Spark SQL: Another 16x Faster After Tungsten: Spark Summit East talk by Brad Carlile

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SparkSQL:Another16xfasteraFerTungstenSPARCprocessorhasdrama1cadvantagesoverx86onApacheSpark

BradCarlileSr.DirectorSoluKonArchitectureEngineeringSAERoom311,Wednesday,Feb8,2:40pm–2:55pmFeburary7,2017Addi$onalinfoonProofpoints:hXp://blogs.oracle.com/bestperf


SafeHarborStatementThefollowingisintendedtooutlineourgeneralproductdirecKon.ItisintendedforinformaKonpurposesonly,andmaynotbeincorporatedintoanycontract.Itisnotacommitmenttodeliveranymaterial,code,orfuncKonality,andshouldnotberelieduponinmakingpurchasingdecisions.Thedevelopment,release,andKmingofanyfeaturesorfuncKonalitydescribedforOracle’sproductsremainsatthesolediscreKonofOracle.

2

SPARCDAXinApacheSparkisProof-of-conceptandnotproduct


SparkisExciKng–Lotsofso=wareinnova$onSpark’sWholeEcosystemtotallyappealstotheperformanceexpertinme

• GreatEcosystemforAnalyKcs– SparkformsaconKnuumwithothertechnologies(Ka`a,Solr,…)– LotsofOracle’scustomerusingSpark

•  Sparkisfast&scalablebecauseofcleandesign– Spark’sin-memoryfocus

•  SparkspeaksSQL&DataFrames– PerfectmarriageforDataScienKsts&HardwareAcceleraKon

– …butwhatisthestatusofprocessorperformance?3


ModernAnalyKcsisaboutYourData&ExternalData

• AnalyKcsisnowyourdataPLUSexternaldata– OracleDatabaseIn-MemoryFastestonSPARC– ConKnuouslyanalyzingsamedataindifferentways–Fastestifstoredatain-memory

• BigDatacanbeusedtodescribeexternaldata– Socialnetworks,publicdata:senKments,weather,traffic,events,raKngs,trends,IoTsensors,behaviors,…

– LotsofETL/filteringneededtofindusefuldata– BigDataSQL

ConfidenKal–OracleInternal/Restricted/HighlyRestricted 4

BeBerinforma1onwhenyoucananalyzeallrelevantdata

Weather

Tweets

ExternalData

Order

Customer

YourData

SensorsSocial


0.8x

1.0x

1.2x

1.4x

1.6x

1.8x

2.0x

2012 2013 2014 2015 2016

CorePerform

ance

vs.x86E5v2

WhyNOTUpHERE?

Isitreallylatency?

X86percoreperformanceflatfor4genera1ons

ISTHERECOMPUTEBOTTLENECK?REALLY?

MITTechReview,“ChipmakerIntelhassignaledaslowingofMoore’sLaw,…whichplayedaroleinjustabouteverymajoradvanceinengineeringandtechnologyfordecades.”

5

x86Innova1onsinPerCorePerformancehasStalled

"percore=(serverperformance)/(servercorecount)"

E5 E5v2 E5v3 E5v4


0.8x

1.0x

1.2x

1.4x

1.6x

1.8x

2.0x

2012 2013 2014 2015 2016

CorePerform

ance

vs.x86E5v2

x86:flatforfourgenera1ons

SPARC’sInnovaKonsareConKnuallyIncreasingCorePerformance

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

6

Innova1veHWDesigngetsaround“x86boBleneck”

SPARCM7 S7

SPARCS7

SPARC:DB

&Java/JVM

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

Java-JVMOLTPDBMeoryBandwidthGB/s

SPARCS7is1.6xto2.1xfasterthanx86

It’smoreimportanthowyouusetransistors,thanthenumbertransistorsyoumake(Moore’sLaw)

7

Innova1veHWDesigngetsaround“x86boBleneck”,x86percoreperfstalled

x86indashedlines

SPARCins

olidlines

E5E5v2

E5v3 E5v4

SPARCT5

SPARCM7S7

SPARCS7

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


SPECjbb2015MulKJVMSPARCS7

•  InnovaKonsinSPARCprocessorinnovaKonsimproveJavaandJVMperformance– x86performanceisflatgeneraKon-to-generaKon•  TradeoffbetweentuningforMaxjOpsorCritjOpsonx86

8

SPARCS7core1.5xto1.9xfasterthanx86E5v4coreonMaxjOps/core

0

1,000

2,000

3,000

4,000

5,000

6,000

S7-2 Huawei HP HP Cisco HuaweiLenovo

CritjOps/coreMaxjOps/core

x86E5v4 x86E5v3

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

Processor chip,core

MaxjOPS

CritjOPS

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

HuaweiRH2288Hv3 2.2GHzE5v422c 2,44 121,381 38,595CiscoC220 2.2GHzE5v422c 2,44 94,667 71,951HuaweiRH2288Hv3 2.3GHzE5v318c 2,36 98,673 28,824Lenovox240M5 2.3GHzE5v318c 2,36 80,889 43,654


Java(JVM)istheLanguageoftheCloud&FOSS

JVM=JavaVirtualMachine-Run$meORACLECONFIDENTIAL-HighlyRestricted

Java&theunderlyingJVMisthedesigntargetforSPARCBigData&DevOps Descrip1on WriBentoOracleFusion EnterpriseApps Java

ApacheSpark In-memoryAnaltyics JVMApacheCassandra 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 Descrip1on WriBentoAkka(REST) RESTforBigData JVM

ApacheAccumulo key/valuestore Java

ApacheFlink Batch&StreamProcessing Java(JVM)

ApacheKa`a 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


CassandraNoSQL2.2.6(YahooCloudServingBenchmark)SPARCcoreis2.0xfasterthanE5v3core

•  SPARCS72.0xfasterpercorethanE5v3Haswell•  SPARC’sJava/JVMadvantagesbenefitCassandraperformance– YCSB–YahooCloudServingBenchmark–300M• MixedLoadWorkloadA(50%Read/50%Update)• Moreinfoat:hXp://blogs.oracle.com/bestperf

10

Cassandra Chip,core Processor AveOps/s KOps/s

percoreSPARCpercoreAdvantage

SPARCS7-2 2,16 4.27SPARCS7 69,858 4.4K 2.0xE5v3Haswell 2,36 2.3E5v3 90,184 2.5K 1.0x


OracleDatabasealsoOpKmizedforAnalyKcQueriesApacheSparkneedstoimplementtheseop$miza$ons!

• ColumnStoreisopKmalforAnalyKcs– AnalyKcsLooksacrosstransacKonsatspecificcolumns– DatabasecolumnsareMachineLearningfeatures

• Columnsfarmoreefficientwhenanalyzingfeatures(efficientbandwidth&CPUneeds)• CanexploitdatacharacterisKcs>20xbenefit

•  StorageopKmizaKons–datarepeatedlyanalyzed– VectorprocessingofDicKonary-encodedcomparesneedstobeaddedtoinSparkpersisKngDataFramesinmemory

–  layeredcompressionmeans10’sTBofdatafitintoTB’sofmemory•  ProcessingopKmizaKons– BloomFilterjoinprocessing,operatorpushdown,metadataopKmize

11

SQLop1mizedforanaly1cqueries BigFeatureRevoluIon

#featurescollectedisExploding10’sto100’s

Order F1 F2 … F150

Customer F1 F2 … F78 … F100


OracleDB

OracleDatabaseIn-MemoryAtScale&SPARCDAX

• OLTPusesprovenrowformat• AnalyKcsusein-memoryColumn

•  10xfasteranalyKcsduetosoFware•  Columnarcompressionmeanshugedatabasescannowfitin-memory

• OracledatabasestoresBOTHrowandcolumnformats•  SimultaneouslyacKveandtransacKonallyconsistent

• 10xto20xFasterSPARCDAX(DataAccelerator)HWinnovaKon

12

RowFormat ColumnFormat

CachedOLTP

TransacKonsWholeDBInMemory

Compressed

WholeDBInMemory

Compressed

10-100’sTBytesRowstore


SPARCDramaKcallyFasterIn-MemorySQLAnalyKcsinDB

•  SPARCS79.4xfasterpercorethanx86E5v4– SPARCS7-2422query/mvs2-chipx8656query/m•  S7-2(18-corestotal),2-chipx86E5v4(20-corestotal)

– x86percoreperformanceflatfor4generaKons

• DAXoffloadsIn-MemoryScans– DAXoffloadallowscorestoincreasethroughput

•  160GBinmemoryrepresents1TBDBondisk•  6.2xcompressionwithOracle12.2In-memory

13

SPARCismul1genera1onalleadinperformance:14Billion-row/secpercore

In-MemorySQLAnaly1cs

9.4xfasterSPARCcoreadvantage

E5v4x86

SPARCOffloadDAXfrees

Coresforotherprocessing

6xCompression

6xIMcapacity


SPARC’sMulK-generaKonalLeapfroginPerformance

•  IntegratedOffload– DataAnalyKcsAcceleraKon(DAX)– EncrypKon&Security

•  It’smoreimportanthowyouusetransistors,thanMoore’sLaw(thenumbertransistorsyoumake)

RadicalInnova1on:IntegratedOffloadoffers10xfasterperformance!

OthervendorsfocusedonDetachedGPUs•  DetachedGPUsarepoorlydesignedforSQL•  SlowGPUinterconnec$onrobsperformance

Memory

HalfBW

Memory

X86IBMPower

100%U1lizedNOOFFLOAD!NOOPENCores!

Band-Width

SPARCS7

DAXOFFLO

AD O

FFLOADDAX

14


GraphicProcessingUnits(GPUs)GPUsOnlyHelpVeryComputeIntensiveAlgorithmsComplexML/StaIsIcsoRennotfastonGPUs…“Canonlycomputeasfastasmovedata”• DetachedGPUshavelimitedbandwidth– GPUsfitforlotsofsimplegraphics•  GraphicshaveKnydatamovement:Coords&texturesin,videoframeout

– BigDataMLmuchmoredemanding:GPUshavelimitedapplicaKons• MLLearning&Scoring– SomeMLLearningcanbecomputeintensive,butonlydoneiniKallytocreateModel

•  NvidiaMathLibrariesonlyhaveBLAS3rouKnes– BLAS2andBLAS1doNOThavecomputeintensitythereforearenotinlibrary

– ComplicatedMLhasmixofBLAS1,BLAS2,BLAS3 BandwidthlinestoscaleGPUcomputeoRenstalledbylackofbandwidth

Memory

Chip

Local169GB/s

Local57GB/s

E5v422core

Local57GB/s

E5v422core

GPU GPU

CurrentPCIe FutureNVlink

SPARCM732core


SQL&theManyFlavorsofDSL–AllpotenKalsforDAX

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

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

•  JavaStreams– intvoters=arrayList.parallelStream().filter(ciKzen->ciKzen.olderThan(18)).count();

• ApacheEclipse(GoldmanSachsCollecKons)– intvoters=fastList.count(ciKzen->ciKzen.olderThan(18));

ORACLECONFIDENTIAL-HighlyRestricted

SQLcanbewriBeninDomainSpecificLanguages(akaLanguageIntegratedQueries)

ApacheSparkfeedsbothformatsintoSQLopImizerJoinscanbewrinen&op$mized


SPARCDAXIntegratedwithJavaJDK8Streams

•  JavaStreamsprovideSQL-stylecodeinJava– JavaStreamsisaperfectfitforDAXacceleraKon

• DAX&JavaStreams– IntegerStreamfilter,allMatch,anyMatch,noneMatch,map(ternaryoperator),toArray&countfuncKons– SimplecodechangestouseDAX•  importcom.Oracle.Streaminsteadofimportjava.uKl.Stream•  DaxIntStreaminsteadofIntStream

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

ORACLECONFIDENTIAL-HighlyRestricted

DAXacceleratesJavaStreams:3.6xto21.8xfaster

3.6x 4.0x 4.1x

10.7x

21.8x

Outlier %Kle Top-N Filter allMatch

SpeedupwithDAX10MillionRows

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


ApacheSpark:SQLandDSLBothOpKmizedbyCatalystOpKmizer

18

Ex:“SelectallbooksbyauthorsbornaRer1980named‘Paulo’frombooks&authors”SQLSELECT*FROMauthoraJOINbookbONa.id=b.author_idWHEREa.year_of_birth>1980ANDa.first_name='Paulo’ORDERBYb.Ktle

QueryDSL(DomainSpecificLanguage)valjoinDF=author.as(‘a).join(book.as(‘b),$”a.id”===$”b.author_id”).filter($”a.year_of_birth”>1980).filter($”a.first_name”=“Paulo”)).orderby(‘b.Ktle)

CatalystOp1mizerOp$mizedPlan

(canIreordervariousopera$ons?)Whole-StageCodeGen

newinSpark2.0,thisisProjectTungsten


ApacheSpark2.1Whole-stageCodeGenPerformance

•  Let’sevaluaKonperformanceonFullTableScanof600Mrows– TimetoScan=0.16secImpressive:104.2MillionRows/secpercore

19

2-chipx86E5v3(Haswell),total36cores,72threads/VCPU


ByEvaluaKngDeliveredBandwidth,WecanseewearesKllNotYetEfficient

•  EvaluaKonperformanceonFullTableScanof600Mrows– TimetoScan=0.16secImpressive:104.2MillionRows/secpercore

• Goingbackto1stprinciples:Scanningisaboutdatamovement– Whatisbandwidthof2.1in-memoryScan? 14GB/s(Spark2.0.1)– Whatisthesystemmemorybandwidth? 114GB/s(StreamTriad)– 7.6xmorebandwidthavailable!

• OtherQueriesdeliverless:“SELECTcount(*)FROMlineorderWHERElo_quanKtyBETWEEN10and20”

– 19xmorebandwidthavailable!--BUTonlydelivering6GB/sonthesystem

20

2-chipx86E5v3(Haswell),total36cores,72threads/VCPU


AFerSpark2.1:Another10xinPerformanceSKllPossibleSelectcount(*)fromstore_saleswheress_item_sk>100andss_item_sk<1000

1.  VolcanoIteratormodel:planinterpretaKon–  Open;Nextdataelement;performpredicate;Close

2.  “CollegeFreshman”Java:Whole-StageCodeGenpipelinedcode–  for(ss_item_skinstore_sales){if(ss_item_sk>100andss_item_sk<1000){count+=1}}

3.  TunedtruevectorizaKonlibrary:highly-tunedcodeoperatesonconKguouscolumn–  vectorRangeFilter(n,VECTOR_OP_GT,1000,VECTOR_OP_LT,1000,store_sales,result,result_cnt)

4.   HardwareAccelera1onfurtheracceleratesscanning–  vectorRangeFilter(n,VECTOR_OP_GT,1000,VECTOR_OP_LT,1000,store_sales,result,result_cnt)•  x86AVX2(GraphicsInstrucKon)– deliver70GB/sperchip•  Oracle’sSPARCM7processor– deliver140GB/sperchip

21

ProjectTungsten

SPARCDAX


SPARCDAXAcceleraKngApacheSpark2.1.0

• 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

•  SPARCDAXoffloadscriKcalfuncKons– SQL:filtering,dicKonaryencoding,1&2predicate,joinprocessing,addiKonalcompression,etc.

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

22

SparkSQLonSPARCS7-2DAXover15.6xfasterpercorethanx86

x86 SPARCDAX

15.6xfasterpercorethanx86

SparkOn

SPARCS7


In-MemoryColumnarTwo-levelCompression:OracleDBSametechniqueneedstobeaddedtoApacheSpark

•  EfficientInMemoryColumnarTable• DicKonaryencodinghugecompression–  50USonlyneed6bits(<1byte)vs.“SouthDakota”needs12charactersor192unicodebits=32xsmaller

–  Ozip/RLEcompressionisthenappliedontopofdicKonaryencoding

•  Innova1onsthatApacheSparks1llneeds–  Directlyscandic$onaryencodeddata!–  Rangescans(2-predicate)inonestep–  Ozipdecompressionwithoutwri$ngtomemory–  SaveMin&MaxforscaneliminaKon–  CanusedicKonaryfor“featurizaKon”ofdataforML

23

0132023

ColumnMin:SouthDakotaMax:Utah

DicIonary

Dictencode

Columnvaluelist

SouthDakota

Tennessee

Utah

Texas

SouthDakota

Texas

Utah

DicIonaryVALUEID

SouthDakota0Tennessee1Texas2Utah3

OZip+

RLE)

Capa-cityLow(Ozip)

Verizonre-inventedthisaswell(SparkSummit2017Boston):RealKmeAnalyKcalQueryProcessing&PredicKveModelDebasishDas(Verizon)


libdaxOpenAPIforFree&OpenSourceSoFware(FOSS)

• DesignedtoacceleratewidevarietyofOracle&FOSSsoFware– Examples:• OracleDBIn-memory9.5xfasterwithDAX• ApacheSparkSQLover16xfasterwithDAX• AcKveViam6xto8xfasterwithDAX•  JavaStreams3.6xto21.8xfasterwithDAX

• OpenAPIforlibdax–signupforfreesystemstoactuallydevelop/trycode– Publishedsamplecodes– hXps://swisdev.oracle.com

Libdaxdesignedforkeyscan&dic1onaryopera1onstoaccelerateavarietyofsoyware

24


SPARCProcessorisfastestforStaKsKcs&ML(MachineLearning)SPARCisFastestforSparkApplica1onsSPARCDAXcandrama1callyaccelerateSparkSQL



TheBasicAnalyKcsFlow

• Datacancomefrommanysources– Databases,NoSQL,csv,feeds…

• Weneedtoprepareit– DataMungingofallsorts!

• Analyzethedata– Findthe“rightway”toanalyzeit• ML,Graph,SQL…


Alotof1mespentinDataMunging

DataMunging

RESULTS

Analy1cs

NoSQL,Search.…

StreamingKaza,Storm…

Databases


ConKnuousAnalyKcsCycleResultsOFenEnrichTransacKonsAnaly1csismorethanonepipelinestream

• ConKnuousiteraKonsaroundthedataanalyKcswheel– Save,catalog,andre-useallthings:data,SQL,code,andanalyKcs

•  In-memoryadvantagesateachstage– SPARC’sDAX&leadingbandwidthiskey

• Manysourcesofdata– Internalproprietary,publicdata,externalstreaming,archives

OracleConfidenKal–HighlyRestricted 27

StreamingKaza,Storm,…

EnhanceTransacIons

Reports

NoSQL

ETL(SQL)

ETL(SQL)

In-Memory:Reusedata&analysis

ML&Graph(FP)

Results(SQL)

FeatureExtract,Generate&Transform

(SQL)

Databases


ComputaKonalGraphAlgorithms:PageRank&Single-SourceShortestPath(SSSP)

• GraphcomputaKonsacceleratedbySPARC’smemorybandwidth– Bellman-Ford/SSSP(single-sourceshortestpath)–opKmalrouteorconnecKon– PageRank-measuringwebsiteimportance

Graph:SPARCM7upto1.5xfasterpercorethanx86GraphAlgorithm Workload

Size4-chip

X86E5v34-chip

SPARCT7-4SPARCperchipAdvantage

SPARCpercoreAdvantage

SSSPBellman-Ford

448MverKces,17.2Bedges 39.2s 14.7s 2.7x 1.5x233MverKces,8.6Bedges 21.3s 8.5s 2.5x 1.4x

PageRank448MverKces,17.2Bedges 136.7s 62.6s 2.2x 1.2x233MverKces,8.6Bedges 72.1s 27.6s 2.6x 1.5x

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


OracleDatabaseAdvancedAnalyKcsOpKonMachineLearningonSPARC

•  OracleAdvancedAnalyKcsinOracleDatabase12.2–  SPARCM7fasterpercoreontraining64-bitfloaKngpointintensive–  In-memory640millionrecords,AirlineOn-Kmedataset

MLtrainingSPARCM7upto2.0xfasterpercorethanx86

SGD(StochasKcGradientDescent)IPM(InteriorPointMethod)

Training:CreaIngModelfromdata

ABri-butes

X5-44-chip

T7-44-chip

SPARCperchipAdvantage


Supervised

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

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


Predict Train


OracleDatabaseAdvancedAnalyKcsOpKonMachineLearningonSPARC

•  OracleAdvancedAnalyKcsinOracleDatabase12.2–  SPARCM7muchfasterpercoreonscoringbandwidth-intensive–  In-memory1Billionrecords,AirlineOn-Kmedataset

MLpredic1onSPARC3.0x-4.8xfasterpercorethanx86

SGD(StochasKcGradientDescent)IPM(InteriorPointMethod)

Predic1onUsingmodelon1Brecords

ABri-butes

X5-44-chip

T7-44-chip

SPARCperchipAdvantage


Supervised

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

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


Predict Train


SparkMLDataFrameAlgorithmsuseBLAS1&someBLAS2

•  ALS(AlternaKngLeastSquaresmatrixfactorizaKon–  BLAS2:_SPR,_TPSV–  BLAS1:_AXPY,_DOT,_SCAL,_NRM2

•  LogisKcregressionclassificaKon–  BLAS2:_GEMV–  BLAS1:_DOT,_SCAL

•  Generalizedlinearregression–  BLAS1:_DOT

•  Gradient-boostedtreeregression–  BLAS1:_DOT

•  GraphXSVD++•  BLAS1:_AXPY,_DOT,_SCAL

•  NeuralNetMulK-layerPerceptron•  BLAS3:_GEMM•  BLAS2:_GEMV

31

Lowercomputeintensityof0.66to2.0,BLAS1/BLAS2limitsMLperformance,BLAS3faster

SparseLinearAlgebrahasEvenlowercomputeintensity

“Onecanonlycomputeasfastasonecanmovedata”MaxFlops=Compute-intensity*(Memory-BW/bytes-element)Compute-Intensity=Flops/elementSingleFP=4bytes/elementDoubleFP=8bytes/element

HINT:SparkDevelopersneedtowritesub-blockmatriximplementa$onstogetmoreBLAS3usageinSparkML…Why?BLAS3aremoreefficiently,becausetheyuselessmemorybandwidthandreducenumberofmethodcallsFastestLAPACKsolversuseBLAS3


GPUsOnlyAccelerateSomeComputeKernelsDetachedGPUmemorybandwidthisboBleneckformostcomputa1ons

Rou1neLevel Opera1on 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 935Gflops 239Gflops1M1D-FFT 25% 935Gflops

1M1D-FFTNearpeak


Only6BLAS3rouKnetypesinNvidiaLibrary:gemm,syrk,syr2k,trsm,trmm,symmCanwritecodesusingBLAS2/1inGPUsbutresulKngcomputeintensitymustbelargetoaccelerate


SPARCAdvantagesonAnalyKcsAcrosstheSpectrumKeyunderlyingtechnologiesprovidedrama1cperformanceadvantages

SPARC’s Java & JVM advantages

SQL DAX Offload

SPARC DAX offload acceleration Oracle Developer Perflib math libraries

NoSQL Machine Learning Graph & Spatial

•  Oracle Database •  Big Data with DAX

•  Cassandra NoSQL •  Oracle NoSQL

•  Spark MLlib •  Advanced Analytics

•  Spark GraphX •  Oracle Spatial&Graph

upto10xfasterpercore

upto5xfasterpercore

upto1.5xfasterpercore

upto1.9xfasterpercore

33


RequiredBenchmarkDisclosureStatement•  AddiKonalInfo:hXp://blogs.oracle.com/bestperf•  Copyright2016,Oracle&/oritsaffiliates.Allrightsreserved.Oracle&JavaareregisteredtrademarksofOracle&/oritsaffiliates.OthernamesmaybetrademarksoftheirrespecKveowners•  SPECandthebenchmarknameSPECjEnterpriseareregisteredtrademarksoftheStandardPerformanceEvaluaKonCorporaKon.Resultsfromwww.spec.orgasof7/6/2016.SPARC

S7-2,14,400.78SPECjEnterprise2010EjOPS(unsecure);SPARCS7-2,14,121.47SPECjEnterprise2010EjOPS(secure)OracleServerX6-2,27,803.39SPECjEnterprise2010EjOPS(unsecure);IBMPowerS824,22,543.34SPECjEnterprise2010EjOPS(unsecure);IBMx3650M5,19,282.14SPECjEnterprise2010EjOPS(unsecure).

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

•  SPECandthebenchmarknamesSPECfpandSPECintareregisteredtrademarksoftheStandardPerformanceEvaluaKonCorporaKon.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-KerSAPSalesandDistribuKon(SD)standardapplicaKonbenchmarks,SAPEnhancementPackage5forSAPERP6.0asof5/16/16:SPARCM7-8,8processors/256cores/2048threads,SPARCM7,4.133GHz,130000SDUsers,713480SAPS,Solaris11,Oracle12cSAP,CerKficaKonNumber: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,WindowsServer2012R2DatacenterEdiKon,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&M7Presenta1onswithBenchmarkResults


BackupSlides

35


WhyApacheSparkisbestonSPARC

•  SPARC’sleadershipinfastJVMperformancespeedsScala– SPARCdesignfocusonJava&JVMbenefitsallAppsthatuseJVM

•  SPARC’shugeleadindeliveredmemorybandwidth– SPARCacceleratesSpark’sin-memorydesign–Bigdataappsneedsfastdatamovement

•  SPARCDAXtoprovidehugeoffloadacceleraKoninApacheSpark– ContribuKngcodetoApacheSparkSQL/DataFramestouseDAX

•  SPARC’sleadingfloaKng-pointperformanceforMachineLearning– DesignedforfastFloaKngPoint(FP)onsparse&advancedalgorithm

Designinnova1onsateverylevel

36


SPARCAdvantagesforEveryFormofAnalyKcs

AnalyIcs In-memoryColumnarSW&SoRwareinSilicon(SPARCDAX)

SPARC’sefficientCPU&memorydesign

DatabaseAnaly1csAnaly$cSQLQueriesfordatabase ✔ ✔JavaAnaly1csJavaCodethatprobesdataSQL-likecode ✔ ✔MachineLearning(ML)Automa$callyfindinghiddenpanerns&correla$ons ✔ ✔GraphAutoma$callyfindpanernsinSocialnetworks,etc. ✔NoSQLFastaccesstosemi-structured&unstructureddata ✔Spa1alFastthroughputongeometricdata ✔

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SPARCis1.3xtoover10xfasterpercoreonAnaly1csthanx86

Featuregenera$on&Selec$onforMLiso=enSQL


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sRow

s/sec

Bitstoencodedata:log2(cardinality)

RangeScanUsedinSQL“Between”

S7-1DAX8-coreS7-2DAX16-coreE5v3AVX218-core

SPARCDAXScanPerformanceversusx86E5v3(AVX2)DAXoffloadscans:SPARCS7onlyuses25%coresandx86uses50%cores

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18-corex86E5v3

8-coreSPARCS7

OraclehasopenAPIforDAX:hXps://swisdev.oracle.com/

16-coresSPARCS7-2


“Big”Features:AXributes/QualiKestoAnalyze

• OrganizingfeaturesiscriKcaltotheanalyKcscycleConfidenKal–OracleInternal/Restricted/HighlyRestricted 39

BigDataisalsoBigFeatures:10’sto100’sfeaturestochose,create,&manage

StaKsKcsML

(MachineLearning)

ResultsReport

FeatureDBSQL

JoinsSQL

ETLSQL

(Extract,transform,&load)

FeatureSelect&

FeaturizaIon(behaviors)

SQL

RAWDATAPublic

&private


MachineLearning(ML):Scoring/PredicKonversusTraining/LearningCharacterisKcsPredic1on/Scoringoperatesonhugeamountsofdatawithlowcomputeintensity

MLScore/Predic1on

MLLearn/Train

%ofacKvity MostData *IniKal

ComputaKon O(n^2)Matrix-vector

O(n^3)Matrix-matrix

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

ComputeIntensity(Compute/Data) Lowconstant O(n)

SPARCAdvantageDuetoMemoryBandwidth&design

3xto6xpercore

Upto1.3xpercore


TrainingSet

Scoring/PredicKon

Results

Cantrainononeserverthenmovemodeltopredic$onserver(ex:StubHub)Modelslive1week!

Training/Learning

DatatoEvaluate

*Ini$alandthenoccasionallyupdatemodelsdatasamples

Model

Every4weeks

Spark SQL: Another 16x Faster After Tungsten: Spark Summit East talk by Brad Carlile

Data & Analytics

Transcript of Spark SQL: Another 16x Faster After Tungsten: Spark Summit East talk by Brad Carlile