THE PON-SCOPE GRID INFRASTRUCTURE

P.I. – Giuseppe Marrucci

Astrophysics – Longo

Electromagnetism - Franceschetti

High energy Physics - Merola

Computer sciences - Russo

Mathematics - Murli

Materials and environment - Barone

Medicine and Genetics - Salvatore

Social and Human sciences - Zollo

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The four PON’s

THE PON-SCOPE GRID INFRASTRUCTURE

The Metropolitan Grid VST-Cen

The core of the infrastructure

Scala: 1:100

Liv.1 ed G

CentroCentroPON S.CO.P.EPON S.CO.P.E..

DDipartimento di ipartimento di SScienze cienze FFisicheisicheINFNINFNEdificio GEdificio G

Cabina elettricaG.E. 1 MW

Tier2Tier2––ATLASATLASPrototipo Prototipo SCoPESCoPE

CAMPUS GRIDMSA

DMADiChi

DSF INFN

C.S.I.

GARR

Fibra ottica

PON PON S.CO.P.ES.CO.P.E..

CAMPUS GRIDMSA

DMADiChi

DSF INFN

C.S.I.

GARR

Fibra ottica

PON PON S.CO.P.ES.CO.P.E..

LAN INFN

1 Gbps

1 Gbps1 Gbps

1 Gbps

2 x1Gbps

100/200 Mbps

10 Gbps

1 Gbps

10 Gbps

1 Gbps

N x1 Gbps

2.5 GbpsCampus Grid

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MAN UNINA

Siti Campus Grid

Tier 2Pon UniNA

LAN INFN

1 Gbps

1 Gbps1 Gbps

1 Gbps

2 x1Gbps

100/200 Mbps

10 Gbps

1 Gbps

10 Gbps

1 Gbps

N x1 Gbps

2.5 GbpsCampus Grid

GARR

MAN UNINA

Siti Campus Grid

Tier 2Pon UniNA

250 CPU quadri-processoriXeon Quad-core EM64T Clovertown E5320, 1.86 GHz, 2x4MB cache, supportati da 8

GB di RAM, 1066 FSB, con 2 dischi SAS 36GB, controller RAID SAS 3 Gb/s. Storage: 100 HDD FC / 300GB (30TB) + 100 HDD SATA 2 da 500GB (50TB),

expandable up to 120TB & 240 HD4 cpu dual core 16GB RAMRetenon blocking with 240 porte 10/20 gigabit infiniband full redundant, 12 PS “banda

aggregata” 12 Gigabit, 2 gateway fiber channel-infiniband with 2 gates FC4 - 8 gigabit. 230 nodi in infiniband; 20 blade forfibre channel connectivity.

PON-SCOPEPreexistence

Campus GRID AstrogridSM LHC Tier 2 (INFN)

For a total 0f 1 k-CPU’s

Astrophysics in GRID-SCOPE• Astroparticles

• Simulations of CR and induced showers – F. Guarino• Simulation of NEMO - G. Barbarino• Simulations of Gravitational Waves from various astrophysical sources

VIRGO – L. Milano• Cosmology

• Simulations of cosmic string signatures on CMB – G. Longo• Primordial Nucleosynthesis – G. Miele

• VO related activity• Pipeline for survey data (VST-Tube + 2dphot) – de Carvalho, Grado, La

Barbera, Longo• Integration of ASTROGRID with GRID-SCOPE (VO-Tech broker)• Extractor (image segmentation) – O. Laurino• Data Mining • Supervised and unsupervised data mining tools

Photometric redshiftsQSO and AGN search &

classification

Cluster identification & characterization

G. Longo (PI) M. Brescia (INAF - PM)

S. Cavuoti (applications) A. Corazza (models and algorithms)R. D’Abrusco (applications) G. d’Angelo (documentation, GRID)N. Deniskina (GRID – VO interface) M. Garofalo (applications)O. Laurino (System, Applications) A. Nocella (UML software engineering)G. Riccio (Applications) B. Skordovski (models)

External Members

C. Donalek (Caltech-CRAC) G. Djorgovski (Caltech-CRAC)

The VO-Neural Teamhttp://people.na.infn.it/~astroneural/

THE PROBLEM

Grid Launcher (N.V. Deniskina) allows to launch GRID-applications using the ASTROGRID Workbench and to transfer the results from the GRID-UI to the data storage of Astrogrid.

• VO is an environment open to a wide community & the GRID IS NOT (access through personal certificates)

• Time consuming tasks cannot be run from VO users unless the security problem is solved (or “fooled”….)

GRID-Launcher (N.V. Deniskina)

Workbench of the user

GRID SCOPE

AstroGRIDMySpace

(data storage)

UI

RB

SE

WN CE

data

result

result

The workflow of the job is following:

1. “Grid_launcher“ a) takes the user input from Workbench of Astrogrid; b) collects all files, tabs and programs needed; c) wraps them in an archive and sends it to the Scope-GRID UI. (The Authentication on Scope is done by public keys exchange).

2. The Scope UI receives data and JDL program from "GRID_launcher", unpacks them and translates them to Grid job format.

3. Once GRID job jdl file is ready, "GRID_launcher" starts it in Grid (from a AstroGrid node); periodically checks the status; and then (when job is finished) retrieves the results.

5. "GRID_launcher" receives the data archive, unpacks it and puts the results into the “MySpace” data storage of AstroGRID.

GRID launcher has been implemented and tested on :

•VONeural_MLP: supervised clustering

•VONeural_SVM: supervised clustering

•Sextractor: for survey data processing

•SWARP - is a program to resample and co-add FITS images using any arbitrary astrometric projection defined in the WCS standard.

Tests on scientific cases (done and in progress)

•Photometric redshifts of SDSS galaxies (D’Abrusco et al. ApJ, 2007) uses VONeural_MLP

•Classification of AGN in UKIDS+SDSS data (D’abrusco et al. 2008, MNRAS in press) Using VONeural_SVM

•Search for LSB in SDSS data using NEXT (in progress, Laurino)

SDSS-DR4/5 – GG

training validation Test set 60%, 20%, 20%

MLP, 1(5), 1(18)

0.01<Z<0.25 0.25<Z<0.50 99.6 % accuracy

MLP, 1(5), 1(23) MLP, 1(5), 1(24)

rob = 0.206 rob = 0.234

Interpolationof systematic errors

Interpolationof systematic errors

Phot Z for SDSS General Galaxy sampleat least 30 experiments (10-12 h/each)training on 350.000 objects 12 features

results for 32.000.000 objects

σz = 0.02

Redshifts for 30 million galaxies

Photometric redshifts for 30 million SDSS galaxies

Looking for AGN candidates in SDSS+UKIDS

3-D PCA

PPS

SDSS UKIDSS

preprocessing

clustering

labeling

BoK

results

PPS/SVM

NEC

dendrogram

Cluster optimization

1 experiment/one node ca. 11 days

Looking for AGN candidates in SDSS+UKIDS

3-D PCA

Applicazione 2 con SVMApplicazione 2 con SVMMiglior Risultato: 81.5%Miglior Risultato: 81.5%

PON-SCOPE GRID Infrastructure (110 nodes PON NA-CA-CT)

lg2(gamma)

lg2(C)

What types of programs we plan to launch to GRID?

There are typical astronomical tasks that need long-time calculations:

1) all types of numerical simulations.,2) image reduction (+, -, statistic, calibration).,3) search of astronomical solution (astrometry calibration).,4) photometry calibration.,5) determination of luminosity function of the galaxies., 6) photometric redshift.,7) source selection (clustering, determination of the type of the object) (mushroom as an example). , These tasks can be as AG programs (or other VO programs ) inside of AG as user's programs.

These tasks can be the sequence of programs (not one program).