ascr overview and perspective - cxrocxro.lbl.gov/pdf/bill_harrod_ascr.pdfascr overview and...

ASCROverviewandPerspectiveon

SemiconductorTechnologies

BillHarrodDOE/ASCR

March24,2016

RelevantWebsitesASCR:science.energy.gov/ascr/ASCRWorkshopsandConferences:science.energy.gov/ascr/news-and-resources/workshops-and-conferences/SciDAC:www.scidac.govINCITE:science.energy.gov/ascr/facilities/incite/

AdvancedScientificComputingResearch(ASCR)ataGlance

Office ofAdvancedScientific ComputingResearch

AssociateDirector–SteveBinkleyPhone:301-903-7486

E-mail: [email protected]

Research

DivisionDirector–WilliamHarrodPhone:301-903-5800


Facilities

DivisionDirector–BarbaraHellandPhone:301-903-9958


1

ASCRResearchDivision• AppliedMathematics• Emphasizes scalable numerical methods forcomplex systems, uncertainty quantification, large-scale data

analysis and exascalealgorithms;• ComputerScience• Exascalecomputing (architecture, parallelism, power aware,faulttolerance), operating systems, compilers,

performance tools, productivity, scientific datamanagement, analysis andvisualization forpetabyte toexabytedatasets;

• Partnerships• Co-Design andpartnerships topioneer thefuture ofscientific applications;• NextGenerationNetworksforScience• Tools for thefuture ofdistributed science• ResearchandEvaluationPrototypes• FastForward andDesign Forwardpartnerships with Industry andNon-Recurring Engineering fortheplanned

facility upgrades

2

NATIONALSTRATEGICCOMPUTINGINITIATIVEJuly29,2015

EXECUTIVEORDER- - - - - - -

CREATINGANATIONALSTRATEGICCOMPUTINGINITIATIVE

BytheauthorityvestedinmeasPresidentbytheConstitutionandthelawsoftheUnitedStatesofAmerica,andtomaximizebenefitsofhigh-performancecomputing(HPC)research,development,anddeployment,itisherebyorderedasfollows:

TheNSCI isawhole-of-governmenteffortdesignedtocreateacohesive,multi-agencystrategicvisionandFederalinvestmentstrategy,executedincollaborationwithindustryandacademia, tomaximizethebenefitsofHPCfortheUnitedStates.

https://www.whitehouse.gov/the -pre ss -offi ce/2015/07/29/executive-order -creati ng-national -strategi c-computing -initiativehttps://www.whitehouse.gov/sites/de fault/ files/mi crosite s/ostp/nsci _fa ct_sheet.pdf

March31,2016 DOEEEEWorkshop 3

NSCIIntent• National

• “Whole-of-government”and“whole-of-Nation”approach• Public/privatepartnershipwith industryandacademia

• Strategic• Leveragebeyondindividualprograms(akey“platform”technology)• Longtimehorizon(decadeormore)

• Computing• HPC=mostadvanced,capablecomputingtechnologyavailableinagivenera• Multiplestylesofcomputingandallnecessaryinfrastructure• Scopeincludeseverythingnecessaryforafullyintegratedcapability

• Theoryandpractice,softwareandhardware

• Initiative• Abovebaselineeffort• Linkandliftefforts

Enhance U.S.strategicadvantage inHPC foreconomic competitiveness and scientific discovery


KeyThemes

• Striveforconvergenceofnumericallyintensiveanddata-intensivecomputing• KeeptheU.S.attheforefrontofHPCcapabilities• StreamlineHPCapplicationdevelopment• MakeHPCreadilyusableandaccessible• EstablishhardwaretechnologyforfutureHPCsystems


Genomics• Sequencer datavolume increasing 12x,next 3years• Sequencer costdeceasing by 10oversameperiod

HighEnergyPhysics• LHCExperiments produce petabytes ofdata/year• Peakdataratesincrease 3-5x over 5years

LightSources• Manydetectors onMoore’s Lawcurve• Datavolumes rendering previous models obsolete

Climate• By2020, climatedataexpected tobeexabytes• Significant challenges in datamanagement &analysis

“VeryfewlargescaleapplicationsofpracticalimportanceareNOTdataintensive.”Alok Choudhary, IESP,Kobe,Japan,April2012

• DOEmissionsrequirecomputationalenvironmentsthataddressbothcomputeanddata-intensivesimultaneously

• Data-intensivesciencefacesmanyofthesametechnologychallengesofextreme-computing– Someareevenworsefor“big-data”

– Energyuse isthegrandchallenge(e.g.thesquarekilometerarrayestimates100MWneededforcomputing)


ConvergenceofComputeandData-intensiveScienceCriticalto21st CenturyScience

Systemattributes NERSCNow

OLCFNow

ALCFNow

NERSCUpgrade

OLCF CORALUpgrade

ALCF CORALUpgrades

NameInstallation

Edison TITAN MIRA Cori2016

Summit2017-2018

Theta2016

Aurora2018-2019

System peak(PF) 2.6 27 10 >30 150 >8.5 180

PeakPower(MW)

2 9 4.8 <3.7 10 1.7 13

Totalsystemmemory

357TB 710TB 768TB

~1PBDDR4+ HighBandwidthMemory

(HBM)+1.5PBpersistentmemory

>1.74 PBDDR4+HBM+2.8PB

persistentmemory

>480TBDDR4+HighBandwidthMemory(HBM)

>7 PBHighBandwidthOn-PackageMemoryLocalMemoryandPersistentMemory

Node Perf.(TF) 0.460 1.452 0.204 >3 >40 >3 >17timesMira

Nodeprocessors IntelIvyBridge

AMDOpteron

Nvidia Kepler

64-bitPowerPCA2

Intel KnightsLandingXeonPhi

IntelHaswell CPUindatapartition

IBMPower9CPU

Nvidia VoltasGPUS

IntelKnightsLandingXeonPhi

IntelKnightsHillXeonPhi

Systemsize(nodes)

5,600nodes 18,688nodes 49,1529,300nodes

1,900nodesindatapartition

~3,500 nodes >2,500nodes >50,000 nodes

SystemInterconnect

Aries Gemini 5DTorus Aries DualRailEDR-IB Aries 2nd GenerationIntelOmni-PathArchitecture

FileSystem7.6PB

168GB/s,Lustre®

32PB1TB/s,Lustre®

26PB300GB/sGPFS™

28PB744GB/sLustre®

120 PB1TB/sGPFS™

10PB210GB/sLustre®

150 PB1TB/sLustre®March31,2016 DOEEEEWorkshop 7

HighPerformanceComputing (HPC)USFederalGovernment Investments

• USfederalHighPerformanceComputing (HPC)investments– Madepivotalinvestmentsinthecomputerindustryatcriticaltimes– Duringstabletimes,noinvestmentisrequiredorrequested– Today,wehavereachedacriticalperiod:confluenceofdigitalizationofoureconomyandsocietyandtheendofDennardScaling

• PreviousUSFederalHPCinvestmentsFueledmajorHPCadvances– 1946ENIAC:startofelectronicdigitalcomputing– 1951ERA-1101:technicalcomputing– 1972ILLIACIV:parallelcomputing– 1993CrayT3D:massivelyparallelcomputing– 2004IBMBG:lowpowercomputing– 2011CrayXC30&IBMPOWER7:productivitycomputing– 2023Exascale:energyefficiencycomputing


UncertaintyThreatensUSEconomicGrowth• Theworldhaschanged– technology ischangingatadramatic rate–DennardScaling hasended–EndofMoore'sLawlooming

• The ITmarketplace isalsochangingdramatically–PCsaleshaveflattened–Handhelds dominategrowth,H/WandS/W–HPCvendoruncertainty

• Need todrive innovationsatalllevelsoftechnology–Nodeandsystemdesigns–Systemanddevelopment software–Workflows–Algorithms

March31,2016 9

IDCWorldwideITDataITSpending($m)RowLabels 2015 2016 2017 2018 2019Devices 795,402 807,257 810,643 814,677 809,947EnterpriseHardware 249,493 256,669 264,770 272,898 280,366Software 434,727 464,242 496,264 530,551 568,049Services 668,305 690,734 713,829 738,019 762,725TotalIT 2,147,927 2,218,902 2,285,507 2,356,145 2,421,087

HPCOnly: 2015 2016 2017 2018 2019Revenue$M 11,434 12,327 13,286 14,160 15,262Source:IDC2016

Source: Intel

DOEEEEWorkshop

Challenges– acrosstheITmarketspace• IncreasingPerformance/Value• Efficiency

• Energyefficiency:reduceenergyperoperation(pJ/op)• Hardwareefficiency:massivelyparallelarchitectures,downto

theprocessorlevel• Softwareefficiency:effectivelyexploitH/Wparallelism• Currentefficienciesonconventionalmachinesare<10%formany

real-worldapplications• Memory/Storage

• Makeeffectiveuseofdatamovement (thisisthedominantenergycost)

• Reliability• Successfullycompleteexecutionthroughsystemfailures

• Productivity• ProgrammingenvironmentthatmakesHPCmachinesaccessible

toeveryone• Reducetimetosolution

• Cost /Affordability


FromGigatoExa,viaTera &Peta

1

10

100

1000

1986 1996 2006 2016

RelativeTransistorPerfo

rmance

Giga

Tera

PetaExa

32xfrom transistor32xfromparallelism

8xfrom transistor128x fromparallelism

1.5xfrom transistor670x fromparallelism

Basiccompute loop


Shekhar Borkar,Intel

PerformanceFactors- SLOWER

DefinitionofSLOWERterms

• Starvation• Insufficiencyofconcurrencyofwork

• Impactsscalabilityandlatencyhiding

• Effectsprogrammability

• Latency• Timemeasureddistanceforremoteaccess andservices

• Impactsefficiency

• Overhead• Criticaltimeadditionalworktomanagetasks&resources

• Impactsefficiencyandgranularityforscalability

• Waitingforcontentionresolution• Delaysduetosimultaneousaccessrequeststosharedphysicalorlogicalresources

P = s S ×e(L, O,W)×U(E)×a(R)P – averageperformance(ops)e – efficiency(0<e <1)s – application’saverageparallelism,a – availability(0<a <1)U – normalizationfactor/computeunitE – wattsperaveragecomputeunitR – reliability(0<R <1)


ThomasSterling,IU

HardwareArchitectureImpactonSLOWERmetrics• Starvation• Support for fine grainparallelism andlightweightmessaging, eliminate global barriers

• Latency• Putmemory and computational elements inclose proximity, support message drivencomputation

• Overhead• Reduce times forthreadcreation and contextswitching, support forGAS

• Waitingforcontentionresolution• Increase bandwidths formemory, networks,

andALUswith adaptive scheduling, routing,and resource allocation

Algorithms&Applications


I'm supposed to be a scientific person but I

use intuition more than logic in making basic

decisions.Seymour Cray

Read more at: http://www.azquotes.com/quote/729170

Algorithms&Applications

Thepost-2025applications,softwareandhardwareeffortsdesperately requiretheutilizationofanapplication-drivenco-designprocess

Application-drivenco-designistheprocessbywhich:• Scientificproblemsrequirementsguidethecomputerarchitectureandsystemsoftwaredesign• Technologycapabilitiesandconstraints informformulationanddesignofalgorithms,applicationsandsoftware


HardwareArchitectureResearchAreas

Architecturesdrivenbynew

devices

SpecializationRespondingtoreal-world

heterogeniety

Improvingperformance,efficiency,andproductivity


ArchitecturesdrivenbynewdevicesARPA-e: SWITCHES

SWITCHES projects aimtofind innovativesemiconductor materials,device architectures, anddevice fabrication processesthatwill enable increasedswitching frequency

Researchprograms areaimed at“7nanometer andbeyond”silicon technology anddeveloping alternativetechnologies for post-silicon-erachips using entirely differentapproaches($3Billion investment)

IBMResearch Initiative

Stanford-led skyscraper-stylechip design boosts electronicperformance byfactorofathousand

Carbon nanotube transistors

StanfordN3XTProject


Improvingperformance,efficiency,andproductivity

• Photonicsswitch• HMC• Shekhar’s nodedesign

Silicon photonics (SiP)Bandwidth

NearThreshold Voltage (NTV)EnergyEfficiency

CircuitsPowerReduction


SpecializationIntegrating currentandfutureprocessingtechnology intothecomputing fabric– heterogeneous processing

CoreA

CoreB

CoreC

CoreD

CoreC

CoreD

CoreD

CoreC

MC

MC

NIC


FinalWords• Thesemiconductor industry iscrucialtotheU.S.economy– drivesa>$2T/yr ITmarket• What’safterCMOS?– efficientCMOS&substantially improvedHW/SWarchitectures• There isanexponentially increasing demandforinformationtechnology– newtechnologiesarelimitedbycosttodevelopandmanufacture,notinnovations

• Itisn’tclearwhatistheenablingtechnology forthe“PostCMOS”|“PostMoore’sLaw”epoch– requiresinvolvement frommanydifferentorganizations

• Weneedasignificant investment inHW/SWarchitecture thatutilizes theco-designprocess – $$$

• Weneedtostoptryingtomaketomorrow'scomputers lookandoperate likeyesterday’scomputers– don’tbeconstrainedbythevonNeumannparadigm

• Theultimate challenge isn’t findingthetechnical solutions – it’sacceptingthatachangeinhowwethinkaboutcomputersisrequiredtoenablethenextmajoradvances


ascr overview and perspective - cxrocxro.lbl.gov/pdf/bill_harrod_ascr.pdfascr overview and...

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