Supercomputadores e a computação híbrida

Supercomputadoreseacomputaçãohíbrida

HaroldoFragadeCamposVelhoE-mail:[email protected]:www.lac.inpe.br/~haroldo

§  O que è “super-computador”?

§  Demanda por processamento de alto desempenho

§  Computação híbrida §  Processadores e co-processadores §  Alternativas abandonadas §  CPU + GPU (exemplo com o BRAMS) §  CPU + FPGA (caso em assimilação de dados) §  Futuro do computador/computação

§  Processamento paralelo + Inteligência Computacional §  Cooperação: COPDT-INPE e IEAv-DCTA §  Cooperação: Cátedra de meteorologia – DECEA e UFRJ

Brevedescrição…

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Haroldo F. de Campos Velho – INPE Pesquisador Sênior / Computação Científica

ÁREAS DE PESQUISA:

•  Problemas Inversos (várias aplicações, melhores metodologieas)

•  Assimilação de Dados

Novo método: baseado em redes neurais artificiais

•  Parametrização de Turbulência Atmosférica

Resultados: Teoria de Taylor em turbulência em nuvens

Modelo de crescimento para comada limite conventiva

Turbulência e dinâmica cosmológica

§  Em termos simples, é um equipamento com grande capacidade de cálculos aritméticos (*).

(*) A capacidade de cálculo é quantificada pelo número de operações (multiplicações) de ponto flutuante (**) por segundo (“flops”). (**) Os computadores trabalham com representação de números inteiros, ponto fixo e ponto flutuante.

Super-computador

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Colossus (UK - 1943) https://en.wikipedia.org/wiki/Colossus_computer

ENIAC (USA - 1945) Foto: https://en.wikipedia.org/wiki/ENIAC

§  1ª Geração (1945, Eniac): válvula

§  2ª Geração (IBM 1404): transistor

§  3ª Geração (IBM/360 – computadores pessoais): circuito integrado

§  4ª Geração: microprocessadores (VLSI)

§  Processamento vetorial: supercomputadores

Super-computador

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1976: É o ano do lançamento do Cray-1 (horseshoe-shape design)

Foto: https://en.wikipedia.org/wiki/Cray-1

§  1ª Geração (1945, Eniac): válvula

§  2ª Geração (IBM 1404): transistor

§  3ª Geração (IBM/360 – computadores pessoais): circuito integrado

§  4ª Geração: microprocessadores (VLSI)

§  Processamento vetorial: supercomputadores

Super-computador

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1976: É o ano do lançamento do Cray-1 (horseshoe-shape design)

1980: processamento distribuído Connection-machine (CM-5)

Foto: https://en.wikipedia.org/wiki/Connection_Machine

Multi-processing machine with distributed memory

CPTEC-INPE: Cray XE6

1280nodes

30720cores

Supercomputermachines:mul;-core

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CPTEC-INPE:Computersystems

n  UNA(~800processors,1600cores)

Other alternative for hybrid computing

CPU + GPU.

Cluster of computers GPU (Fermi NVIDIA)

Cluster of computers GPU (Firestream AMD)

Demandaporaltodesempenho

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§  MathemaGcalmodel(hardequaGons:non-linear)

Movement Equation (momentum)

Continuity Equation (mass)

Thermodynamic equation (energy)

01

=∂

∂+−

x

pfv

dt

du

ρ0

1=

∂

∂++

y

pfu

dt

dv

ρ0

1=

∂

∂

ρ++

z

pg

dt

dh

( ) ( ) ( ) 0=ρ∂

∂+ρ

∂

∂+ρ

∂

∂+

∂

ρ∂h

zv

yu

xt

( )ρTfp =( ) ( )

)(cteRg

T

Tf≡=⇒

ρρ ( )Tgp ρ=

RTp ρ=( )

dt

dq

dt

dp

dt

dTCv =+

ρ/1

Porqueum“up-grade”dosistemadecomputação?

SantaCatarina(BR)November/2008

135deaths/78,000homeless

Prec. Ac. 20 a 23/Nov/2008 12Z – 3 days : Eta 20 km

Prec. Ac. 20 a 23/Nov/2008 12Z – 3 days: Eta 5 km

20km

5km

20km

RioandAngradosReis,31/Dec/2009

(predic;onfor24h)5km

Precipitation Total (mm) – Eta 20 km

Precipitation for24 horas – Eta 5 km

CatarinaHurricane:imagesfromspace

Fotos: https://pt.wikipedia.org/wiki/Furacao_Catarina

210km 105km

63km 20km

CatarinaHurricane:differentsresolu;ons(Globalmodel)

CatarinaHurricane:differentsresolu;ons(Globalmodel)

Demandaporaltodesempenhon  CiênciadosDados

Heart disease

Solar physics

Plane waves

Demandaporaltodesempenhon  CiênciadosDados

Techniques :

•  clustering •  data summarization •  detecting anomalies •  analysing changes •  finding dependency networks •  learning classification rules

Computaçãohíbrida

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Forecasts Scores ECMWF

Dataassimila;on

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Very important inverse problem

Determining the initial condition

Dataassimila;on

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Very important inverse problem

Determining the initial condition

§  Temperature: ANN assimilation experiment

LETKFneuralnetwork

True

Results from Rosangela Cintra PhD thesis (2011)

Data assimilation: an essential issue

Numericalexperiment:LEKFandANN

AtmosphericgeneralmodelcirculaGon(spectralmodel):3DSPEEDY(SimplifiedParameterizaGonsprimiGvEEquaGonDYnamics)

Gaussiangrid:96x48(horizontal)x7levels(verGcal)=T30L7Totalgridpoints:32,256Totalvariablesinthemodel:133,632ObservaGons:(00,06,12,18UTC)–radiosonders“OMMstaGons”ObservaGons:12035(00and12UTC)=415x4x7+415ObservaGons:2075(06and18UTC)=415x5(onlysurface)

LETKFmethod ANNmethod

04:20:39 00:02:53

Execu;on;me

hours:minutes:seconds

Data assimilation by NN: hardware components

FPGA

GPU vs FPGA:

1.  GPU: have more investiments from computer community.

2.  FPGA: Axel Project (system CPU + GPU + FPGA), N-body simulation, the FPGA is faster than GPU.

3.  FPGA uses 2.7 up to 293 less energy than GPU.

2010

2010

Hybrid computing with FPGA

Perceptron-NN for the Cray XD1

Shallow water 2D for ocean circulation

Process Time(μs)

Sodware 121709FPGA(Total) 209187


Process Time(μs)

Sodware 121709CPUtoFPGA 181365

FPGA 2FPGAtoCPU 9455FPGA(Total) 209187


3 Nodes FPGA (2014)

3 Nodes FPGA (2015)

4 Nodes ARM (2015)

8 Nodes 2013 (1, 2, …, 7)

4 Nodes HP (storage)

5 Nodes ARM (2014)

Nodes (3) FPGA (2014): 2 proc. Intel 12-cores 1 GPU K20 1Xeon Phi 60-cores 1 FPGA Virtex-7

Nodes (8) (2013): 2 proc. Intel 10-cores 2 GPU K20 FPGA Virtex-6

Nodes (3) FPGA (2015): 2 proc. Intel 12-cores 1 GPU K80 1 Xeon Phi (Knights) 60-core 1 FPGA Virtex-7

Nodes ARM (2014): 6 AppliedMicro 8-core (Calxeda: we can’t buy)

Nodes ARM (2015): 8 Cavium ThunderX 48-cores

Cluster LACibrido

Cooperação COPDT-INPE + IEAv-DCTA

§  Haroldo F. de Campos Velho (*) INPE: National Institute for Space Research – Brazil E-mail: [email protected]

§  Elcio H. Shiguemori DCTA: Department of Aerospace Science and Technology – Brazil E-mail: [email protected]

Navegaçãoautônomadedrones

Drone trajectory correction based on aerial images information

Planned Inertial

navigation

Image correction

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Computer vision by image segmentation

Dronenaviga;onwithoutGNSSsignal

§  Examples

Original image True Canny

RBF CNN MLP

Available for download: www.epacis.net/jcis/PDF_JCIS/JCIS11-art.01.pdf

MPCA:Mul;-Par;cleCollisionAlgorithm

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FindinganOPTIMALneuralnetwork

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§  Supervised neural network: Multi-Layer Perceptron (MLP)

MPCA solution

#hiddenlayers

#neuronslayer-1

#neuronslayer-2

#neuronslayer-3

AcGvaGonfuncGon

MomentumraGo

LearningraGo

Parameters Vallue

Numberofhiddenlayers |1||2||3|

Numberofneuronsforeachlayer |1|...|32|

LearningraGo |0|...|1|

Momentum |0|...|0.9|

AcGvaGonfuncGon |Tanh||Log||Gauss|

UAV positioning algorithm: embedded system

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1.  Edge patterns

a)  24 are selected

b)  MLP-NN classifies

the other patterns

c)  Classification table


Pré-processing Neural Network

Satellite image

Drone image

Correlation

UAV estimated position

Image segmentation and correlation

Drone positioning algorithm

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Georeferenced image

Extracting Edges (NN)

UAV image acquisition

Extracting Edges (NN)

correlation

UAV position

Image from the Google Earth


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1.  Drone trajectory correction

Without GNSS signal: edge extraction


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1.  Trajectory correction: edge extraction

Edgeextrac;on(methods)

#correctedpointswitherror<10m

CPU-;me(seconds)

Canny 197 0.25Sobel 296 0.29

NN-sodware 414 5.00(20xslower)


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CPU-;me(seconds)

Canny 197 0.25Sobel 296 0.29

NN-sodware 414 5.00(20xslower)


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** Speeding up the processing:

a)  Removing computation: from activation function to LUP

b)   Use hybrid computing: CPU + FPGA

CPU: Rasberry PI B-1 FPGA: Xinlix Spartan 6 LX9


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CPU-;me(seconds)

Canny 197 0.25Sobel 296 0.29

NN-sodware 414 5.00(20xslower)NN-FPGA_Raspberry 414 0.58(2xslower)

NN-FPGA_Zybo 414 0.13(2xfaster)

Data fusion: Combining two drone positioning estimations: INS signal + Computer vision Method: Neural Network (emulating Kalman filter

and high precision GPS)


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Data fusion: INS signal + Computer vision


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INS measurements

Positioning 1

Computer vision (CV)

Positioning 2 NN fuser

Drone position

Image aquisition

Data fusion: INS signal + Computer vision


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Positioning 1 Positioning 2 NN fuser

Drone position

Image aquisition

§  Bayesian strategy – filtering

1.  Kalman filter a)  Linear stochastic process b)  Gaussian statistics

2.  Particle filter a)  Applied to non-linear process b)  Applied for non-Gaussian statistical models c)  Kernel of Particle Filter:

Bayesian filters

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!"!#$!"!#$!"!#$)()()( priori a

1

likelihoodposteriori a

1 )|()|()|(

nwnwnw

nnnnnn YwpwypYwp −− ∝

§  Non-extensive particle filter:

§  Tsallis’s non-extensive distribution

§  Choice for using Gaussian distribution can be justified by the Central Limit theorem.

§  However, there is another attractor on the distribution space.

§  This is the Levy-Gnedenko’s central limit theorem.

§  For the case of Tsallis’ distributions:

Bayesian filters

ρq (x) =G(x) q < 5 3

Lγ (x) q > 5 3

⎧⎨⎪

⎩⎪

§  Likehood function: Tsallis’ distribution (non-extensive formalism of thermodynamics)

§  where “q” is the non-extensive parameter:

to be estimated by a secondary process

Adap;veNon-extensivePar;cleFilter

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Data fusion by Non-Extensive Particle Filter (NEx-PF)


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Data fusion by Non-Extensive Particle Filter Uncertainty quantification

Cátedrademeteorologia:DECEAeUFRJ

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Cátedra de Meteorologia Aeronáutica UFRJ/UNIFA/DECEA


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MembrosdoComitê:ü  GutembergBorgesFrança,PhD.(Presidente)–IGEO-LMA-UFRJü  HaroldoFragadeCamposVelho,DSc.–LAC-INPEü  GilbertoFernandoFisch,DSC.–IAE-COMAERü  FranciscoLeiteAlbuquerqueNeto,DSc.–IGEO-LMA-UFRJü  FranciscoPinheiroGomes–ComandantedaCIMAER

AvaliadoresConvidadosparaVCSCMA:n  NelsonFranciscoF.Ebecken,DSc.–COPPPE-UFRJn  HugoAbiKaramDSc.–IGEO-UFRJn  WallaceF.MenezesDSc.–IGEO-UFRJn  AnaMariaBuenoNunesDSc.–IGEO-UFRJn  JulianaAnochiDSc.–CPTEC-INPEn  AudalioRebeloTorresJuniorDSc.UFMA


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Futurodocomputador

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Há algumas apostas para o futuro:

a)  Um computador fotônico!

b)  Computação quântica!

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Muito Obrigado !!

Supercomputadores e a computação híbrida

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