A F amewo k and Me hodology o Pe o mance
P edic ion o HPC Wo kloads
J´
ulia O eu∗, Ma c Clasc`
a∗, Ma a Ga cia-Gasulla∗, Jes´
us Laba a∗†, Elise Jennings‡
∗Ba celona Supe compu ing Cen e , Ba celona, Spain
†Uni e si a Poli `
ecnica de Ca alunya, Ba celona, Spain
‡Pa Tec AG, M¨
unchen, Ge many
E-mail: {julia.o eu, ma c.clasca, ma a.ga cia, jesus.laba a}@bsc.es
elise.jennings@pa - ec.com
Keywo ds—HPC Wo kloads, Pe o mance P edic ion, Run ime
Ha dwa e Coun e s, Ins uc ions pe Cycle (IPC), Pe o mance
Tools, Pa allel Applica ions, Reg ession ees, ML & AI
I. EXTENDED ABSTRACT
A. In oduc ion
The p esen ed pos e ou lines an app oach o p edic ing
he pe o mance o High-Pe o mance Compu ing wo kloads
(HPC). By u ilizing da a ga he ed om un ime ha dwa e
coun e s ac oss a ange o HPC applica ions and benchma ks,
we de elop an a i icial in elligence model based on ensemble
ee algo i hms. This model is capable o o ecas ing he
pe o mance o o he HPC applica ions. This wo k di e s om
cu en esea ch by ocusing on he g anula i y o aining
and p edic ion. Speci ically, ou model is de eloped u ilizing
indi idual compu a ion bu s s as inpu samples o aining.
Th ough his app oach, we p o e ha a p edic ion o he
ins uc ions pe cycle (IPC) me ic o unseen applica ions
is possible based on a chi ec u al pe o mance coun e s ha
can be ob ained easily wi h al eady used and con enien
pe o mance ools.
B. Resea ch and De elopmen
The esea ch line ocuses on explo ing he possibili y o
p edic ing he pe o mance, and ul ima ely he execu ion ime,
o known wo kloads in HPC machines p io o hei execu ion.
P edic ing he pe o mance o HPC applica ions o
wo kloads is a complex ask wi h a widely discussed se
o app oaches and me hodologies. The la es esea ch wo k
on his opic ocuses on da a-d i en me hodologies using
machine lea ning, bu p e ious s udies p esen di e en ypes
o p edic ion me hods: analy ical me hods, ha can be de i ed
om a machine ep esen a ion, o can be he esul o a
s a is ical wo k; and non-analy ical me hods, which comp ise
he a i icial in elligence echniques and he simula ion
me hods [1] .
Ou app oach dis inguishes i sel om exis ing esea ch
by i s ocus on he g anula i y o aining and p edic ion.
Using pe o mance analysis ools Ex ae and Pa a e
[2] [3], de eloped in Ba celona Supe compu ing Cen e
(BSC), we ex ac da a a he compu a ional bu s le el and
a ge he IPC me ic o e e y bu s as a pe o mance measu e.
Fig. 1. Ex ae and Pa a e gene aliza ion o bu s
A bu s is de ined as he ime in e al o ac i e ope a ion
be ween wo successi e e en s in a p ocess. In Figu e 1,
we p o ide a isual example o a imeline om a execu ion
ep esen a ion o an applica ion unning wo p ocesses. The
ac i i ies wi hin hese p ocesses ha e been ca ego ized in o
wo ypes: communica ion bu s s, indica ed in ed, and com-
pu a ion bu s s, ep esen ed in blue. These ca ego iza ions a e
de i ed om MPI e en ma ke s, wi h g een lags ini ia ing
compu a ion and ed lags ma king he s a o communica ion.
In ou s udy, we concen a e on compu a ion bu s s, which
we e e o as use ul bu s s. These bu s s a e pe iods whe e
he applica ion is ac i ely engaged in da a p ocessing and
execu ing ins uc ions. These indi idual use ul bu s s se e
as he inpu samples o he model, as one en y o ou
da ase , highligh ing ha his app oach p o ides a e y p ecise
pe o mance p edic ion o a e y speci ic applica ion’s pa .
C. Fea u es and wo kload cha ac e iza ion
To acili a e ou s udy, we conside a se o Pe o mance
Applica ion P og amming In e ace (PAPI) coun e s [4] as he
ounda ional da a. These coun e s include he o al numbe o
ins uc ions comple ed (N), he o al cycles seen by h ead
(Cyc), memo y load ins uc ions (NLD), memo y s o e in-
s uc ions (NSR), b anch ins uc ions (NBR), and o al cache
miss e en s a bo h L1 and L3 le els (missL1,missL3).
We no malize he da a in each bu s by using he a io o
ins uc ions and cache misses ins ead o absolu e coun e s
alues. This allows us o compa e any indi idual bu s om
any pa o a ace and om any applica ion. F om he
coun e s, we cha ac e ize he a iables using he ollowing
compu a ions:
•Ins uc ion mixes, which a e a ios o speci ic ins uc-
ion ypes o he o al numbe o ins uc ions, such as
LD =NLD
N o loads, SR =NSR
N o s o es, and
BR =NBR
N o b anches.
•Cache miss a es, calcula ed as L1=missL1
NLD+NSR o
he L1 cache and L3=missL3
NLD+NSR o he L3 cache,
which a e indica o s o memo y access e iciency.
•The a e age node concu ency du ing co e execu ion,
h ough he in eg al o he Pa allelism unc ion o e
he ime om Tbegin o Tend:RTend
Tbegin P a ( )d .
•IPC, which is he a ge pe o mance me ic, gi en by
he equa ion IP C =N
Cyc .
D. Da a sou ces
The p ocess in ol es he selec ion o a speci ic se o
benchma ks and ke nels o ex ac da a and adap i o he
pu pose o aining he models. Addi ionally, a sepa a e se o
applica ions has been chosen o e alua ing he pe o mance
o he ained models ( es ing).
The selec ion o ke nels and benchma ks o he aining
se is a pi o al decision ha acili a e he ep esen a ion o he
bu s space, enabling he gene aliza ion o new applica ions.
Fo each applica ion selec ed o he aining se , we’ e a ied
he p oblem sizes o cap u e a comp ehensi e da ase . The
na u e o he a ia ion depends on he applica ion’s cha ac e is-
ics—i could be he size o an a ay, he g anula i y o a mesh,
o he complexi y o inpu s. Addi ionally, we’ e scaled he
compu a ional wo kload by al e ing he numbe o p ocesses
wi hin a single node o each a ian o p oblem size. This
me hodical app oach allows us o cons uc a aining da ase
ha co e s a wide ange o scena ios.
E. Da a ex ac ion F amewo k
Fig. 2. Flowcha o he da a ex ac ion pa o he aining amewo k
The da a ex ac ion p ocess o each applica ion, bo h o
aining and es ing da ase s, is ou lined in Figu e 2.
The depic ed da a ex ac ion p ocess begins wi h he execu-
ion o a known applica ion on a known machine. In his case,
we ha e used he Ma eNos um 4 supe compu e ’s a chi ec u e
o e e ence. We ob ain he use ul bu s s om a p og am
execu ion using he BSC ools, which a e hen p ocessed in o
a ea u es o ma .
F. AI Model
The s udy in es iga es a ange o di e se machine lea ning
algo i hms wi h he aim o aining an e ec i e p edic i e
model. In his explo a ion, we ha e employed a 10- old
c oss- alida ion me hod on ou aining da ase o e alua e
he pe o mance and compa ibili y o hese algo i hms
wi h ou ype o da a. The empi ical esul s highligh a
clea ad an age o using Boos ing Ensembles ha ely
on decision ees, leading o a ocus on XGBoos me hod
[5] . This also ma ches wi h p e ious esea ch conclusions [6].
To ensu e a ai assessmen o he models, we’ e de eloped
a me hod o injec hese p edic ions in o Pa a e aces and
we’ e de ised speci ic e o me ics in o he ace ailo ed o
e alua e he pe o mance ou comes o each model. This allows
o a mo e p ecise analysis o how well he models p edic
applica ion pe o mance.
G. Summa y
The pos e shows he main indings and discusses ou ex-
plo a ion o his opic. We p esen a me hod o da a collec ion
and p ep ocessing based on low-e o p og am ins umen a-
ion and au oma ic ools, as i is well known in he li e a u e
ha being able o collec da a au oma ically and building he
model e o lessly is c i ical o end up wi h aluable and
con enien aining and p edic ion wo k low.
We also s udied how changing he ea u e se , he aining
da a size o he machine lea ning algo i hm a ec s he accu-
acy. We discuss a me hod o cha ac e ize compu a ional bu s s
based on ins uc ion mix ea u es and ins an aneous machine
concu ency ha is la e able o classi y, using ees, he IPC
o unseen bu s s. The e o e, we p o e ha i is possible o
o esee he pe o mance o a whole unseen applica ion ace
based on his cha ac e iza ion me hod.
II. ACKNOWLEDGMENT
This wo k bas been published in p oceedings o he 11 h
In e na ional BSC Se e o Ochoa Doc o al Symposium, 2024.
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J´
ulia O eu is a s uden in he i s coho o he
Bachelo ’s deg ee in A i icial In elligence a Uni-
e si a Poli `
ecnica de Ca alunya (UPC). Since 2023,
has been a Junio Resea ch Enginee a he Ba celona
Supe compu ing Cen e (BSC) in he Bes P ac-
ices o Pe o mance and P og ammabili y (BePPP)
g oup, whe e she cu en ly wo ks as a pe o mance
p edic ion analys o HPC Applica ion Wo kloads.