I risultati scientifici del progetto SCoPEI risultati scientifici del progetto SCoPESCoPE Scientific ResultsSCoPE Scientific Results

L. Merola

Workshop finale dei Progetti Grid del PON "Ricerca" 2000-2006 - Avviso 1575

Catania, 10-12 febbraio 2009

Summary

• The “University of Napoli Federico II”• The SCoPE Project and Research Areas• The SCoPE Data Center & Metropolitan Network• Results from Material Sciences• Results from Life Sciences• Results from MicroCosm and MacroCosm Sciences• Results from Middleware applications• Beyond SCoPE

Founded in 1224

Second largest university in Italy

More tha 3.000 professors and researchers

More than 10.000 new student per year

Involved in the most strategic areas of scientific research, e-Science and technology

The “University of Napoli Federico II”The “University of Napoli Federico II”

The SCoPE project and Research AreasThe SCoPE project and Research Areas

SCoPE : SCoPE : Sistema Cooperativo distribuito ad alte Prestazioni per Elaborazioni Scientifiche Multidisciplinari

(Distributed Cooperative High Performance System for Multidisciplinary Applications)

Objectives:

Innovative and original software for fundamental scientific research. High performance Data & Computing Centre for multidisciplinary

applications. Grid infrastructure and middleware INFNGRID LCG/gLite:

Compatibility with EGEE middleware Interoperability with the other three PON 1575 projects and SPACI in

GRISU’ Integration in the Italian and European Grid Infrastructure.

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Research areas:

MicroCosm and MacroCosm SciencesMicroCosm and MacroCosm Sciences Materials and Environment SciencesMaterials and Environment Sciences Life SciencesLife Sciences Social SciencesSocial Sciences MiddlewareMiddleware

19 Departments and Research Institutes128 Professors and Senior researchers from UniNA + many others from Research Institutes (INFN, etc.) 35 Young researchers (assegni di ricerca) 28 Technology specialists (co.co.co.)

MacroareaScie

nzeM.F.N.

MacroareaMedicina

Struttura centrale

(CSI)

Area delleScienze MM.FF.NN.

(Campus – GRID)

Area delle Scienze Ingegneristiche

Area delle ScienzeMediche e

Biotecnologie

Dip.Informatica e Sistemistica

Dip. Ingegneria Elettrica

Dip.Ingegneria Elettronica e delle Telecomunicazion

i

Dip. Biochimica e Biotecnologie

mediche

MacroareaMedicina

Area delle Scienze

Umane e Sociali

Dip.Matematico-

Statistico

Dip. di Scienze

Fisiche

Dip.Ingegneria Chimica

Organizzazioni esterne

ma collegate

INFN Sezione di

Napoli

CNR-SPACI Napoli

INFM Unità di Napoli

CEINGE

CRIAI

ARPA

CINI

Dip. di Analisi e Progettazione

Strutturale

Dipartimentodi SociologiaCentro di

Eccellenza per lo Studio delle Malattie Genetiche

Dip. di Matematica

Dip. di Chimica

Astrophysics group• Search for gravitational waves• Data mining and visualization of astronomical massive data setshttp://virgo.infn.it and http://www.eurovotech.org/

Particle Physics (subnuclear physics) group

Study of proton-proton interactions at the CERN-LHC Large Hadron Collider and implementation of a Tier2 Data Centre for large scale, data intensive Montecarlo simulations and data analysis

http://lxatlas.na.infn.it

Bioinformatics group

Study of genome sequence analysis and image analyses for

cell motility and other dynamical phenomena

http://bioinfo.ceinge.unina.it/ and http://www.ceinge.unina.it

Numerical Mathematics and Scientific computing group

Study and design of algorithms for distributed scientific applications

and implementation on HPC infrastructures

Statistical mechanics groupStudy of applications of statistical mechanics to complex systemshttp://smcs.na.infn.it/

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Electromagnetism and Telecommunication group

Study of models and measurements of electromagnetic field in the Napoli metropolitan area

http://www.diet.unina.it/gruppoCE/GruppoEl.html

Material Science group

Study of molecular dynamics and optical properties of nano-structured materials

http://lsdm.campusgrid.unina.it/ and http://www.nanomat.unina.it

Soft Matter Engineering group

Study of models and simulations of the flux of micro-structured materials http://wpage.unina.it/p.maffettone

The SCoPE Data CenterThe SCoPE Data Center

33 Racks (of which 10 for Tier2 ATLAS)304 Servers for a total of 2.432 processors 130 TeraByte storage

2 remote sites: Fac. Medicine: 60 TB storage Dep. Chemistry: 8 server multiCPU (4-proc)

INGRESSO

Data CenterData CenterSCOPESCOPE

Control RoomControl RoomSCOPESCOPE

Biologia

Fisica

SCoPE DataCenter SCoPE DataCenter & Tier2 ATLAS& Tier2 ATLAS

Cabina elettrica 1 MW1 MW

Control room

Chem. Med.

Data Center

Low latency network2432 core

CAMPUS GRIDMonte Sant’Angelo

DMADiChi

DSF DSF INFNINFN

C.S.I.

GARR

Fibra ottica

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

GARR2.4 Gb/s

Centro Centro S.CO.P.ES.CO.P.E..

DDipartimento diipartimento di SScienzecienze FFisicheisicheSezioneSezione INFN INFN

Cabina elettricaG.E. 1 MW

Control roomControl roomS.CO.P.ES.CO.P.E..

The Metropolitan NetworkThe Metropolitan Network

SUSPENSION OF PARTICLES IN LIQUIDSSUSPENSION OF PARTICLES IN LIQUIDSIntensive simulations in 2D & 3D FEM

MotivationMotivation• Suspensions of particles in liquids are a class of materials relevant in a huge

variety of applications, e.g.– Polymer melts with fillers – Biomedical materials– Food – Cosmetics– Detergents

• This variegate spectrum is due to the differences in particle concentration, mechanical properties, shape, and size.

• Particles suspended in viscoelastic media are known to develop structures when sheared at sufficiently high shear rates.

Results from Material SciencesResults from Material Sciences(Dip. Ingegneria Chimica – Dip. Matematica e Applicazioni

AimAimCharacterize the flow behavior of dilute and semidilute suspensions of

rigid spheres in viscoelastic media in confined geometries.

• Sw libraries:– BLAS/LAPACK – METIS

• To solve linear systems emerging from FEM, following solvers have been used/compared:– HSL library (commercial, direct solver, sequential)– MKL Gmres (included in MKL, iterative solver, sequential)– Sparsekit (free, iterative solver, sequential)– Pardiso (included in MKL, direct solver, OpenMP)– Mumps (free, direct solver, MPI)– Petsc (free, iterative solver, MPI)

• 3D Two Particles flux in confined geometries Newtonian Fluid Non Newtonian Fluid

Results from Material SciencesResults from Material Sciences(Dip. Scienze Fisiche)

From single-particle energy levels Tight-Binding sw package to study optical properties (absorbance, reflectivity, refraction index, photoluminescence) of semiconductor nanocrystals vs. shape and dimensions.Intensive computing and RAM requirements (10^3-10^5 rows matrix diagonalization) .

Medintz et al. Quantum dot bioconjugates for imaging, labelling and sensing, Nat. Mat. 4, 435 (2005)

ProblemProblem

H.Hofmeister, F.Huisken and B.Kohn, Eur. Phys. J. D 9, 137 (1999).

Nanocrystal (real) Nanocrystal (model)

3 nm

Tight Binding = wave functions as linear combination of atomic orbitals.Advantages:1) Hamiltonian Matrix of small dimension: only 4 orbitals per single Si atom;2) High level of sparsity (>95%): the diagonalization time scales linearly with dimension ;3) Symmetries: Hamiltonian Matrix decomposed in independent blocksaccording to the irriducible representations of the simmetry group.

ResultsResults

Absorbance of InAs colloidal nanocrystals vs. dimensions

Line: this model (Trani et al. Phys. Rev. B 76, 085302 2007)

Points: experiment(Yu et al. J. Phys. Chem. B 109, 7084 2005)

Workshop SCoPE - Stato del progetto e dei Work PackagesSala Azzurra - Complesso universitario Monte Sant’Angelo

21-2-2008

Language: Fortran 90 Libraries: BLAS, LAPACK, math lib (MKL) for matrix

manipulation

Package tested on: linux AMD Athon a 1.6 GHz, AMD64 a 3.2 GHz, Alpha True64, Intel Xeon.

Compilers: Intel, PGI, Gfortran HPC needed: Parallelization under development Web interface: http://www.nanomat.unina.it SCoPE portal

SoftwareSoftware

Computational modelling of molecular and supra-molecular systems

Recent developments: theorytheory: both classical (MM) and quantistic (QM); algorithmsalgorithms (linear scaling methods) and technologytechnology. Intensive simulation processes. Efficient description for:• Average dimension structures• Periodic systems

Problematic areas:• large non periodic systems:large non periodic systems:-- Nanoparticles- Nanoparticles-- biomacromolecules- biomacromolecules-- “defects” of materials- “defects” of materials

Results from Material (and Life) SciencesResults from Material (and Life) Sciences(Dip. Chimica)

Soft MatterSoft Matter

Application (example)Application (example) Dynamic ADMP (Dynamic ADMP (Atom centered Density Atom centered Density Matrix PropagationMatrix Propagation)) / ONIOM / ONIOM ((Our own Our own N-layered Integrated molecular Orbital + N-layered Integrated molecular Orbital + Molecular MechanicsMolecular Mechanics) on ionic channel of on ionic channel of the gramicidine A.the gramicidine A.In ADMPIn ADMP the electronic degrees of freedom the electronic degrees of freedom have a “unreal” mass and propagate from have a “unreal” mass and propagate from step to step.step to step.

- nella dinamica ADMP (come nella congenere dinamica Car-Parrinello), anche i gradi di libertà nella dinamica ADMP (come nella congenere dinamica Car-Parrinello), anche i gradi di libertà elettronici hanno associata una massa fittizia e vengono propagati da uno step all’altro (Lagrangiana elettronici hanno associata una massa fittizia e vengono propagati da uno step all’altro (Lagrangiana estesa).estesa).

- nell’ADMP, i gradi di libertà elettronici sono codificati da una - nell’ADMP, i gradi di libertà elettronici sono codificati da una matrice densitàmatrice densità espressa in termini di espressa in termini di funzioni di base centrate sugli atomifunzioni di base centrate sugli atomi..

- nella dinamica classica, le posizioni e i momenti nella dinamica classica, le posizioni e i momenti dei nuclei vengono propagati da uno step al dei nuclei vengono propagati da uno step al successivo: il campo di forze consente di successivo: il campo di forze consente di calcolare l’energia e le accelerazioni punto per calcolare l’energia e le accelerazioni punto per punto.punto.

24

Real-time forecast of the e.m. field on the metropolitan area (Napoli)

Numerical solvers for the optimization of planning of wireless mobile phones networks. Interpolation on the metropolitan area of the e.m. exposure, starting from a limited number of sensors. Application to a project for a call- center for public information

Livello del campoLivello del campo

Results from Material & Environment SciencesResults from Material & Environment Sciences(Dip. Ingegneria Elettronica e delle Telecomunicazioni)

P.zza Plebiscito Palazzo Reale Study of the environmental impact of e.m. fieldStudy of the environmental impact of e.m. field

Interests of UniNA towards a Interests of UniNA towards a Virtual Organization in Virtual Organization in MATMATererIIalal SSciencecience SSimulation andimulation and EEngineeringngineering ( (MATISSEMATISSE))

(ref. Prof. Domenico Ninno)STRUTTURA TEMA METODI E CODICI

Dip. Scienze Fisiche Fisica delle nanostrutture, ossidi, grafeni e sistemi ibridi organico inorganico

Teoria del funzionale densità – Basi localizzate di Wannier- Codici open source: Quantum Espresso, Wannier 90

Dip. Scienze Fisiche Sistemi a forte correlazione elettronica (ossidi di metalli di transizione)

Diagonalizzazione esatta Lanczos e/o Jacobi-Davidson – Quantum Montecarlo diagrammatico

Dip. Scienze Fisiche Meccanica Statistica dei vetri, mezzi granulari, superconduttori e sistemi biologici

Montecarlo e dinamica molecolare – Codici propri

Dip. Scienze Fisiche Meccanica Statistica dei vetri di spin, vetri strutturali, gel e colloidi,materiali granulari

Montecarlo e dinamica molecolare – Codici propri

Dip. Scienze Fisiche Modelli d’interazione tra biomolecole e laser

Dinamica molecolareMolpro, Gaussian

Dip. Chimica Proprietà chimico-fisiche di materiali nanostrutturati, solidi e superfici

Teorie ab initio, teoria del funzionale della densità, basi localizzate e onde piane, metodi multiscalaCodici non open source: Gaussian, Crystal, Molpro, Materials studio, Vasp, dlpoly. Codici open source: Gamess-us, quantum espresso, gulp

Dip. Ingegneria Chimica Reologia e fluidodinamica di sospensioni solide in fluidi viscoelastici

Simulazioni agli elementi finiti – Codice proprio (TFEM)

Dip. Ingegneria dei Materiali e della Produzione Diffusione di sostanze a basso peso molecolare in sistemi macromolecolari

Dinamica molecolare (Materials studio)

Dip. Ingegneria dei Materiali e della Produzione Nucleazione e crescita di microparticelle per precipitazione in fluido supercritico

Codici propri interfacciatI con FLUENT

Dip. Ingegneria dei Materiali e della Produzione Nanofluidica. Moto di particelle e macromolecole in nanochannels

Codici propri

Dip. Ingegneria dei Materiali e della Produzione e Dipartimento di Ingegneria strutturale

Comportamenti meccanici di polimeri e compositi e problemi di omogeneizzazione

Codici propri interfacciati con ANSYS

Results fromResults from Life SciencesLife Sciences(Fac. Medicine & CEINGE - Biotecnologie Avanzate)

Large DataBase for genomic sequences of bacteria, vertebrates, trees.

Applications CPU & Data intensive: • Identification and characterization of nucleotide sequences

H. Sapiens

M. Musculus

CSTs

DG-CST (DISEASE GENE CONSERVED SEQUENCE TAGS), A DATABASE OF HUMAN–MOUSE CONSERVED ELEMENTS ASSOCIATED TO DISEASE GENES.More than 60.000 sequences identified associated to diseases.

DNA

Proteina

RNA Strutturato

mRNA

biological function

Applications CPU & Data intensive: • Gene mining

G. Paolella Napoli, 28/5/ 2008 28

Gene mining

Lunghezza trascrittoma (bps) 14,609,025

Geni codificanti proteine 299

Pseudo-geni 26

Geni codificanti ncRNA 39

Frammenti di sequenza 291,589

Processi 1,167

Cromosoma 21

0

20

40

60

80

100

120

140

160

0 500 1000 1500

Processi (ogni processo = 250 sequenze)

Gio

rni

Execution time on GRID

1 WN

Grid

Large DataBase for genomic sequences of bacteria, vertebrates, trees.

G. Paolella Napoli, 28/5/ 2008 29

Length 46,944,323 bps

Total genes 392

> miRNA Genes 10

> rRNA Genes 3

> snRNA Genes 7

> snoRNA Genes 8

> miscRNA 8

Found known RNAs 9

Transcriptome length 14,609,025

Automatic annotation of genomic sequences: Search for functional RNA structuresSequences potentially transcribed has been split in overlapping fragments of 150 bp length.

290,904 sequences

Results

G. Paolella Napoli, 28/5/ 2008 30

Bioinfo portal

G. Paolella Napoli, 28/5/ 2008 31

HPCon

ClusternodesG

ateway

iPage

image

area

data + images

page

iPaneiPaneiPane

proc-steps

IPROC architecture

32

Grid-aware HPC

for medical images:

management processing visualization

PProblem roblem SSolving olving EEnvironment nvironment MedIGridMedIGrid

Results fromResults from Middleware for applicationsMiddleware for applications(Dip. Matematica e Applicazioni)

(see talk by Vania Boccia)

Multilevel adaptive algorithms on MP multicore architectures(poster; abstract no. 92)

(Dip. Matematica e Applicazioni)

1st level: message passing among CPUs of a blade server2nd level: multithreading among cores of a single multicore CPU

memory

coresCPU

memory

coresCPU

memory

coresCPU

memory

coresCPU

Message passing level

Multithreading level

While (local error > local tolerance) refine subdomains on the cores

rearrange subdomains among CPUs

Endwhile

Parallel out-of-order task schedulingwithout synchronization and idle time

Better efficiency on the single CPUs

Subdomains reorganization without global communications

Better scalability on the blade system

VO-Neural Project

Implementation of a web application (WA), of Data Mining and visualization methodologies for complex scientific data in distributed systems.

WA is intended to be a service for both astronomical and bioinformatic international communities.

Virtual Observatory: objective: federation and interoperability of worldwide astronomical data archives according to the standards of the International Virtual Observatory Alliance (IVOA).

Large astronomical surveys (from 100 TB to 1000 TB) requirements: patterns, trends etc in high dimensionality parametric spaces.

Results fromResults from MacroCosm SciencesMacroCosm Sciences(Dip. Scienze Fisiche & INAF)

http://nesssi.cacr.caltech.edu/dmtest/index.html

International Collaboration :•Università Federico II•INAF - Napoli•Caltech•Pennsylvania State University•Pune IUCAA - India

Applications:•Astrophysics•Biology e Bioinformatics• Enterprises

MIRROR sites: •SCOPE - UNINA •NESSSI - Caltech

S.Co.P.E. at CaltechDAME – Data Mining & Exploration

DAME offers user friendliness task for Data mining tasks.

DM models now available:• MLP: Multi Layer Perceptron• SVM: Support Vector Machines• PPS: Probabilistic Principal Surfaces

DM models under developments:• Bregmann co-clustering• SVM-C: SVM per clustering• Reti Bayesiane• PCA & ICA

Access to the GRID through robot-certificates (e-Token)

Specific applications are offered to the user as web – applications.

• Photometric redshifts for galaxies and quasar• Search for quasar candidates• Automatic classification of AGN (Active Galactic Nuclei) by photometric multiband surveys.

Talk by M. BresciaPoster by Laurino

Poster by Riccio

Ex: Automatic classification of AGNlg

2 (gamm

a)

lg2(C)

Base di conoscenza spettroscopica(per addestramento SVM)30380 objects

Superficie dei parametri delle SVMOttenuta su 110 nodi di S.Co.P.E.

e = 79.69%e Seyfert: esey = 74.76%e LINER : eLIN = 81.09%c Seyfert: csey = 52.77%c LINER : cLIN = 91.69%

VIRGO interferometer at Cascina (PI)

DATA INTENSIVE ALGORITHMS TO SEARCH FOR

GRAVITATIONAL WAVESGRAVITATIONAL WAVES

MORE THAN 1 TB /day1 TB /dayto be analyzed

Signals from:- periodic systems (Pulsar)- coalescent binary systems (Chirp)- impulsive systems (Burst)

VERY LOW SNR (SIGNAL to NOISE RATIO) HIGH COMPUTATIONAL CHALLENGE

Results from Results from Gravitational waves researchGravitational waves research(Dip. Scienze Fisiche &

INFN – Istituto Nazionale di Fisica Nucleare)

The online analysis in Virgo has been tackled via MerlinoMerlino, a SMFT-based (Static Matched Filter Technique) framework, whose architecture is sketched below.

Merlino: Data Analysis via Matched Filters

Bosi’s Merlino computes the correlation between the data series and a number of signal templates, obtained by simulating the chirp signals emitted by pairs of coalescing stars with solar masses within a given range. Precision and efficiency are strongly influenced by the number of considered working points, i.e. the granularity of the search in the space of the star masses.

A Grid-based Evolution of Merlino

Adaptive Filters for Detection of Gravitational Waves in Virgo

Data Size and Computational Cost of the AnalysisData from VIRGO are characterized by a very low SNR, and thus need to be accurately filtered to actually detect the presence of the signal. However an on-line analysis requires roughly 300 Gflops to retrieve the 90 per cent of the SNR.

Approach to the Analysis via Adaptive FiltersThe aim is to implement a rough analysis with: small signal losses (w.r.t. the use of matched filters); robustness against false detections; low computational costs (for use in real-time). The idea is to use the adaptive IIR ALE filter to reconstruct at the output the “coherent” component at the input. The “reconstruction” can then be used as (noisy) template for building a correlation detector for the analysis.

infinite impulse response adaptive line enhancer (IIR ALE)

A Genetic Parallel Evolution of the Price's Controlled Random Search Algorithm

As most of the others, the Price’s algorithm is based on a matched filter approach. However, instead of adopting a fixed grid of templates, it heuristically explores the search space via a controlled random search.

Parallel Genetic PricePrice algorithm has been modified to improve the performances and better ensure the thoroughness of the exploration of the search space - without having to consider a too high number of working points. To these aims we parallelized the software, and introduced a genetic modification of the search procedure, which introduce some randomness in the generation of new working points, thus making the software more resilient to local minima. Furthermore more than one trial point is now generated at each step.

A number of different population members are randomly chosen, and each of them is reflected through the centroid of the others, generating a new trial point. These trial points are then compared to the worst population members, and substitute them in case of better behaviour.

p

p

• Large Hadron Collider (proton-proton interactions)• Centre of mass energy: 14 TeV • Accelerator circumference: 27 km• Physics objectives:

– Particle physics in the TeV energy domain– Search for Higgs boson– Search for Physics Beyond the Standard Model

(supersimmetry etc.)– Precision measurements for konwn (and unkown)

physics.

ATLASALICE

CMS

LHCb

Bunch crossing every 25 ns rate 109 Hz

25 interactions/b.c. High granularity detectors: 108 electronic channels event size ~1 MB Input data rate: 1PB/s ! But only ~100 MB/s to tape. High selectivity triggersystem

(rejection power 107 ) Max\size of single files 2 GB, 16k

files/day 10 TB/day !

Napoli in exp.ATLAS e CMS

Results from Results from Subnuclear Physics @CERN-LHCSubnuclear Physics @CERN-LHC (Dip. Scienze Fisiche & INFN) (see talk by G. Carlino)

ATLAS Huge international effort

(scientific and tecnological)

37 nations 167 institutions 2000 scientists

22 m

46 m

First events at the LHCFirst events at the LHC (10-09-2008)(10-09-2008)

ATLAS

CMS

Reconstructed events with HPC & Grid computing

DisseminationDissemination

Meetings, workshops, events for dissemination of the results to research and industry communities:

•Le idee della ricerca al lavoro (26-27/2/08)•Networking Day (15/4/08)•Incontro con le imprese (16/4/08)•Italian e-science2008 (27-29/5/08)•Inaugurazione di SCoPE (1/12/08)

Beyond SCoPEBeyond SCoPE • High bandwidth network and

services• Cloud computing Scientific and

industrial applications• High Performance Computing e Grid

Computing.• Data Mining• Development of algorithms and

software

• Aerospace, Automobile• Telecommunications,

Informatics, Elettronics• Security• Chemistry, Farmaceutica,

Biomedicine• Transportation e logistics• Finance and Economy• Services for Public

Cooperation with Science and IndustryCooperation with Science and Industry Interoperability and Integration in GriSù Interoperability and Integration in GriSù IGI IGI EGI EGI

GRISU’

GARR

PI2S2

GARR Altri Enti e realtà

SPACI

OGNI INFRASTUTTURAIMPLEMENTA ALMENO UNSITE (CE+SE+WN) E REPLICAI SERVIZI COLLECTIVE

SERVIZI COLLECTIVE ECORE DI OGNI INFRASTRUTTURA SUPPORTANOTUTTE LE VO

1 VO PER PROGETTOcybersarcrescocometascopespaci

PORTICIPORTICI BRINDISIBRINDISILECCELECCE

TRISAIATRISAIA

GRISU’

EGIEuropean GRID Infrastructure

DEISAIGIItalian GRID Infrastructure

INFN-GRID

ENEA-GRID