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1 Big Data in HPC John Shalf Lawrence Berkeley National Laboratory

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Page 1: Big Data in HPC - Ohio State Universitynowlab.cse.ohio-state.edu/static/media/bofs/isc17/05_Shalf.pdfData Intensive Arch for 2017 (as imagined in 2012)-4-Compute Intensive Arch Data

1

Big Data in HPC

John ShalfLawrence Berkeley National Laboratory

Page 2: Big Data in HPC - Ohio State Universitynowlab.cse.ohio-state.edu/static/media/bofs/isc17/05_Shalf.pdfData Intensive Arch for 2017 (as imagined in 2012)-4-Compute Intensive Arch Data

Evolving Role of Supercomputing CentersEvolving Role of Supercomputing Centers

• TraditionalPillarsofscience• Theory:mathematicalmodelsofnature• Experiment:empiricaldataaboutnature

• Scientificcomputingconnectstheorytoexperiment– Computationalmodelsareusedtotesttheoriesinvolvingcomplexphenomenathat

cannotbematcheddirectlyagainstexperiments– Enablecomprehensionofcomplexexperimentaldatabydistillingcomplexdatadown

tounderstandableinformation• Thereisnoscientific“discovery”withoutatheoryaboutunderlyingmechanism• Thereisnoscientific“discovery”withoutexperimentalvalidationofmodel

TheoryMathematicalModels

ofNature

ExperimentEmpiricalDataabout

Nature

Simulation

Analysis

Mechanism

EvidenceConfirm/Violate

Page 3: Big Data in HPC - Ohio State Universitynowlab.cse.ohio-state.edu/static/media/bofs/isc17/05_Shalf.pdfData Intensive Arch for 2017 (as imagined in 2012)-4-Compute Intensive Arch Data

Breakthrough science will occur at the interface of observation and simulation

- 3 -

MaterialsandChemistry

EnvironmentandClimate

Subsurfacescience Combustion

Cosmology

Newresearchchallengestomaketheseworktogether

Page 4: Big Data in HPC - Ohio State Universitynowlab.cse.ohio-state.edu/static/media/bofs/isc17/05_Shalf.pdfData Intensive Arch for 2017 (as imagined in 2012)-4-Compute Intensive Arch Data

Data Intensive Arch for 2017 (as imagined in 2012)

- 4 -

DataIntensiveArchComputeIntensiveArch

++5TF/socket1-2sockets

5TF/sock8+sockets

64-128GBHMCorStack

1-4TBAggregateChained-HMC

1TB/s

.5-1TBCDIMM(opt.)

200TB/s5-10TBMemoryClassNVRAM

10-100TBSSDCacheorlocalFSn.a.

OrganizedforBurstBuffer

1-10PBDist.Obj.DB(e.g whamcloud)

�/�!(root)!

�Dataset0�!type,space! �Dataset1�!

type, space!�subgrp�!

�time�=0.2345!

�validity�=None!

�author�=JoeBlow!

�Dataset0.1�!type,space! �Dataset0.2�!

type,space!

Spatially-orientede.g.3D-5DTorus

50GB/s/node10TB/s/rack

100TB/s

50GB/sinject10TB/sbisect All-to-Alloriented

e.g.Dragonflyor3T

~1%nodesforStorageGateways

~10-20%nodesforStorageGateways

~1%nodesforIPGateways 40GBeEthernettoDirectfromeachnode

Compute Node! I/O Server!

Compute Node!

Compute Node!

. . .!I/O Server!

Compute Node!

Disks!

Disks!

Disks!

Disks!Metadata Server (MDS)!

Interconnect"Fabric!

RAID!Couplet!

RAID!Couplet!

50GB/sinject0.5TB/saggregate

4GB/spernode

Compute Node! I/O Server!

Compute Node!

Compute Node!

. . .!I/O Server!

Compute Node!

Disks!

Disks!

Disks!

Disks!Metadata Server (MDS)!

Interconnect"Fabric!

RAID!Couplet!

RAID!Couplet!

I/O Server!

. . .!

Compute

On-PackageDRAM

CapacityMemory

On-node-Storage

In-RackStorage

Interconnect

GlobalSharedDisk

Off-SystemNetwork

Page 5: Big Data in HPC - Ohio State Universitynowlab.cse.ohio-state.edu/static/media/bofs/isc17/05_Shalf.pdfData Intensive Arch for 2017 (as imagined in 2012)-4-Compute Intensive Arch Data

Data Intensive Arch for 2017 (as imagined in 2012)

- 5 -

DataIntensiveArchComputeIntensiveArch

++5TF/socket1-2sockets

5TF/sock8+sockets

64-128GBHMCorStack

1-4TBAggregateChained-HMC

1TB/s

.5-1TBCDIMM(opt.)

200TB/s5-10TBMemoryClassNVRAM

10-100TBSSDCacheorlocalFSn.a.

OrganizedforBurstBuffer

1-10PBDist.Obj.DB(e.g whamcloud)

�/�!(root)!

�Dataset0�!type,space! �Dataset1�!

type, space!�subgrp�!

�time�=0.2345!

�validity�=None!

�author�=JoeBlow!

�Dataset0.1�!type,space! �Dataset0.2�!

type,space!

Spatially-orientede.g.3D-5DTorus

50GB/s/node10TB/s/rack

100TB/s

50GB/sinject10TB/sbisect All-to-Alloriented

e.g.Dragonflyor3T

~1%nodesforStorageGateways

~10-20%nodesforStorageGateways

~1%nodesforIPGateways 40GBeEthernettoDirectfromeachnode

Compute Node! I/O Server!

Compute Node!

Compute Node!

. . .!I/O Server!

Compute Node!

Disks!

Disks!

Disks!

Disks!Metadata Server (MDS)!

Interconnect"Fabric!

RAID!Couplet!

RAID!Couplet!

50GB/sinject0.5TB/saggregate

4GB/spernode

Compute Node! I/O Server!

Compute Node!

Compute Node!

. . .!I/O Server!

Compute Node!

Disks!

Disks!

Disks!

Disks!Metadata Server (MDS)!

Interconnect"Fabric!

RAID!Couplet!

RAID!Couplet!

I/O Server!

. . .!

Compute

On-PackageDRAM

CapacityMemory

On-node-Storage

In-RackStorage

Interconnect

GlobalSharedDisk

Off-SystemNetwork

Goal:MaximumComputationalDensityandlocalbandwidthforgivenpower/costconstraint.

Maximizesbandwidthdensitynearcompute

Goal:MaximumDataCapacityandglobalbandwidthforgivenpower/costconstraint.

Bringmorestoragecapacitynearcompute(orconverselyembedmore

computeintothestorage).

Requiressoftwareandprogrammingenvironment

supportforsuchaparadigmshift

Page 6: Big Data in HPC - Ohio State Universitynowlab.cse.ohio-state.edu/static/media/bofs/isc17/05_Shalf.pdfData Intensive Arch for 2017 (as imagined in 2012)-4-Compute Intensive Arch Data

Old/New Conception of Cloud/Datacenters(Simplified Conceptual Model of Interconnect Convergence)

6

TheDatacenter/Cloud

OldConceptionDesignedforexternallyfacingTCP/IP

Nearly100%Std.TCP/IPethernet insideandout

DMZRouter90+%traffic

Backbone

Page 7: Big Data in HPC - Ohio State Universitynowlab.cse.ohio-state.edu/static/media/bofs/isc17/05_Shalf.pdfData Intensive Arch for 2017 (as imagined in 2012)-4-Compute Intensive Arch Data

Old/New Conception of Cloud/Datacenters(Simplified Conceptual Model of Interconnect Convergence)

7

TheDatacenter/Cloud

OldConceptionDesignedforexternallyfacingTCP/IP

Nearly100%Std.TCP/IPethernet insideandout

DMZRouter90+%traffic

Backbone

Cloud/Datacenter80+%oftraffic

High-PerformanceFluidCenterLowOverhead,HighBandwidth,Semi-custominternalinterconnect

CrunchyTCP/IPExterior

NewConceptionNeedtoHandleInternalDataMining/Processing

Designfor80+%internaltraffic


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