Join

t Mat

h/C

S In

stitu

tes

Sum

mar

y

Mik

e H

erou

x

2

Key

Top

ics

Dis

cuss

ed

1.E

ffect

ive

use

of m

any

core

and

hy

brid

arc

hite

ctur

e.2.

Exp

loiti

ng m

ixed

pre

cisi

on.

Sin

gle/

doub

le a

nd d

oubl

e/qu

ad.

Sub

-sin

gle/

XX

X.

3.A

ddre

ssin

g co

mpl

exiti

es o

f nod

e ar

chite

ctur

es.

4.F

ault

dete

ctio

n an

d to

lera

nt

algo

rithm

s, re

silie

nce.

5.C

omm

unic

atio

n-av

oidi

ng a

nd

com

mun

icat

ion-

com

puta

tion

conc

urre

nt a

lgs.

6.S

ensi

tivity

ana

lysi

s (b

road

de

finiti

on)

7.M

ultis

cale

/mul

tiphy

sics

mod

elin

g8.

Fas

t im

plic

it so

lves

.

9.P

erfo

rman

ce d

egra

datio

n at

sca

le d

ue

to lo

ad im

bala

nce

expo

sed

by

sync

hron

izat

ion.

10.

Alg

orith

m a

dvan

ces:

Mag

neto

-co

mpr

essi

ve w

ave

refo

rmul

atio

n. T

ime

para

llel a

lgor

ithm

s11

.E

ffici

ent m

ultig

rid, e

ffici

ent m

ulti-

grid

-lik

e tim

e al

gorit

hms

12.

Effe

ctiv

e us

e of

new

and

em

ergi

ng

mem

ory

sys

tem

s13

.D

ebug

ging

of c

orre

ctne

ss a

nd

perf

orm

ance

.14

.M

otifs

, int

erop

erab

le m

otifs

.15

.N

ew c

apab

ilitie

s to

pro

mot

e ef

ficie

nt

deve

lopm

ent o

f opt

imiz

ed c

ode.

16.

New

dis

cret

e op

timiz

atio

n m

etho

ds fo

r co

mpu

ter s

yste

m re

sour

ce

man

agem

ent.

3

Pro

blem

s

1.In

abili

ty to

effi

cien

tly d

evel

op

stra

ight

-for

war

d, h

igh-

perf

orm

ance

por

tabl

e co

de.

2.U

sing

mac

hine

s ef

ficie

ntly

:a)

Usi

ng c

ompu

tatio

nal u

nits

(m

ultic

ore,

GP

Us)

.b)

Usi

ng m

emor

y sy

stem

effi

cien

tly.

c)U

sing

sw

itch-

leve

l sys

tem

ef

ficie

ntly

(e.g

. IC

N).

d)U

sing

sys

tem

pow

er e

ffici

ently

.e)

Usi

ng s

ynch

roni

zatio

ns e

ffici

ently

.

3.F

ault

dete

ctio

n, to

lera

nce

and

man

agem

ent

4.S

ensi

tiviti

es, U

Q, Q

MU

, etc

.5.

Mul

tisca

le/M

ultip

hysi

cs.

6.F

ast i

mpl

icit

solv

es.

–es

p: S

mal

l coa

rse

prob

lem

on

big

dedi

cate

d m

achi

ne.

7.N

umer

ical

sta

bilit

y of

tran

sien

t pr

oble

ms

at s

cale

.8.

Deb

uggi

ng o

f cor

rect

ness

and

pe

rfor

man

ce is

unt

enab

le.

9.S

ubop

timal

alg

orith

ms

for

com

pute

r sys

tem

res

ourc

e m

anag

emen

t.

4

Cro

ss-c

uttin

g to

ols

1.C

ode,

alg

orith

m a

nd m

odel

tran

sfor

mat

ions

.2.

Mot

ifs a

nd th

eir

inte

rope

rabi

lity.

3.P

orta

ble

prog

ram

min

g m

odel

, exe

cutio

n m

odel

.4.

Alg

orith

ms.

a)Im

plic

it m

etho

ds.

b)R

efor

mul

atio

ns fo

r la

rger

tim

e st

eps.

Par

area

l.

c)D

iscr

ete

optim

izat

ion

for

page

map

ping

, rou

ter

man

agem

ent,

etc.

5.M

ixed

pre

cisi

on, r

educ

ed d

ata

repr

esen

tatio

ns.

6.Li

brar

ies

can

be a

test

-bed

, pro

of-o

f-co

ncep

t for

new

pr

ogra

mm

ing/

exec

utio

n m

odel

s.

Join

t Mat

h/C

S In

stitu

tes

Dis

cuss

ion

Jack

Don

garr

a

6

Cha

lleng

es F

acin

g S

cala

ble

App

licat

ions

: W

here

are

the

‘gap

s’ b

etw

een

pote

ntia

l and

ach

ieve

d p

erfo

rman

ce?

�T

opic

: Effe

ctiv

e us

e of

man

ycor

e an

d hy

brid

arc

hite

ctur

e.�

Wha

t are

the

prim

ary

bottl

enec

ks fa

cing

com

puta

tion

al s

cien

tists

?–

Usi

ng th

em a

t all

is th

e is

sue.

–M

PI i

s th

e on

ly th

ing,

with

a li

ttle

Ope

nMP

.–

Pro

duct

ivity

is is

sue:

Is li

bs th

e an

swer

?–

Tw

o m

ain

issu

es:

•A

lgs

can

be m

appe

d bu

t CS

infr

astr

uctu

re is

mis

sing

to e

xpre

ss a

lgs.

•C

urre

nt a

lgs

don’

t ha

ve c

oncu

rren

cy, w

e ne

ed n

ew o

nes

or n

eed

to r

efoc

us to

hi

gher

up

the

alg

tree

(fo

r ne

w c

oncu

rren

cy).

�T

o w

hat a

reas

can

a ti

ghtly

inte

grat

ed M

ath/

CS

effo

rt c

ontr

ibut

e?–

Pro

of o

f con

cept

of n

ew la

ngua

ges,

new

alg

s in

libr

arie

s an

d pr

oxie

s fo

r la

rge-

scal

e ap

plic

atio

ns.

–W

e do

n’t h

ave

a su

itabl

e pr

ogra

mm

ing

mod

el a

nd (

a pr

e-re

quis

ite)

an e

xecu

tion

mod

el.

–D

ealin

g w

ith th

e da

ta la

yout

issu

e: C

once

ptua

l vs.

opt

imiz

e la

yout

s.

7

Cha

lleng

es F

acin

g S

cala

ble

App

licat

ions

: W

here

are

the

‘gap

s’ b

etw

een

pote

ntia

l and

ach

ieve

d p

erfo

rman

ce?

�T

opic

: Exp

loiti

ng m

ixed

pre

cisi

on.

–S

ingl

e/do

uble

and

dou

ble/

quad

. Sub

-sin

gle/

XX

X.

–In

tege

r ra

nge

also

an

issu

e: d

ynam

ic o

rdin

al r

ange

s.–

Ben

efits

: Red

uced

dat

a m

ovem

ent,

fast

er F

P e

xecu

tion.

�W

hat a

re th

e pr

imar

y bo

ttlen

ecks

faci

ng c

ompu

tatio

nal

sci

entis

ts?

–S

tabi

lity

and

conv

erge

nce.

•P

ract

ical

use

of S

P a

t sca

le.

•A

utom

atio

n of

det

ectio

n/co

rrec

tion.

–E

xpre

ssib

ility

of m

ixed

dat

a ty

pes

in c

urre

nt la

ngua

ges.

�T

o w

hat a

reas

can

a ti

ghtly

inte

grat

ed M

ath/

CS

effo

rt c

ontr

ibut

e?–

Dev

elop

men

t of a

utom

ated

det

ectio

n/co

rrec

tion

capa

bilit

ies.

–P

rogr

amm

ing

mod

el s

uppo

rt fo

r m

ixed

pre

cisi

on.

–N

ote:

Alre

ady

som

e w

ork

star

ted

on to

ols

for

debu

ggin

g nu

mer

ical

sen

sitiv

ity

(UC

-Ber

kele

y).

8

Cha

lleng

es F

acin

g S

cala

ble

App

licat

ions

: W

here

are

the

‘gap

s’ b

etw

een

pote

ntia

l and

ach

ieve

d p

erfo

rman

ce?

�T

opic

: Add

ress

ing

com

plex

ities

of n

ode

arch

itect

ure

s.�

Wha

t are

the

prim

ary

bottl

enec

ks fa

cing

com

puta

tion

al

scie

ntis

ts?

–H

ave

8-10

diff

eren

t app

roac

hes.

Whi

ch, i

f any

, to

use?

–La

rge

colle

ctio

n of

lega

cy c

ode.

–S

cope

of c

urre

nt a

naly

ses

is to

o na

rrow

and

focu

sed

on c

ompu

te-r

ich

algo

rithm

s. C

oupl

ed to

sto

rage

ass

ocia

tion

prob

lem

.

�T

o w

hat a

reas

can

a ti

ghtly

inte

grat

ed M

ath/

CS

effo

rt

cont

ribut

e?–

Sel

f ada

ptin

g / a

uto-

tuni

ng o

f sof

twar

e.–

ID a

ppro

pria

te re

pres

enta

tions

of a

pp n

eeds

.–

Libr

arie

s an

d pr

oxie

s ca

n pr

ovid

e fir

st e

xper

ienc

es.

9

Cha

lleng

es F

acin

g S

cala

ble

App

licat

ions

: W

here

are

the

‘gap

s’ b

etw

een

pote

ntia

l and

ach

ieve

d p

erfo

rman

ce?

�T

opic

: Fau

lt de

tect

ion

and

tole

rant

alg

orith

ms,

res

ilien

ce.

�W

hat a

re th

e pr

imar

y bo

ttlen

ecks

faci

ng c

ompu

tatio

nal

sci

entis

ts?

–T

wo

bran

ches

:•

Sui

tabl

e al

gs, n

o w

ay t

o ex

pres

s th

em.

•M

ovin

g up

the

exec

utio

n hi

erar

chy

to e

xpre

ss t

hem

.–

Cur

rent

RT

/OS

tool

s an

d sy

stem

pol

icie

s in

suffi

cien

t fo

r de

tect

ing

faul

ts.

–Is

sues

exi

sts

with

in t

he c

ompl

ete

hier

arch

y of

the

syst

em f

rom

HW

to A

pp.

It’s

a C

S p

robl

em

to d

efin

e th

e in

terf

aces

.

�T

o w

hat a

reas

can

a ti

ghtly

inte

grat

ed M

ath/

CS

effo

rt c

ontr

ibut

e?–

Impr

ove

chec

kpoi

ntin

g pr

oces

s: E

.g.,

Red

uce

the

foot

prin

t of

the

chec

kpoi

nt.

–E

xpan

ded

defin

ition

of c

heck

poin

ting,

impr

oved

res

ilien

ce,

not j

ust t

oday

’s d

efin

ition

, e.

g.,

disk

less

w/

chec

ksum

.–

Pro

gram

min

g M

odel

/Lan

guag

e/lib

rary

lev

el s

uppo

rt f

or fa

ult d

etec

tion/

reco

very

.•

Aug

men

ted

prog

ram

min

g to

incl

ude

algo

rithm

“sa

nity

che

cks”

.•

Mec

hani

sm t

o ex

pres

s th

e re

lativ

e im

port

ance

of g

ettin

g th

e rig

ht a

nsw

er.

•In

terv

al a

rithm

etic

?•

Aut

omat

ion

of s

anity

che

cks.

–S

yner

gy w

ith tr

ansa

ctio

nal p

rogr

amm

ing

mod

el?

May

be

a na

tura

l fit

with

som

e nu

mer

ical

co

des.

–N

eeds

to b

e so

lved

in th

e ne

xt g

en o

f sys

tem

s: M

TB

F is

get

ting

too

bad.

–S

olut

ion

need

s an

inte

grat

ed/c

onsi

sten

t ap

proa

ch t

hrou

gh t

he H

W/S

W s

tack

.

10

Cha

lleng

es F

acin

g S

cala

ble

App

licat

ions

: W

here

are

the

‘gap

s’ b

etw

een

pote

ntia

l and

ach

ieve

d p

erfo

rman

ce?

�T

opic

: Com

mun

icat

ion-

avoi

ding

and

com

mun

icat

ion-

com

puta

tion

conc

urre

nt a

lgs.

�W

hat a

re th

e pr

imar

y bo

ttlen

ecks

faci

ng c

ompu

tatio

nal

sc

ient

ists

?–

Lots

of s

yste

ms

don’

t sup

port

this

in r

ealit

y. C

an’t

mea

sure

impa

ct.

–A

lgor

ithm

s th

at a

llow

con

curr

ency

(lac

k of

thes

e).

�T

o w

hat a

reas

can

a ti

ghtly

inte

grat

ed M

ath/

CS

effo

rt

cont

ribut

e?–

Dev

elop

men

t of n

ew a

lgs

with

gre

ater

con

curr

ency

/ove

rlap.

•N

ote:

Nee

d 10

0X-1

MX

ove

r cu

rren

t con

curr

ency

.•

CS

Too

ls to

allo

w d

isco

very

of c

oncu

rren

t alg

orith

ms.

–F

ull s

yste

m (

RT

, OS

, HW

, …)

supp

ort f

or e

xplo

iting

con

curr

ency

.–

Dev

elop

men

t of c

onsi

sten

cy m

odel

that

sup

port

s co

ncur

rent

co

mm

unic

atio

n.

Join

t Mat

h/C

S In

stitu

tes

Dis

cuss

ion

Tre

y W

hite

12

Cha

lleng

es F

acin

g S

cala

ble

App

licat

ions

: W

here

are

the

‘gap

s’ b

etw

een

pote

ntia

l and

ach

ieve

d p

erfo

rman

ce?

�S

umm

ary

of s

umm

arie

s.

Tow

n H

alls

–B

ig s

olve

rs,

–B

ig d

ata,

–

Big

SW

–Lo

ng s

imul

atio

ns–

UQ

, sen

sitiv

ity, V

&V

–M

ultis

cale

–H

iera

rchi

cal p

aral

lelis

m–

Des

ign

Opt

imiz

atio

n

Exa

scal

e S

urve

y su

mm

ary

–A

utom

ated

dia

gnos

tics

–H

W L

aten

cy–

Hie

rarc

hica

l alg

s–

Par

alle

l pro

gram

min

g m

odel

s–

Sol

ver

tech

nolo

gy a

nd

inno

vativ

e so

lutio

n te

chni

ques

–A

ccel

erat

ed ti

me

inte

grat

ion

–M

odel

cou

plin

g–

Mai

ntai

ning

cur

rent

libr

arie

s

13

Cha

lleng

es F

acin

g S

cala

ble

App

licat

ions

: W

here

are

the

‘gap

s’ b

etw

een

pote

ntia

l and

ach

ieve

d p

erfo

rman

ce?

�T

opic

: Sen

sitiv

ity a

naly

sis

(bro

ad d

efin

ition

):–

Mod

el d

efin

ition

var

iabi

lity.

–P

aram

eter

sen

sitiv

ity.

–U

Q a

nd Q

MU

.�

Wha

t are

the

prim

ary

bottl

enec

ks fa

cing

com

puta

tion

al s

cien

tists

?–

Aut

omat

ic to

ols

(AD

) ar

e de

ficie

nt:

•In

terla

ngua

ge s

uppo

rt•

C+

+ s

uppo

rt•

F9X

sup

port

–Li

brar

ies

lack

sen

sitiv

ity in

terf

aces

.�

To

wha

t are

as c

an a

tigh

tly in

tegr

ated

Mat

h/C

S e

ffort

con

trib

ute?

–M

ulti-

lingu

al c

ompi

latio

n en

viro

nmen

t.–

Cou

plin

g w

ith m

ulti-

prec

isio

n co

mpu

tatio

ns.

–Li

brar

y A

PIs

for

sens

itivi

ties.

14

Cha

lleng

es F

acin

g S

cala

ble

App

licat

ions

: W

here

are

the

‘gap

s’ b

etw

een

pote

ntia

l and

ach

ieve

d p

erfo

rman

ce?

�T

opic

: Mul

tisca

le m

odel

ing

–A

wid

e ra

nge

of s

cale

s.–

Prim

ary

mod

el a

t one

sca

le, a

ssim

ilatio

n of

sub

scal

e m

odel

s.�

Wha

t are

the

prim

ary

bottl

enec

ks fa

cing

com

puta

tion

al s

cien

tists

?–

Dat

a st

ruct

ures

: •

Dat

a ex

chan

ge b

etw

een

scal

es.

–Lo

ad b

alan

cing

:•

Bal

ance

of c

ompu

tatio

n at

eac

h sc

ale.

•W

ork

and

Dat

a re

part

ition

ing.

–M

athe

mat

ical

pro

pert

ies.

•S

tabi

lity

•A

ccur

acy.

•T

rans

latio

n be

twee

n pa

rtic

le <

-> c

ontin

uum

.�

To

wha

t are

as c

an a

tigh

tly in

tegr

ated

Mat

h/C

S e

ffort

con

trib

ute?

–A

way

to

expr

ess

inho

mog

eneo

us p

aral

lelis

m, a

fram

ewor

k fo

r ex

pres

sion

.–

Too

ls to

sup

port

inho

mog

eneo

us p

aral

lelis

m.

15

Cha

lleng

es F

acin

g S

cala

ble

App

licat

ions

: W

here

are

the

‘gap

s’ b

etw

een

pote

ntia

l and

ach

ieve

d p

erfo

rman

ce?

�T

opic

: Fas

t im

plic

it so

lves

.�

Wha

t are

the

prim

ary

bottl

enec

ks fa

cing

com

puta

tion

al s

cien

tists

?–

Poo

r si

ngle

cor

e pe

rfor

man

ce (

rela

tive

to p

eak)

.–

Inef

ficie

nt u

se o

f mem

ory

band

wid

th.

–C

halle

ngin

g sc

alin

g (lo

ad im

bala

nce,

alg

orith

m c

ompl

exity

)–

Alg

orith

m ro

bust

ness

in th

e pr

esen

ce o

f fau

lts.

–M

emor

y co

nstr

aint

s, e

sp. i

n fo

rmin

g m

atrix

.�

To

wha

t are

as c

an a

tigh

tly in

tegr

ated

Mat

h/C

S e

ffort

con

trib

ute?

–A

lgor

ithm

resi

lienc

e, e

sp fo

r ite

rativ

e m

etho

ds.

–Q

uant

ifica

tion

of th

e ga

p be

twee

n ob

serv

ed a

nd a

chie

vabl

e pe

rfor

man

ce:

•P

redi

ctio

n is

diff

icul

t, bu

t bou

ndin

g is

eas

ier.

–Q

uant

ifica

tion

of p

erfo

rman

ce b

enef

it/lo

ss fo

r im

plic

it ov

er e

xplic

it.–

Qua

ntifi

catio

n ar

chite

ctur

al im

pedi

men

ts to

bet

ter

algo

rithm

per

form

ance

:•

HW

des

ign,

acc

essi

bilit

y an

d co

ntro

l of h

ardw

are

feat

ures

(e.

g., c

ache

con

trol

)

Join

t Mat

h/C

S In

stitu

tes

Dis

cuss

ion

Bria

n va

n S

traa

len

17

Cha

lleng

es F

acin

g S

cala

ble

App

licat

ions

: W

here

are

the

‘gap

s’ b

etw

een

pote

ntia

l and

ach

ieve

d p

erfo

rman

ce?

�T

opic

: Per

form

ance

deg

rada

tion

at s

cale

due

to lo

ad im

bala

nce

expo

sed

by s

ynch

roni

zatio

n.•

Rag

ged

en

try

into

co

llect

ives

.–

HW

faul

t rec

over

y (p

ersi

sten

t bad

act

or)

(Str

ong

and

wea

k pr

oble

m).

–M

odel

var

iabi

lity

(app

licat

ion

leve

l).–

Mem

ory

syst

em a

nom

alie

s (t

rans

ient

, mig

rato

ry)

(Jus

t str

ong

prob

lem

).

�W

hat a

re th

e pr

imar

y bo

ttlen

ecks

faci

ng c

ompu

tatio

nal

sc

ient

ists

?–

Dep

ende

nce

on fl

at S

PM

D m

odel

at s

cale

. But

not

cle

ar th

at s

omet

hing

el

se is

bet

ter.

–D

epen

denc

e on

sin

gle-

leve

l MP

I.–

HW

: Is

the

depe

nden

ce o

n M

PI

help

ing

or h

urtin

g ou

r bo

ttlen

eck?

�T

o w

hat a

reas

can

a ti

ghtly

inte

grat

ed M

ath/

CS

effo

rt

cont

ribut

e?

18

Cha

lleng

es F

acin

g S

cala

ble

App

licat

ions

: W

here

are

the

‘gap

s’ b

etw

een

pote

ntia

l and

ach

ieve

d p

erfo

rman

ce?

�T

opic

: Alg

orith

m a

dvan

ces

•M

agne

to-c

ompr

essi

ve w

ave

refo

rmul

atio

n.

•T

ime

para

llel a

lgor

ithm

s.�

Wha

t are

the

prim

ary

bottl

enec

ks fa

cing

com

puta

tion

al

scie

ntis

ts?

–S

mal

l tim

e st

eps

hind

er la

rge-

scal

e ru

ns.

�T

o w

hat a

reas

can

a ti

ghtly

inte

grat

ed M

ath/

CS

effo

rt

cont

ribut

e?–

Cho

ice

of m

ath

form

ulat

ion

is c

oupl

ed w

ith m

otif

sele

ctio

n w

hich

is

coup

led

with

par

alle

l pro

gram

min

g m

odel

s.–

Out

reac

h an

d tr

aini

ng: p

ushi

ng a

war

enes

s of

new

mat

h te

chni

ques

to

the

mod

elin

g co

mm

unity

.

19

Cha

lleng

es F

acin

g S

cala

ble

App

licat

ions

: W

here

are

the

‘gap

s’ b

etw

een

pote

ntia

l and

ach

ieve

d p

erfo

rman

ce?

�T

opic

: Effi

cien

t mul

tigrid

, effi

cien

t mul

ti-gr

id-li

ke ti

me

algo

rithm

s.�

Wha

t are

the

prim

ary

bottl

enec

ks fa

cing

com

puta

tion

al

scie

ntis

ts?

–C

oars

e gr

id s

olve

.–

Alg

orith

m w

ith g

reat

am

ount

of w

ork,

dep

ends

on

smal

l pro

blem

: ser

ial

frac

tion.

�T

o w

hat a

reas

can

a ti

ghtly

inte

grat

ed M

ath/

CS

effo

rt

cont

ribut

e?–

Hig

hly

effic

ient

coa

rse

solv

es.

–B

ette

r di

strib

uted

mem

ory

inte

rcon

nect

net

wor

ks.

–R

esou

rce

shar

ing

so th

at s

eria

l bot

tlene

cks

are

not a

n is

sue?

20

Cha

lleng

es F

acin

g S

cala

ble

App

licat

ions

: W

here

are

the

‘gap

s’ b

etw

een

pote

ntia

l and

ach

ieve

d p

erfo

rman

ce?

�T

opic

: Effe

ctiv

e us

e of

new

and

em

ergi

ng m

emor

y s

yst

ems.

�W

hat a

re th

e pr

imar

y bo

ttlen

ecks

faci

ng c

ompu

tatio

nal

sci

entis

ts?

–C

ache

mem

ory

syst

ems

poor

ly s

erve

man

y sc

ienc

e ap

ps.

–G

athe

r/sc

atte

r, in

dire

ct m

em c

opy

seem

like

impo

rtan

t opp

ortu

nitie

s fo

r im

prov

emen

t.–

Use

rs h

ave

little

con

trol

ove

r H

W/r

untim

e sy

stem

beh

avio

r.–

Fla

t SP

MD

, MP

I-on

ly m

odel

doe

s no

t sca

le, o

r m

ake

effe

ctiv

e us

e of

mul

ticor

e.�

To

wha

t are

as c

an a

tigh

tly in

tegr

ated

Mat

h/C

S e

ffort

con

trib

ute?

–S

tudi

es o

f “w

hat-

if” s

cena

rios

for

our

impo

rtan

t com

puta

tions

.–

Low

ban

dwid

th a

lgor

ithm

s. R

educ

ed s

ync

pt a

lgor

ithm

s.–

Mem

ory

syst

em a

war

e ac

cess

(eg.

, mul

ticor

e sh

arin

g of

mem

. sys

tem

)–

Cha

ract

eriz

atio

n of

ess

entia

l ban

dwid

th n

eeds

and

ela

bora

tion

of ty

pes

(str

eam

ing,

G/S

).–

Dev

elop

men

t and

use

of s

imul

ator

s.–

Stu

dy o

f par

alle

l pro

gram

min

g m

odel

s, s

oftw

are

stru

ctur

e fo

r hi

erar

chic

al m

emor

y sy

stem

s (3

-4 le

vels

: dis

trib

uted

, sha

red

1-2,

SIM

D).

–M

ath

cont

ribut

ion:

Exp

lora

tion

of a

ltern

ativ

e al

gorit

hms

and

form

ulat

ions

that

are

eq

uiva

lent

but

may

hav

e m

ore

favo

rabl

e pe

rfor

man

ce.

21

Cha

lleng

es F

acin

g S

cala

ble

App

licat

ions

: W

here

are

the

‘gap

s’ b

etw

een

pote

ntia

l and

ach

ieve

d p

erfo

rman

ce?

�T

opic

: Deb

uggi

ng o

f cor

rect

ness

and

per

form

ance

.�

Wha

t are

the

prim

ary

bottl

enec

ks fa

cing

com

puta

tion

al

scie

ntis

ts?

–D

ebug

ging

at s

cale

is h

ard.

–C

ompu

ting

envi

ronm

ent i

s co

mpl

ex, d

iffic

ult t

o ch

arac

teriz

e ex

pect

ed

perf

orm

ance

.�

To

wha

t are

as c

an a

tigh

tly in

tegr

ated

Mat

h/C

S e

ffort

co

ntrib

ute?

–M

athe

mat

ical

ass

ertio

ns: i

dent

ities

, equ

ival

ence

s, …

–F

ram

ewor

k fo

r de

tect

ing

and

resp

ondi

ng to

ass

ertio

n an

d m

odel

vi

olat

ions

.

22

Cha

lleng

es F

acin

g S

cala

ble

App

licat

ions

: W

here

are

the

‘gap

s’ b

etw

een

pote

ntia

l and

ach

ieve

d p

erfo

rman

ce?

�T

opic

: Mot

ifs, i

nter

oper

able

mot

ifs.

�W

hat a

re th

e pr

imar

y bo

ttlen

ecks

faci

ng c

ompu

tatio

nal

sc

ient

ists

?–

Diff

icul

ty in

com

mun

icat

ing

betw

een

dom

ain

scie

ntis

t and

libr

ary/

CS

ex

pert

. N

o co

mm

on la

ngua

ge.

–O

ptim

izat

ion

effo

rts

and

com

mun

icat

ion

betw

een

dom

ain

scie

ntis

t and

co

mpu

ter s

cien

tist i

s do

ne a

t the

sou

rce

code

leve

l.�

To

wha

t are

as c

an a

tigh

tly in

tegr

ated

Mat

h/C

S e

ffort

co

ntrib

ute?

–Id

entif

y an

d ca

talo

gue

mot

ifs.

–Id

entif

y m

otifs

use

d in

an

appl

icat

ion/

libra

ry.

–E

duca

te c

omm

unity

abo

ut m

otif

conc

ept a

nd c

atal

og.

–E

xplo

re e

ffici

ent i

mpl

emen

tatio

ns fo

r m

otifs

, con

side

r alte

rnat

ive

mot

ifs.

Join

t Mat

h/C

S In

stitu

tes

Dis

cuss

ion

Bar

ry S

mith

24

Cha

lleng

es F

acin

g S

cala

ble

App

licat

ions

: W

here

are

the

‘gap

s’ b

etw

een

pote

ntia

l and

ach

ieve

d p

erfo

rman

ce?

�T

opic

: New

cap

abili

ties

to p

rom

ote

effic

ient

dev

elo

pmen

t of

optim

ized

cod

e.�

Wha

t are

the

prim

ary

bottl

enec

ks fa

cing

com

puta

tion

al

scie

ntis

ts?

–H

and

optim

izat

ions

err

or-p

rone

, sta

tic.

–C

once

ptua

l dat

a la

yout

non

-opt

imal

, e.g

., ijk

grid

inde

xing

fuse

d to

st

orag

e.�

To

wha

t are

as c

an a

tigh

tly in

tegr

ated

Mat

h/C

S e

ffort

co

ntrib

ute?

–C

ode

tran

sfor

mat

ion

tool

s, a

llow

ing

user

inte

rven

tion,

sou

rce

to s

ourc

e•

Loop

unr

ollin

g fo

r sp

ecifi

c si

zes:

spa

rse

SV

, LA

PA

CK

for

smal

l siz

es.

–C

ompi

ler t

rans

form

atio

ns to

red

uce

sync

hron

izat

ion

poin

ts: e

.g.,

Bi-

CG

ST

AB

tran

sfor

mat

ion.

Join

t Mat

h/C

S In

stitu

tes

Con

trib

utio

n: R

ich

Gra

ham

, Ron

Brig

htw

ell

26

Cha

lleng

es F

acin

g S

cala

ble

App

licat

ions

: W

here

are

the

‘gap

s’ b

etw

een

pote

ntia

l and

ach

ieve

d p

erfo

rman

ce?

�T

opic

: New

dis

cret

e op

timiz

atio

n m

etho

ds fo

r co

mpu

ter

sys

tem

re

sour

ce m

anag

emen

t.�

Wha

t are

the

prim

ary

bottl

enec

ks fa

cing

com

puta

tion

al

scie

ntis

ts?

–A

d ho

c m

etho

ds fo

r co

mpu

ter s

yste

m r

esou

rce

man

agem

ent.

•P

age

plac

emen

t, ro

uter

sch

edul

ing,

…�

To

wha

t are

as c

an a

tigh

tly in

tegr

ated

Mat

h/C

S e

ffort

co

ntrib

ute?

–D

iscr

ete

optim

izat

ion

algo

rithm

s fo

r sy

stem

tool

s.

Join

t Mat

h/C

S In

stitu

tes

Inst

itute

Des

crip

tion

28

Iden

tify

the

‘opt

imal

’ end

sta

te in

10

year

s tim

e . .

.

�Li

brar

ies

and

tool

s w

ill h

ave

built

-in s

uppo

rt fo

r se

nsiti

vity

in

form

atio

n.�

Libr

arie

s an

d to

ols

will

hav

e bu

ilt-in

pre

dict

ive

per

form

ance

m

odel

s.�

Libr

arie

s ar

e fa

ult-

awar

e; re

silie

nce

or in

form

ativ

e or

bot

h.�

Libr

arie

s w

ill h

ave

perf

orm

ance

por

tabi

lity.

�R

eal a

pplic

atio

n pe

rfor

man

ce w

ill m

atch

ach

ieva

ble

perf

orm

ance

on

stat

e-of

-the

-art

sca

labl

e sy

stem

s.�

Sen

sitiv

ity, U

Q a

nd Q

MU

will

hav

e pe

netr

ated

app

licat

ion

area

s w

here

the

forw

ard

prob

lem

has

suf

ficie

nt fi

delit

y.�

Mul

tisca

le/m

ultip

hysi

cs m

odel

ing

will

be

ubiq

uito

us.

�T

he ti

mes

tep

limit

will

be

set b

y ac

cura

cy c

onsi

der

atio

ns, n

ot

for s

tabi

lity

need

s.�

We

will

hav

e a

port

able

par

alle

l pro

gram

min

g an

d ex

ecut

ion

mod

el.

29

Wha

t doe

s a

join

t Mat

h/C

S In

stitu

te

look

like

?

�In

stitu

te is

:–

Sta

ffing

of M

ath

and

CS

, Lab

s an

d U

nive

rsiti

es, c

ontin

uum

of s

kills

.–

App

roxi

mat

ely

10-2

0 m

embe

rs, s

ingl

e P

I.–

Sin

gle

them

e w

ith m

ultip

le p

roje

cts.

–In

tegr

ated

Mat

h an

d C

S e

ffort

.

�S

ize:

–$1

M to

o sm

all.

$3M

OK

.

�H

ow d

o w

e ob

tain

an

inte

grat

ed e

ffort

?–

Foc

us o

n pr

oble

ms

that

req

uire

syn

ergi

stic

Mat

h &

CS

effo

rt.

�H

ow d

o w

e in

trod

uce

join

t acc

ount

abili

ty?

–P

ropo

sed

wor

k m

ust c

lear

ly d

emon

stra

te n

eed

for

com

bine

d M

ath

& C

S

rese

arch

to s

ucce

ed.

–M

ilest

ones

mus

t dep

end

on jo

int e

ffort

.

30

Ele

men

ts o

f a s

ucce

ssfu

l pro

gram

�W

hat a

re s

igns

of s

ucce

ss?

–S

cien

ce te

ams

beat

ing

dow

n th

e do

or to

get

wha

t we

prod

uce.

–V

endo

rs p

icki

ng u

p co

ncep

ts w

e de

velo

p.–

App

licat

ion

code

s re

ly m

ore

on li

brar

ies.

–S

ucce

ssfu

l mul

tisca

le/m

ultip

hysi

cs a

pplic

atio

ns.

�A

re t

here

ext

erna

l dep

ende

ncie

s th

at m

ust b

e ta

ken

into

ac

coun

t?–

Arc

hite

ctur

e ro

adm

aps.

–In

dust

ry la

ngua

ge s

tand

ards

, pro

gram

min

g A

PIs

.

31

Add

ition

al C

omm

ents

�A

re t

here

add

ition

al it

ems

that

aro

se in

dis

cuss

ion

that

nee

d to

be

bro

ught

to li

ght?

–H

ow w

ill w

e ge

t lon

g-te

rm s

uppo

rt fo

r to

ols

and

softw

are

we

prod

uce?

–In

stitu

tes

mus

t be

long

-live

d, 5

-10

year

s ne

eded

to r

ealiz

e th

e vi

sion

.