Parallel vs Distributed

8/12/2019 Parallel vs Distributed

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Eugene Magnier Astro 734 : Lecture 09

Eugene MagnierAstronomy 734Spring 2006

Parallel and DistributedProcessing


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Lecture !er!ie"

Moti!ations

understanding bottlenec#s

multiple$ processing or %&' (ypes o) Parallel and Distributed Processing

Multitas#ing !s Multit*reading

Multicomputer !s multiprocessor

Parallel processing !s distributed processing

MP% !s P+M ,ondor Pan,ontrol Pan(as#s


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!er!ie" - Moti!ations

(*e Problem - processing one.at.a.time ta#es too long (*e Solution - do more t*an one at a time/

nderstanding your bottlenec#s- (oo muc* data or too muc* "or#' measure your processing speed

add timing points "it*in t*e code time complete1 representati!e obs

count your data %&s

is it local or net"or#' measure your %&s

time dd i))ile o)&de!&null

e$amine your t*roug*puts- seconds )or processing' seconds )or %&' compare ,P 5igacycles & sec to %& Megabytes & sec


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Multitas#ing !s Multit*reading

(*e simplest parallel processing-

multiple obs on your o"n mac*ine

Multitas#ing separate programs

independent data

*andled by #ernel automatically Multit*reading

multiple realiations o) t*e sameprogram

s*ared memory

independent processing

re8uires care "it* memory andmessages

programs must be "ritten to usemultit*reading

chip 1 chip 2

collect results

chip 1 chip 2

read data

collect results

single program

multiple programs


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Eugene Magnier A t 734 L t 09


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Parallel processing !s Distributed processing

Distributed processing-

multiple obs "*ic* re8uire little or no intercommunication

Data is not s*ared bet"een distributed obs E$amples

large number o) indi!idual images *undreds o) distinct spectra data preparation

Parallel processing- multiple obs re8uire )re8uent communication

Data is *ea!ily s*ared bet"een obs

E$amples large >.body simulations )ull.s#y astrometric & p*otometric analysis !ery large matri$ in!ersion !ery large ??(s



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MP% !s P+M

MP% - Message Passing %nter)ace Library

allo" distributed processes to

s*are data send messages bloc# )or messages *ig*ly e))icient )or message passing

P+M - Parallel +irtual Mac*ine

also pro!ides a message passing library

includes resource and process control layer

pro!ides a single point )or interactions bot* re8uire de!eloper to program to t*e model



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,ondor

Layered on top o) P+M Pro!ides management o) distributed obs

obs don'tre8uire recompilation e$pects *eterogeneous cluster with machine owners! some"*at restricti!e on be*a!ior o) obs



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Pan,ontrol

manages distributed obs li#e ,ondor manage mac*ines in pool

obs can re8uest or demand speci)ic mac*ines simple user inter)ace inter)aces "it* Pan(as#s

host add foo

host add bar

job program

job -host foo program

job +host bar program

job +host baz program

for i 0 100

sprintf input chip.%02d.fits

sprintf output chip.%02d.flat

job process $input $output

end

check job 0

stdout job 0stderr job 1

delete job 5

host off foo

host on foo



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Pan(as#s

@egularly.sc*edule tas# e!aluation (as#s potentially spa"n obs obs may be local or parallel obs may be targetted to speci)ic mac*ines

task datalist

command ls /data/foo

periods -exec 5.0

periods -timeout 50.0 periods -poll 1.0

task.exit 0

queueprint stdout

queuedelete stdout

end

task.exit 1

queuepush failure "task failed" end

end

task datalist

periods -exec 5.0

periods -timeout 50.0

periods -poll 1.0

task.exec

$file = `next.file`

if ($file == "none")

break

end

command cp /data/foo/$file /data/bar

end

task.exit 0

queueprint stdout

queuedelete stdout

queuepush copied $file

end

task.exit 1

queuepush failure $file

end end



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g g Astro 734 : Lecture 09

pcontrol ser!er,*ec#,ontrol

,*ec#(as#s

,*ec#,*ild

tas# 8ueue

ob 8ueue

user cmds readline

Pan(as#s - Process Loop & ser %nter)ace

ser issues commands1 loads scripts !ia readline ,lient & Ser!er Model designed1 not yet implemented



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g g

run tas# prep

construct ob cmd

submit ob

c*ec# tas# timer

ne$t tas#

c*ec# ob timer

c*ec# ob status

return ob results

ne$t ob

Pan(as#s - (as# Loop !s ob Loop

Limited number o) tas#s & obs per interrupt cycle ma$imum o) one e!aluation per cycle tas# & ob 8ueues are continuously cycled



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g g

idle

Pcontrol Bost Cueue States

ne"

do"n

done

o))

busy

delete

SE@- *ost .on name

SE@- *ost .delete name

SE@- *ost .o)) name SE@- *ost .o)) name

SE@- *ost name

SE@- *ost .o)) name

LP- Startob

Pan(as#s - Pcontrol Bost States

Pcontrol monitors *osts *osts may be added & deleted =do"n= *osts are automatically re.attac*ed ss* communication to t*e *osts



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busy

ne"

pending done

e$it cras*

delete

:

22

P: (:

(: (2

*ung

Pan(as#s - Pcontrol ob States

Pcontrol mo!es obs and *osts in parallel sers may delete pending obs1 #ill running obs1 or *ar!est cras*&e$it obs< Pan(as#s sc*eduler is normal Pcontrol =user=



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command srcsc*eduler

pcontrol client

readline

,*ec#,*ild

Pcontrol Ser!er

,*ec#,*ild

,*ec#,*ild

pcontrol client

pcontrol client

Pan(as#s - Pcontrol Process Loop & ser %nter)ace

ser issues commands1 loads scripts !ia readline !ery similar to Pan(as#s sc*eduler loop same code



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command srcpcontrol

c*ild processreadline ,*ec#,*ild

Pclient

Pan(as#s - Pclient Process Loop & ser %nter)ace

ser issues commands Pclient launc*es1 monitors bac#ground c*ild process reports stdout1 stderr1 e$it status !ery simple command set



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Pan(as#s - loading tests

Pclient - demonstrated ob rates o) 900 per second Pcontrol - tests to manage 62 nodes1 9:0 obs per second total rate Pan(as#s - sc*edule & *ar!est 90 obs per second @e8uirement - FF 640 obs & 4 seconds :4 per second



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%PP computing - distributed processing

classical parallel eg MP% !s distributed processing increase total MB increase total %& rate =targeted= processing

(A >odes S#y >odes

Gig S"itc*

connection toobser!atory system

metadata dbser!er

pantas#sser!er

D+ser!er

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Parallel vs Distributed