Department of Computer Science, University of Pisapvmmpi03/post/vanneschi.pdf · ASSIST...

63
ASSIST ASSIST High High - - performance performance Programming Programming Environment : Environment : Application Application Experiences Experiences and Grid Evolution and Grid Evolution Marco Marco Vanneschi Vanneschi Department of Computer Science, University of Pisa EuroPVM/MPI 2003, Venice

Transcript of Department of Computer Science, University of Pisapvmmpi03/post/vanneschi.pdf · ASSIST...

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stea

d of

spe

cific

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a G

ENER

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N�

i.e. a

stru

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atca

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to a

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par

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puta

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with

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onde

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inis

m, a

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cces

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The parmod

cons

truct

VP

VP

VP

VP

VP

VP

VP

VP

i n p u t s e c t i o n

Shar

ed st

ate

o u t p u t s e c t

Mul

tiple

inpu

t and

out

put t

yped

data

stre

ams

Set o

f Virt

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user

code

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hav

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sign

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polo

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r nam

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(o

ne, n

one,

arr

ays)

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The parmod

cons

truct

VP

VP

VP

VP

VP

VP

VP

VP

i n p u t s e c t i o n

Shar

ed st

ate

o u t p u t s e c t

inde

pend

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butio

nan

d co

llect

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cast

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put s

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so h

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user

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The parmod

cons

truct

VP

VP

VP

VP

VP

VP

VP

VP

i n p u t s e c t i o n

Shar

ed st

ate

o u t p u t s e c t

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hos

t sev

eral

use

r fun

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ns, a

ctiv

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data

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onde

term

inis

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nnel

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shar

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ta st

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ru

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re

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Effi

cien

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-tim

e su

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ne o

f the

mai

n ad

vant

ages

of s

truct

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par

alle

l pr

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mm

ing

is th

e op

portu

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for e

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un-

time

impl

emen

tatio

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�spe

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� ske

leto

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SS

IST

has

prov

ed th

at th

is is

true

for �

gene

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skel

eton

s to

o: parmod

perfo

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com

para

ble

toth

atof

the

sam

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ms

writ

ten

in M

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�co

mpa

rabl

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, or b

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at o

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e pr

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ASS

IST

-Mar

co V

anne

schi

-E

uroP

VM

/MPI

200

3, V

enic

e22

Par

alle

lPar

titio

ned

Apr

iori

(Dat

a M

inin

g)!

Mai

nly

stre

am-p

aral

lel

!C

ompu

tatio

nin

tens

ive,

w

ell b

alan

ced

!da

tase

t> 1

60 M

b!

regu

lar I

/O p

atte

rn

!8

x P

entiu

m4,

Gbi

tEth

Apr

iori

spee

d-up

0,00

1,00

2,00

3,00

4,00

5,00

6,00

7,00

8,00

9,00

12

34

56

78

N. o

f Pro

cess

ors

Idea

lM

easu

red

Per

form

ance

Ben

chm

arks

of par

mod

(effi

cien

t as

MP

I or c

lass

ical

ske

leto

ns)

ASS

IST

-Mar

co V

anne

schi

-E

uroP

VM

/MPI

200

3, V

enic

e23

Apr

iori

algo

rithm

(dat

a m

inin

g) a

sa

pipe

line

of par

mod-

farm

s (�n

one�

topo

logy

)

stag

e 1

stag

e 4

. . .st

age

3

. . .

stag

e 2

stag

e 5

stag

e 6

1.D

atab

ase

read

ing,

gen

erat

ion

of s

tream

of p

artit

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algo

rithm

in p

aral

lel(

load

bala

nced

farm

)

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ombi

natio

nof

par

tial r

esul

ts: c

olla

psin

g hash-tree

data

stru

ctur

es

4.D

atab

ase scan

, gen

erat

ion

of a

new

stre

amof

par

titio

nsof

app

ropr

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tatio

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port"

of t

he c

andi

date

sol

utio

n(fa

rmw

ithbr

oadc

ast)

ASS

IST

-Mar

co V

anne

schi

-E

uroP

VM

/MPI

200

3, V

enic

e24

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form

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Ben

chm

arks

of par

mod

(effi

cien

t as

MP

I, be

tter t

han

clas

sica

l ske

leto

ns)

Dat

a-P

aral

lel B

ench

mar

kV

aria

ble

Ste

ncil

�si

ngle

pa

rmod

!2-

D m

atrix

400

x400

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rtitio

ned

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e!

com

mun

icat

ion

sten

cil

varie

s at

eac

h st

epfo

r h

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ll i,

j �

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x P

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, Gbi

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Dat

a pa

ralle

l spe

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p

0,00

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3,00

4,00

5,00

6,00

7,00

8,00

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02

46

810

N. o

f Pro

cess

ors

Idea

lM

easu

red

ASS

IST

-Mar

co V

anne

schi

-E

uroP

VM

/MPI

200

3, V

enic

e25

An

irreg

ular

-dyn

amic

benc

hmar

k(m

uch

bette

r tha

n cl

assi

cal s

kele

tons

)

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ody,

Bur

nes-

Hut

!P

arm

odim

plem

entin

g a

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cial

ized

farm

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ith s

hare

d m

emor

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ject

s

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lum

mer

Mod

el, v

ery

irreg

ular

dat

a-se

t

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x P

entiu

m 4

, Gbi

tEth

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ody

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d-up

, siz

e10

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0,00

1,00

2,00

3,00

4,00

5,00

6,00

7,00

8,00

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46

810

N. o

f Pro

cess

ors

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lM

easu

red

ASS

IST

-Mar

co V

anne

schi

-E

uroP

VM

/MPI

200

3, V

enic

e26

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plex

par

alle

l pro

gram

s in

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SIS

T

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ompl

ex a

pplic

atio

ns, f

ram

ewor

ksan

d/or

crit

ical

cas

es fo

r co

mpo

sitio

ns

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timat

e m

ix o

f tas

k +

data

par

alle

lism

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olic

com

puta

tions

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gle

parm

od, t

ask

+ da

ta p

aral

lelis

m)

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lass

ifica

tion,

clu

ster

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algo

rithm

s (g

raph

of p

arm

ods)

�U

ser p

rofil

ing

by d

ata

min

ing

(gra

phof

par

mod

s)�

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uage

inte

rpre

ters

�K

now

ledg

e di

scov

ery

in s

emi-s

truct

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tase

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raph

of p

arm

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lel e

xter

nal o

bjec

ts�

Dat

a re

posi

torie

s�

Web

cac

hing

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terf

aces

for l

egac

y SW

ASS

IST

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co V

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-E

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200

3, V

enic

e27

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mpl

e: d

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ing

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5 as

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ralle

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Γ 1T

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nt

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ide

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quer

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ng se

t TS,

dec

isio

n tr

eeΓ

}

P 1

P N

For

load

bal

anci

ng:

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ring

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e ph

ases

: Div

ide

wor

ksin

a d

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para

llelm

anne

r,

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er p

hase

s:

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farm

-like

m

anne

r,

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oth

er p

hase

s�

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cts

expl

oite

d ef

ficie

ntly

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VM

/MPI

200

3, V

enic

e28

Inte

rfac

e CR

M-D

B ->

DR CR

M-D

B(O

racl

e)

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tion

Clas

sif

Ass

oc

Know

ledg

eRe

posi

tory

.(X

ML)

Inte

rfac

eLa

yout

Gen

erat

or

Cont

rol a

ndtu

ning

Inte

rfac

e D

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CRM

-DB

Feed

back

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ser-

prof

iling

fram

ewor

k

Vis

ualiz

e

Clus

t

SA

IB p

roje

ct: M

IUR

L46

SE

MA

Sch

lum

berg

er, U

niv.

Pis

a, P

oly.

Turin

Dat

a Re

posito

ry(p

aral

lelf

ile s

yste

m)

ASS

IST

-Mar

co V

anne

schi

-E

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VM

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200

3, V

enic

e29

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erna

l obj

ects

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eces

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feat

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for f

lexi

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tera

ctiv

e ap

plic

atio

ns!

Obj

ects

reus

e w

ith p

rimiti

ve A

PIs

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evic

es, f

iles,

Par

alle

lFile

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tem

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ata

repo

sito

ries

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hare

d m

emor

y ob

ject

s!

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SIS

T pr

ogra

ms

them

selv

es

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ompo

sitio

n by

stre

ams

only

is n

ot s

uffic

ient

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war

ds C

ompo

nent

AS

SIS

T

{E

xter

nal O

bjec

ts}

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M5

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IST

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co V

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VM

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200

3, V

enic

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mpl

e: d

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nt

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t

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ide

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quer

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aini

ng se

t TS,

dec

isio

n tr

eeΓ

}

P 1

P N

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ed T

ree

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cts

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oite

d ef

ficie

ntly

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IST

-Mar

co V

anne

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VM

/MPI

200

3, V

enic

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ctur

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SIST

prog

ram

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grat

ion

with

CO

RB

AC

ode

comp

ute(P

arMo

d)co

mpute

(Par

Mod)

sequ

entia

lcod

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p co

ntrol

initia

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ulatio

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ults

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e

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r side

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-bod

y si

mul

atio

n!

GU

I CO

RB

A

serv

er!

para

llelc

lient

ASS

IST

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anne

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-E

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200

3, V

enic

e32

Par

t 2

AS

SIS

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Flex

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ar20

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libra

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ASS

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IST

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3, V

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tegr

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ralle

l MP

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rarie

s

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Grid

ver

sion

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eous

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IST

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co V

anne

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VM

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200

3, V

enic

e37

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200

3, V

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IST

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t Grid

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plem

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L co

nfig

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ctiv

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mod

ules

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ph!

path

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e-na

mes

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s m

appi

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IST

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allo

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anta

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un-ti

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n �a

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part

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Abs

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Dat

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ASS

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Dat

a-in

tens

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tions

in A

SS

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Obj

ect(

poss

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h-ba

ndw

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bstra

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gh-

perf

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ject

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impl

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by

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SSIS

T or

ano

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fo

rmal

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VP

VP

VP

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Inpu

t Se

ctio

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ASSI

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tract

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Exte

rnal

Obj

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nter

face

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sibl

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ralle

l)

Than

ks to

ASS

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grou

p �

Dep

artm

ent

of C

ompu

ter

Scie

nce,

Uni

v. o

f Pi

sa:

M.A

ldin

ucci

, S.C

ampa

, P.C

iullo

, M. C

oppo

la, M

.Dan

elut

to, S

.Mag

ini,

S.

Moi

, A

.Pa

tern

esi,

P.Pe

sciu

llesi

, A

.Pe

troce

lli,

E.Pi

stol

etti,

L.

Potit

i, R

.R

avaz

zolo

, M.T

orqu

ati,

G.V

irdis

, P. V

itale

, C.Z

occo

lo

ISTI

-CN

R g

roup

, Pis

a:D

omen

ico

Lafo

renz

a, S

alva

tore

Orla

ndo

(Uni

v. o

f V

enic

e), R

affa

ele

Pere

go, N

icol

a To

nello

tto, R

anie

ri B

arag

lia

Than

k yo

u fo

r atte

ntio

n

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Cur

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Bas

ic H

W+S

W p

latfo

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dlew

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not n

eces

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me

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dlew

are

�as b

efor

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need

s of t

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Prog

ram

min

g En

viro

nmen

t.

�H

igh-

leve

l lan

guag

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posi

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alit

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odul

arit

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rope

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lity

�Co

mpi

ling

Tool

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Run

Tim

e Su

ppor

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Perf

orm

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Mod

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Mod

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for

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opti

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atio

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adin

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xecu

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, m

onit

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rec

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ools

Mid

dlew

are

⇒Gr

id A

bstr

act

Mac

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min

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viro

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