4.0

31



SPARCisFasterthanx86forHadoop

•  SPARCM7secure3.9xfasterperchipthanes$matedunsecurex86E5v2IvyBridge– E5v3HaswelllessbandwidthpercorethanE5v2IvyBridge

•  SPARCM7secure4.3xfasterperchipthanunsecureIBMPower86c

SPARCcoresecureis1.4xfasterthanunsecurex86v2SPARCcoreunsecureis1.6xfasterthanunsecurex86v2

Terasort10TB Chip,core Processor

PerfGB/minPerchip

PerfGB/minPercore

Hadoop SPARCM7CoreAdvantage

OracleT7-41node 4,128 4.13SPARCM7 35.2 0.90 Opensource (unsecurebaseline)

OracleT7-41node 4,128 4.13SPARCM7 32.2 1.02 Opensource SecureAES-256-GCMbaseline

IvyBridgeEX 1,12 2.8E5v2 8.2est 0.67 Cloudera5.3 Es9mate1.35x(1.5x)est

32


UnpredictableAmazonAWSvs.Oracle’sOPC

• MostAWSx86processorsareslow&older

– AWSuseslowerperformancechips,Oracleusesfastestx86chipsateachgenera9on

– AWSvCPUx86= ½core=1hyperthread(mostlyE5v2,someE5v3)

– OPCOCPUx86= 1core=2hyperthreads(mostlyE5v3,newE5v4)

– OPCOCPUSPARC= 1core=8HWthreads(SPARCM7&SPARCT7)

• AWSUnpredictablerun-to-runperformancevaria9on

– Noisyneighboromenhurtsnetwork,storage,&CPUperformance

– x86coreinAWSareslowerandmorevariablethanbare-metalx86


AWS’sUnreliableQoSisahiddencost

SameCost

SPARCCMTThreadsdesignedtoscalex86Hyperthreadinghavewell-knownscalingissues


RequiredBenchmarkDisclosureStatement

•  Addi9onalInfo:hLp://blogs.oracle.com/bestperf•  Copyright2016,Oracle&/oritsaffiliates.Allrightsreserved.Oracle&JavaareregisteredtrademarksofOracle&/oritsaffiliates.Othernamesmaybetrademarksoftheirrespec9veowners

•  SPECandthebenchmarknameSPECjEnterpriseareregisteredtrademarksoftheStandardPerformanceEvalua9onCorpora9on.Resultsfromwww.spec.orgasof7/6/2016.SPARCS7-2,14,400.78SPECjEnterprise2010EjOPS(unsecure);SPARCS7-2,14,121.47SPECjEnterprise2010EjOPS(secure)OracleServerX6-2,27,803.39SPECjEnterprise2010EjOPS(unsecure);IBMPowerS824,22,543.34SPECjEnterprise2010EjOPS(unsecure);IBMx3650M5,19,282.14SPECjEnterprise2010EjOPS(unsecure).

•  SPECandthebenchmarknameSPECjbbareregisteredtrademarksofStandardPerformanceEvalua9onCorpora9on(SPEC).ResultsfromhLp://www.spec.orgasof6/29/2016.SPARCS7-2(16-core)65,790SPECjbb2015-Mul9JVMmax-jOPS,35,812SPECjbb2015-Mul9JVMcri9cal-jOPS;IBMPowerS812LC(10-core)44,883SPECjbb2015-Mul9JVMmax-jOPS,13,032SPECjbb2015-Mul9JVMcri9cal-jOPS;SPARCT7-1(32-core)120,603SPECjbb2015-Mul9JVMmax-jOPS,60,280SPECjbb2015-Mul9JVMcri9cal-jOPS;HuaweiRH2288Hv3(44-core)121,381SPECjbb2015-Mul9JVMmax-jOPS,38,595SPECjbb2015-Mul9JVMcri9cal-jOPSHPProLiantDL360Gen9(44-core)120,674SPECjbb2015-Mul9JVMmax-jOPS,29,013SPECjbb2015-Mul9JVMcri9cal-jOPS;HPProLiantDL380Gen9(44-core)105,690SPECjbb2015-Mul9JVMmax-jOPS,52,952SPECjbb2015-Mul9JVMcri9cal-jOPS;;CiscoUCSC220M4(44-core)94,667SPECjbb2015-Mul9JVMmax-jOPS,71,951SPECjbb2015-Mul9JVMcri9cal-jOPS;HuaweiRH2288HV3(36-core)98,673SPECjbb2015-Mul9JVMmax-jOPS,28,824SPECjbb2015-Mul9JVMcri9cal-jOPs;Lenovox240M5(36-core)80,889SPECjbb2015-Mul9JVMmax-jOPS,43,654SPECjbb2015-Mul9JVMcri9cal-jOPS;SPARCT5-2(32-core)80,889SPECjbb2015-Mul9JVMmax-jOPS,37,422SPECjbb2015-Mul9JVMcri9cal-jOPS;SPARCS7-2(16-core)66,612SPECjbb2015-Distributedmax-jOPS,36,922SPECjbb2015-Distributedcri9cal-jOPS;HPProLiantDL380Gen9(44-core)120,674SPECjbb2015-Distributedmax-jOPS,39,615SPECjbb2015-Distributedcri9cal-jOPS;HPProLiantDL360Gen9(44-core)106,337SPECjbb2015-Distributedmax-jOPS,55,858SPECjbb2015-Distributedcri9cal-jOPS;HPProLiantDL580Gen9(96-core)219,406SPECjbb2015-Distributedmax-jOPS,72,271SPECjbb2015-Distributedcri9cal-jOPS;LenovoFlexSystemx3850X6(96-core)194,068SPECjbb2015-Distributedmax-jOPS,132,111SPECjbb2015-Distributedcri9cal-jOPS.

•  SPECandthebenchmarknamesSPECfpandSPECintareregisteredtrademarksoftheStandardPerformanceEvalua9onCorpora9on.ResultsasofOctober25,2015fromwww.spec.organdthisreport.1chipresultsSPARCT7-1:1200SPECint_rate2006,1120SPECint_rate_base2006,832SPECfp_rate2006,801SPECfp_rate_base2006;SPARCT5-1B:489SPECint_rate2006,440SPECint_rate_base2006,369SPECfp_rate2006,350SPECfp_rate_base2006;FujitsuSPARCM10-4S:546SPECint_rate2006,479SPECint_rate_base2006,462SPECfp_rate2006,418SPECfp_rate_base2006.IBMPower710Express:289SPECint_rate2006,255SPECint_rate_base2006,248SPECfp_rate2006,229SPECfp_rate_base2006;FujitsuCELSIUSC740:715SPECint_rate2006,693SPECint_rate_base2006;NECExpress5800/R120f-1M:474SPECfp_rate2006,460SPECfp_rate_base2006.

•  Two-9erSAPSalesandDistribu9on(SD)standardapplica9onbenchmarks,SAPEnhancementPackage5forSAPERP6.0asof5/16/16:SPARCM7-8,8processors/256cores/2048threads,SPARCM7,4.133GHz,130000SDUsers,713480SAPS,Solaris11,Oracle12cSAP,Cer9fica9onNumber:2016020,SPARCT7-2(2processors,64cores,512threads)30,800SAPSDusers,2x4.13GHzSPARCM7,1TBmemory,OracleDatabase12c,OracleSolaris11,Cert#2015050.HPEIntegritySuperdomeX(16processors,288cores,576threads)100,000SAPSDusers,16x2.5GHzIntelXeonProcessorE7-8890v34096GBmemory,SQLServer2014,WindowsServer2012R2DatacenterEdi9on,Cert#2016002.IBMPowerSystemS824(4processors,24cores,192threads)21,212SAPSDusers,4x3.52GHzPOWER8,512GBmemory,DB210.5,AIX7,Cert#201401.DellPowerEdgeR730(2processors,36cores,72threads)16,500SAPSDusers,2x2.3GHzIntelXeonProcessorE5-2699v3256GBmemory,SAPASE16,RHEL7,Cert#2014033.HPProLiantDL380Gen9(2processors,36cores,72threads)16,101SAPSDusers,2x2.3GHzIntelXeonProcessorE5-2699v3256GBmemory,SAPASE16,RHEL6.5,Cert#2014032.SAP,R/3,regTMofSAPAGinGermanyandothercountries.Moreinfowww.sap.com/benchmarks.

MustbeinSPARCS7&M7Presenta,onswithBenchmarkResults


BackupSlides

35


Oracle’sBigDataSQL

• Oracle’sBigDataSQL– SameSQLaccessesbothDatabase&NoSQL

– SPARCT7-2forOracleDatabasewithDAX&secondSPARCT7-2forOracle’sNoSQL

•  3TBin-memorydatabase,single-threadedquery

OracleDatabaserunsin-memorywithDAXandcombinesdatafromNoSQLdatasource

Query DatabaseQueryModifiedtoalsoaccessNoSQL

Changein,me

a1 32.8s 34.0s 1.04x

b1 18.4s 19.6s 1.07x

b3 14.6s 17.7s 1.21x

q11x 14.5s 15.5s 1.07x

q12x 16.2s 17.4s 1.07x

SELECTc_name,c_mktsegment,lo_orderpriority,lo_ordtotalpriceFROMt1,lineorder,date_dim,customerWHERElo_orderdate=d_datekeyandc_email=ekeyandlo_custkey=c_custkeyandd_daynuminweek=1andlo_quan9ty=1andlo_orderpriority='1-URGENT'andlo_shipmode='RAIL'andlo_discount=1andc_region='ASIA'andc_mktsegment='BUILDING'andlo_ordtotalpriceBETWEEN1700000and1900000;

NoSQLaccessedbyadding:t1andekey

36


SecurityKernelSPARCS7PerformanceAES:SPARCS7coreis2.3xfasterthanx86E5v4core(AES-NI)SHA:SPARCS7coreis7.5xfasterthanx86E5v4core

FIPS140-2Level1Valida$onSolaris11.3:KernelCryptographicFrameworkModule&UserlandCryptographicFrameworkModule

AES-CFBpercore

S72.3xfasterthanE5v4Broadwell

0.89GB/s

0.89GB/s

1.98GB/s

2.05GB/s

x86 E5 v3 2.3 GHz

x86 E5 v4 2.2 GHz

SPARC M7 4.13 Ghz

SPARC S7 4.27 Ghz

SHA512percore

S77.5xfasterthanE5v4Broadwell

0.30

0.40GB/s

2.90GB/s

2.99GB/s

E5 v3 2.3 GHz AVX2

E5 v4 2.2 GHz AVX2

SPARC M7 4.13 GHZ

SPARC S7 4.27 GHZ

AES-256-CFBDataatrestDB,Cloud,..

SHA512-1024SecureChecksumBanking,UKonlinemoney…


MachineLearning(ML):Scoring/Predic9onversusTraining/LearningCharacteris9csPredic,on/Scoringoperatesonhugeamountsofdatawithlowcomputeintensity

MLScore/Predic,on

MLLearn/Train

%ofac9vity MostData *Ini9al

Computa9on O(n^2)Matrix-vector

O(n^3)Matrix-matrix

Data O(n^2) O(n^2)

ComputeIntensity(Compute/Data) Lowconstant O(n)

SPARCAdvantageDuetoMemory

Bandwidth&design

3xto6x

percoreUpto1.3xpercore

OracleConfiden9al–HighlyRestricted 38

TrainingSet

Scoring/Predic9on

Results

Cantrainononeserverthenmovemodeltopredic$onserver(ex:StubHub)Modelslive4weeks!

Training/Learning

DatatoEvaluate

*Ini$alandthenoccasionallyupdatemodels

Model

Every4weeks


GPUsOnlyAccelerateSomeComputeKernelsDetachedGPUmemorybandwidthisbo#leneckformostcomputa,ons

Rou,neLevel Opera,on Flops MemComputeIntensity

TeslaK80peak

MaxPossibleGflops

@12GB/sblock=100

PCIeGPU

%peak

MaxPossibleGflops

@80GB/sblock=100

Nvlink(future)GPU

%peak

BLAS1(vector)

y=ax+y 2n 3n 2/3 935Gflops 1Gflops 0.1% 7Gflops 0.7%

BLAS2(matrix-vector)

y=Ax+y 2n^2+2n n^2 2 935Gflops 3Gflops 0.3% 20Gflops 2.1%

BLAS3(matrix-matrix)

C=AB+C 2n^3 4n^2 n/2 935Gflops 75Gflops 8.0% 500Gflops 53.5%

FFT C=FFT(A) nlog(n) 2n Log(n)/2 935Gflops239Gflops1M1D-FFT

25% 935Gflops1M1D-FFT

Nearpeak


Only6BLAS3rou9netypesinNvidiaLibrary:gemm,syrk,syr2k,trsm,trmm,symmCanwritecodesusingBLAS2/1inGPUsbutresul9ngcomputeintensitymustbelargetoaccelerate

advanced analy9cs & graph ... - the oracle big data€¦ · • oracle advanced analy9cs in...

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