Deployment of Randomised Optimisation Algorithms Benchmarking in DAPHNE

Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
High Performance, Edge And Cloud computing
HiPEAC 2024, 17-19 January 2024, Munich
EVEREST + DAPHNE: Workshop on Design and Programming High- performance, distributed,
reconﬁgurable and heterogeneous platforms for extreme-scale analytics
Orion 2 10:00 - 17:30 Friday, 19 January 2024
Integrated Data Analysis Pipelines for Large-Scale Data Management, HPC and Machine Learning
Deployment of Randomised
Optimisation Algorithms
Benchmarking
in DAPHNE
19 January 2024, Munich
@HiPEAC: EVEREST + DAPHNE
Aleš Zamuda
<ales.zamuda@um.si>
Acknowledgement: this work is supported by EU project no. 957407 (DAPHNE).
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Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 1/141

Introduction & Outline: Aims of this Talk
1 (5 minutes) Part I: Background – Optimization Algorithms
and 100-Digit Challenge
2 (5 minutes) Part II: Method: DISHchain3e+12 Algorithm
3 (2 minutes) Part III: Results
4 (1 minutes) Part IV: Conclusion with Takeaways
5 (1 minute) Questions, Misc
6 (Appendix) Business, Marketing

Benchmarking
in DAPHNE
—
I. Background: Optimization,
100-Digit Challenge
—

Optimization Beginnings - Optimization is
”Everywhere”
• Time: optimizing distribution of what is matter and what is not
(anti-matter), what is energy and what is not (dark energy), etc.:
according to the function of Nature, the system is propelled through
optimizing its constituents dynamics.
• Organic systems combination and propulsion: life (optimization).
• Optimality and optimization modeling (human builds tools).
• Describing ways of acchieving optimality.
• Mathematical optimization procedure deﬁned (Kepler).
• Stepping towards optimum (Newton), gradient method (Lagrange).
• Multi-objective optimization (Pareto):
• meta-criterion (A ⪯ B): make criteria ordered by
dominance.
f′
(x) =
∆f(x)
∆x
,
f∗
(x) = f(x) + ∆xf′
(x).
1
2 2
f
x
x 1
f
( )
A
B
C
D
f x
f(B)
(A)
f
f(D)
0
0
E
f(E)
F
G f
(C)
f
f(F)
(G)

Introduction to Optimization Algorithms
and Mathematical Programming
• Global optimization, mathematical programming, digital computers.
• Computing Machines + Intelligence = Artiﬁcial Intelligence.
• Computational Intelligence.
• Simplistic numerical optimization algorithms:
hill climbing, Nelder-Mead, supervised random search,
simulated annealing, tabu search.
• Optimization: constrained, inseparable, multi-modal, multi-objective,
dynamic, noisy, high dimensional/large-scale/big-data, deceptive, etc.
• multi-objective: f(x)): Pareto optimal approximation set.

Evolutionary Computation and Algorithms
• Evolution theory: C. Darwin (1859), Weismann, Mendel.
• Popularization: darwinism (Huxley), neodarwinism
(Romanes).
• Generational: reproduction, mutation, competition,
selection.
• Evolutionary Computation: Evolutionary Algorithms (EAs)
• population generations (reproduction-based),
• mutation, crossover, selection (evolutionary operators),
• EAs comprised of different mechanisms.
• These algorithms share several common
mechanisms/operators,
• good conﬁgured DEs were prevalent at the winning
positions of all (CEC, including ICEC 1996) competitions on
optimization.

Evolutionary Computation and Algorithms: Given
Names
• Simulated Annealing (SA),
• Tabu Search (TS),
• Genetic Algorithms (GA),
• Genetic Programming (GP),
• Evolutionary Programming (EP),
• Memetic Algorithms (MA),
• Evolution Strategy (ES),
• Artificial Immune Systems (AIS),
• Cultural Algorithms (CA)
• Swarm Intelligence (SI),
• Particle Swarm Optimization
(PSO),
• Firefly Algorithm (FA),
• Ant Colony Optimization (ACO),
• Artificial Bee Colony (ABC),
• Cuckoo Search (CS),
• Artificial Weed Optimization (IWO),
• Bacterial Foraging
Optimization(BFO),
• Estimation of Distribution Alg. (EDA),
• Harmony Search (HS),
• Gravitational Search Algorithm
(GSA),
• Biogeography-based
Optimization(BBO),
• Differential Evolution (DE)
and its variants (jDE, L-SHADE, DISH),
• ... and many more, including
hybrids.

Range of Applications of the Optimization Algorithms
• Meta-heuristics algorithms, applicable to:
• (architectural) morphology (re)construction
(vivo/technical),
• artificial life:
• modeling ecosystem and environmental living conditions,
• e.g.: (automatic) procedural tree modeling,
interactive ecosystem breeding.
• pattern recognition, image processing, computer vision,
• language/documents understanding, speech processing,
• robotics, bioinformatics, chemical engineering,
manufacturing,
• oil search, nuclear plant safety, finance, electrical
engineering,
• energy, big data, data mining, security, ocean/space
research,
• systems of systems, ..., artificial intelligence.

Differential Evolution (DE)
• A ﬂoating point encoding EA for global optimization over
continuous spaces,
• through generations,
the evolution process improves population of vectors,
• iteratively by combining a parent individual and
several other individuals of the same population,
using evolutionary operators.
• We choose the strategy jDE/rand/1/bin
• mutation: vi,G+1 = xr1,G + F × (xr2,G − xr3,G),
• crossover:
ui,j,G+1 =
(
vi,j,G+1 if rand(0, 1) ≤ CR or j = jrand
xi,j,G otherwise
,
• selection: xi,G+1 =
(
ui,G+1 if f(ui,G+1) < f(xi,G)
xi,G otherwise
,

Algorithm DE
1: algorithm canonical algorithm DE/rand/1/bin (Storn,
1997)
Require: f(x) – fitness function; D, NP, G – DE control parameters
Ensure: xbest – includes optimized parameters for the fitness function
2: Uniform randomly initialize the population (xi,0, i = 1..NP);
3: for DE generation loop g (until g < G) do
4: for DE iteration loop i (for all vectors xi,g in current population) do
5: DE trial vector computation xi,g (mutation, crossover):
6: vi,g+1 = xr1,g + F × (xr2,g − xr3,g);
7: ui,j,g+1 =
(
vi,j,g+1 if rand(0, 1) ≤ CR or j = jrand
xi,j,g otherwise
;
8: DE selection using fitness evaluation f(ui,G+1):
9: xi,g+1 =
(
ui,g+1 if f(ui,g+1) < f(xi,g)
xi,g otherwise
;
10: end for
11: end for
12: return best obtained vector (xbest);

Control Parameters Self-Adaptation
• Through more suitable values of control parameters the
search process exhibits a better convergence,
• therefore the search converges faster to better solutions,
which survive with greater probability and they create
more offspring and propagate their control parameters
• Recent study with cca. 10 million runs of SPSRDEMMS:
A. Zamuda, J. Brest. Self-adaptive control parameters’
randomization frequency and propagations in differential
evolution. Swarm and Evolutionary Computation, 2015,
vol. 25C, pp. 72-99.
DOI 10.1016/j.swevo.2015.10.007.
– SWEVO 2015 RAMONA / SNIP 5.220

Self-adaptive control parameters’ randomization
frequency and propagations in differential evolution
– Overview
• Randomization frequency
inﬂuences performance
(SPSRDEMMS on right)
• Suggesting values for
different problems
• 0.1 to 0.8 for τF,
0.05 to 0.25 for τCR
• Empirical insight into
operation of the
randomization mechanism

Listing Some More DE-Family Algorithms Proposed
• My algorithms (CEC – world championships on EAs):
• SA-DE (CEC 2005: SO) – book chapter JCR,
• MOjDE (CEC 2007: MO) – vs. DEMO 40/57 IR, 39/57 IH,
• DEMOwSA (CEC 2007: MO) – rank #3, 53 citations,
• DEwSAcc (CEC 2008: LSGO) – 63 citations,
• DEMOwSA-SQP (CEC 2009: CMO) – rank #2, 47 citations,
• DECMOSA-SQP (CEC 2009: CMO) – rank #3 at 2 functions,
• jDENP,MM (CEC 2011: RWIC) – LNCS SIDE 2012,
• SPSRDEMMS (CEC 2013: RPSOO); Large-scale @SWEVO.
• DISH (SWEVO 2019) – best CEC 2015 & 2017 results.
• Performance assessment of the algorithms at world EA
championships: several times best on some criteria
(also won CEC 2009 dynamic optimization competition).
• Performance assessment on several industry challenges
• procedural tree models reconstruction (ASOC 2011, INS
2013),
• underwater glider path planning (ASOC 2014),
• hydro-thermal energy scheduling (APEN 2015),
• RWIC (Real World Industry Challenges) - CEC 2011; 2013, ...

SPSRDEMMS: Example of Optimization Mechanisms
• SPSRDEMMS = Structured Population Size Reduction
Differential Evolution with Multiple Mutation Strategies
• canonical DE, upgraded with: mechanism of F and CR
control parameters self-adaptation, mutation strategy
ensembles, population structuring (distributed islands),
and population size reduction.
• is an extension of the jDENP,MM variant (Zamuda and
Brest, SIDE 2012) and was published at CEC 2013
(competition).
• The SPSRDEMMS, for a ﬁxed part of the population (NPbest
number of individuals at end of the entire population),
executes only the best/1 strategy.
• This part of population (which might be seen as a
sub-population) has a separate best vector index, xbest bestpop.
• The ﬁrst part of the population (mainpop) operates on target
vectors xi ∈ {x1...xNP−NPbest} and second part (bestpop)
operates on target vectors xi = {xNP−NPbest+1...xNP}.
• Both strategies generate mutation vectors using all vectors of
the population x1...xNP, i.e. r1, r2, r3 ∈ {1..NP}.

Self-adaptive control parameters’ randomization
frequency and propagations in differential evolution
– Methods
• G. Karafotias, M. Hoogendoorn, A. Eiben,
Parameter control in evolutionary algorithms:
trends and challenges, IEEE Trans. Evolut.
Comput. 19 (2) (2015) 167–187.
• A. Zamuda, J. Brest, E. Mezura-Montes,
Structured population size reduction
differential evolution with multiple mutation
strategies on CEC 2013 real parameter
optimization, in: Proceedings of the 2013 IEEE
Congress on Evolutionary Computation (CEC),
vol. 1, 2013, pp. 1925–1931.
• J. Brest, S. Greiner, B. Bošković, M. Mernik, V.
Žumer, Self-adapting control parameters in
differential evolution: a comparative study on
numerical benchmark problems, IEEE Trans.
Evolut. Comput. 10 (6) (2006) 646–657.
• Parameter control study
• Systematic approach to
answering questions about the
control parameters
mechanism
• For certain interesting
functions, deeper insight is
shown

Other Enhancements / Improvements / Mechanisms
in DE
DE, jDE, SaDE, ODE, DEGL, JADE, EPSDE; ϵ-DE, DDE, CDE; PDE,
GDE, DEMO, MOEA/D, ...
• Swagatam Das and Ponnuthurai Nagaratnam Suganthan.
”Differential evolution: a survey of the
state-of-the-art.” IEEE Transactions on Evolutionary
Computation 15(1), 2011: 4-31. DOI:
10.1109/TEVC.2010.2059031.
CoDE, Compact DE, L-SHADE, Binary DE,
Successful-Parent-Selecting Framework DE, ...
• Swagatam Das, Sankha Subhra Mullick, and Ponnuthurai
Nagaratnam Suganthan.
”Recent Advances in Differential Evolution –
An Updated Survey.”
Swarm and Evolutionary Computation, Volume 27, April
2016, Pages 1-30, 2016.
DOI: 10.1016/j.swevo.2016.01.004.
Several hybridizations, improvements, and general
mechanisms.

Functions of the Problems in 100-Digit Challenge
• The stated goal of the 100-Digit Challenge benchmark is:
• to understand better “the behavior of swarm and evolutionary algorithms
as single objective optimizers” (explainable AI)
• Continuous multi-dimensional (D) numerical functions, f(x)
• Solution quality is measured in number of precise digits (max. 10 per function)
• 10 digits added up per 10 functions = score of 100
No. Problem name X∗
D Search Range
1 Storn’s Chebyshev Polynomial Fitting Problem 1 9 [-8192,8192]
2 Inverse Hilbert Matrix Problem 1 16 [-16384,16384]
3 Lennard-Jones Minimum Energy Cluster 1 18 [-4,4]
4 Rastrigin’s Function 1 10 [-100,100]
5 Griewangk’s Function 1 10 [-100,100]
6 Weierstrass Function 1 10 [-100,100]
7 Modiﬁed Schwefel’s Function 1 10 [-100,100]
8 Expanded Schaffer’s F6 Function 1 10 [-100,100]
9 Happy Cat Function 1 10 [-100,100]
10 Ackley Function 1 10 [-100,100]
X∗
denotes an optimum (transformed to 1 for all functions).

Benchmarking
in DAPHNE
—
II. Method: DISHchain3e+12
Algorithm
—

DISH - a Population-based Optimizer at SWEVO (Q1)

DISH – Algorithm Deﬁnition (Pseudocode,
Parameters)
• DISH in C++ code,
• published in SWEVO (served BM),
• mow applied for 100-digit challenge,
• benchmarked using HPC (SLING).
• Historical memory size H = 5,
• archive size A = NP,
• initial population size
NP0 = 25
√
D log D and
• minimum population size
NPmin = 4,
• for pBest mutation p = 0.25 and
pmin = pmax/2,
• with initialization of all but one
memory values at MF = 0.5 and
MCR = 0.8 and
• the one memory entry with
MF = MCR = 0.9, and
• pBest-w strategy with weight value
limits Fw at 0.7F, 0.8F, and 1.2F for

DISH – Algorithm Mechanisms Detailed
xj,i = U
h
lowerj, upperj
i
; ∀j = 1, . . . , D; ∀i = 1, . . . , NP, (1)
MCR,i = MF,i = 0.5; ∀i = 1, . . . , H, (2)
vi = xr1 + F (xr2 − xr3) , (3)
vi = xi + Fi

xpbest − xi

+ Fi (xr1 − xr2) , (4)
Fi = C

MF,r, 0.1

, (5)
uj,i =

vj,i if U [0, 1] ≤ CRi or j = jrand
xj,i otherwise
. (6)
CRi = N
h
MCR,r, 0.1
i
. (7)
xi,G+1 =
(
ui,G if f

ui,G

≤ f

xi,G

xi,G otherwise
, (8)
MF,k =

meanWL (SF) if SF ̸= ∅
MF,k otherwise
, (9)
MCR,k =

meanWL (SCR) if SCR ̸= ∅
MCR,k otherwise
, (10)
meanWL (S) =
P|S|
k=1
wk • S2
k
P|S|
k=1
wk • Sk
(11)
wk =
abs

f

uk,G

− f

xk,G

P|SCR|
m=1
abs

f

um,G

− f

xm,G
. (12)
NPnew = round

NPinit −
FES
MAXFES
∗ (NPinit − NPf)

,
(13)
p = pmin +
FES
MAXFES
(pmax − pmin). (14)
vi = xi + Fw(xpBest − xi) + F(xr1 − xr2), (15)
Fw =





0.7F, FES 0.2MAXFES,
0.8F, FES 0.4MAXFES,
1.2F, otherwise.
(16)
wk =
r
PD
j=1

uk,j,G − xk,j,G
2
P|SCR|
m=1
r
PD
j=1

um,j,G − xm,j,G
2
. (17)
Colors:
• black – L-SHADE base,
• gray – overloaded,
• blue – new w/ DISH.

Benchmarking
in DAPHNE
—
III. Results – Scores, Comparison,
Impact
—

Tuned parameter values
for DISHchain3e+12 algorithm
Function MAX FES NP0
1 1e+5 25
√
D log D
2 1e+6 25
√
D log D
3 1e+7 25
√
D log D
4 1e+8 250
√
D log D
5 1e+6 25
√
D log D
6 1e+5 25
√
D log D
7 1e+8 2500
√
D log D
8 1e+11 10000
√
D log D
9 3e+12 25
√
D log D
10 1e+7 25
√
D log D
• MAX FES: the maximum function evaluations allowed
• Function 9 required the most MAX FES to solve
• For functions 4, 7, and 8, larger population NP0 used

Observed Problem Difﬁculty
Function evaluations to reach accuracy up to certain digit
10000
1x106
1x108
1x1010
1x1012
0 1 2 3 4 5 6 7 8 9
FES
Digits
f1
f2
f3
f4
f5
f6
f7
f8
f9
f10
• combined on all functions 1–10, accuracy evolution plot
• using logscale axis for FES (function evaluations)

Score Obtained: 100
Fifty runs for each function sorted by the number of correct
digits (for DISHchain3e+12 algorithm)
Num. correct digits
No. Problem name X∗
D Search Range 0 1 2 3 4 5 6 7 8 9 10 Score
1 Storn’s Chebyshev Polynomial Fitting Problem 1 9 [-8192,8192] 0 0 0 0 0 0 0 0 0 0 50 10
2 Inverse Hilbert Matrix Problem 1 16 [-16384,16384] 0 0 0 0 0 0 0 0 0 0 50 10
3 Lennard-Jones Minimum Energy Cluster 1 18 [-4,4] 0 0 0 0 0 0 0 0 0 0 50 10
4 Rastrigin’s Function 1 10 [-100,100] 0 0 0 0 0 0 0 0 0 0 50 10
5 Griewangk’s Function 1 10 [-100,100] 0 0 0 0 0 0 0 0 0 0 50 10
6 Weierstrass Function 1 10 [-100,100] 0 0 0 0 0 0 0 0 0 0 50 10
7 Modiﬁed Schwefel’s Function 1 10 [-100,100] 0 0 0 0 0 0 0 0 0 0 50 10
8 Expanded Schaffer’s F6 Function 1 10 [-100,100] 0 0 0 0 0 0 0 0 0 0 50 10
9 Happy Cat Function 1 10 [-100,100] 0 0 0 0 0 3 5 1 6 1 34 10
10 Ackley Function 1 10 [-100,100] 0 0 0 0 0 0 0 0 0 0 50 10
Score (total):) 100
X∗
denotes an optimum (transformed to 1 for all functions).

Impact: Comparing Score to Other Entries – Rank 1
https://github.com/P-N-Suganthan/CEC2019/blob/master/100-DigitChallengeAnalysisofResults.pdf

Preliminary ROAR in DAPHNE Benchmarked
Testing: convergence of a ML system
ROAR: Randomised Optimisation Algorithm
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Next Steps

Benchmarking
in DAPHNE
—
IV: Conclusion
with Takeaways
—

Conclusion with Takeaways
Conclusion: score of 100 (rank 1) vectorized benchmarking,
speed up, and impact — in the context of HPC AI.
Takeaways: 100-digit Challenge; EAs; HPC a key element
Thanks!
Acknowledgement: this work is supported by DAPHNE, funded by the European Union’s Horizon 2020
research and innovation programme under grant agreement No 957407.
10000
1x106
1x108
1x1010
1x1012
0 1 2 3 4 5 6 7 8 9
FES
Digits
f1
f2
f3
f4
f5
f6
f7
f8
f9
f10
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0 1 2 3 4 5 6 7 8 9
Combined power from 110 MW to 975 MW, step 0.01 MW#104
0
100
200
300
400
500
600
700
Individual
output
(power
[MW]
or
unit
total
cost
[$])
Cost, TC / 3
Powerplant P1 power
Powerplant P2 power
Powerplant P3 power
150
200
250
300
350
400
450
500
550
16 32 48 64 80
Seconds
to
compute
a
workload
Number of tasks (equals 16 times the SLURM --nodes parameter)
Summarizer workload
Real examples: science and HPC

Benchmarking
in DAPHNE
—
Final Slide: Questions, Misc
—

AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
Benchmarking
in DAPHNE
—
APPENDIX with Backgrounds
Marketing Materials
—

Introduction: Vectorized Benchmarking
Opportunities
A closer observation of execution
times for workloads processed in [2] is
provided in Fig. 1, where it is seen that
the execution time (color of the
patches) changes for different
benchmark executions.
Fig. 1: Execution time of full
benchmarks for different instances of
optimization algorithms. Each patch
presents one full benchmark
execution to evaluate an optimization
algorithm.
• Therefore, it is useful to consider speeding up of
benchmarking through vectorization of the tasks that a
benchmark is comprised of.
• These include e.g.,
• parallell data cleaning part of an
individual ML tile [1] or
• synchronization between tasks when
executing parallell geospatial processing
[3].
• To enable the possibilities of data cleaning
(preprocessing) as well as geospatial processing in
parallell, such opportunities ﬁrst need to be found or
designed, if none yet exist for a problem tackled.
• Therefore, this contribution will highlight some
experiences with ﬁnding and designing parallell ML
pipelines for vectorization and observe speedup gained
from that.
• The speeding up focus will be on optimization
algorithms within such ML pipelines, but some
more future work possibilities will also be provided.
[2] Zamuda, A, Brest, J., Self-adaptive control parameters’ randomization frequency and propagations in differential evolution. Swarm and
Evolutionary Computation 25C, 72-99 (2015).
[3] Zamuda, A., Hernández Sosa, J. D., Success history applied to expert system for underwater glider path planning using differential
evolution. Expert Systems with Applications 119, 155-170 (2019).

Warmup Highlights on Generative AI
w/ ChatGPT+Synthesia: visiting Canaries (ULPGC)
Photo/video: 1) Me at ULPGC EEI in the Erasmus+ cabinet (2012–); 2) with underwater
glider at ULPGC SIANI; 3) infront SIANI; 4) with autonomous sailboat at SIANI; 5)
rebooting in March 2023 (digital green) 6) HPC generated introduction

Benchmarking
in DAPHNE
—
Appendix Part I: Backgrounds
—

Background A:
HPC Workloads and
Cloud Computing

Appendix
(Vega supercomputer in TOP500)
— A Multimedia Tour —

TOP500: EuroHPC Vega (tour at ASHPC23)

AI Challenges Shortlist
(Part II: First subpart)
Faced 5 types of challenges, leading to the needs to apply HPC
architectures for benchmarking state-of-the-art topics in
1 text summarization,
2 forest ecosystem modeling, simulation, and
visualization,
3 underwater robotic mission planning,
4 energy production scheduling for hydro-thermal power
plants, and
5 understanding evolutionary algorithms.

Challenges 1: Text Summarization (Language)
For NLP (Natural Language Processing),
part of ”Big Data”.
Terms across sentences are determined
using a semantic analysis using both:
• coreference resolution (using WordNet) and
• a Concept Matrix (from Freeling).
INPUT
CORPUS
NATURAL
LANGUAGE
PROCESSING
ANALYSIS
CONCEPTS
DISTRIBUTION
PER
SENTENCES
MATRIX OF
CONCEPTS
PROCESSING
CORPUS
PREPROCESSING
PHASE
OPTIMIZATION
TASKS
EXECUTION
PHASE
ASSEMBLE
TASK
DESCRIPTION
SUBMIT
TASKS
TO PARALLEL
EXECUTION
OPTIMIZER
+
TASK DATA
ROUGE
EVALUATION
The detailed new method called
CaBiSDETS is developed in the HPC
approach comprising of:
• a version of evolutionary algorithm
(Differential Evolution, DE),
• self-adaptation, binarization,
constraint adjusting, and some more
pre-computation,
• optimizing the inputs to deﬁne the
summarization optimization model.
Aleš Zamuda, Elena Lloret. Optimizing Data-Driven
Models for Summarization as Parallel Tasks. Journal
of Computational Science, 2020, vol. 42, pp. 101101.
DOI: 10.1016/j.jocs.2020.101101

Challenges 2: Forest Ecosystem Modeling,
Simulation, and Visualization (Real World / Video)
• HPC need to process spatial data and add procedural
content, generating real-world items for producing a
video of 3D space.
Videos: https://www.youtube.com/watch?list=PL7pmTW8neV7tZf2qx1wV5zbD74sUyHL3Bv=V9YJgYO_sIA

Challenges 3: Underwater Robotic Mission Planning
• Computational Fluid Dynamics (CFD) spatio-temporal model of the
ocean currents for autonomous vehicle navigation path planning.
• Constrained Differential Evolution Optimization
for Underwater Glider Path Planning
in Sub-mesoscale Eddy Sampling.
• Corridor-constrained optimization: eddy
border region sampling — new challenge
for UGPP DE.
• Feasible path area is constrained —
trajectory in corridor around the border of
an ocean eddy.
The objective of the glider here is to sample the
oceanographic variables more efﬁciently,
while keeping a bounded trajectory.
HPC: develop new methods and evaluate them.
Video: https://www.youtube.com/watch?v=4kCsXAehAmU

Challenges 4: Energy Production Scheduling for
Hydro-thermal Power Plants
A. Glotić, A. Zamuda. Short-term combined economic and emission
hydrothermal optimization by surrogate differential evolution.
Applied Energy, 1 March 2015, vol. 141, pp. 42-56.
DOI: 10.1016/j.apenergy.2014.12.020

Challenges 5: Understanding Evolutionary Algorithms
• Evolutionary algorithms benchmarking to
understand computational intelligence of
these algorithms (→ storage requirement!),
• aim: Machine Learning to design
an optimization algorithm
(learning to learn).
• Example CI Algorithm Mechanism Design:
Control Parameters Self-Adaptation (in DE).
Video: https://www.youtube.com/watch?v=R244LZpZSG0
Application stacks for real code:
inspired by previous computational
optimization competitions in
continuous settings that used
test functions for
optimization application domains:
• single-objective: CEC 2005,
2013, 2014, 2015
• constrained: CEC 2006, CEC 2007, CEC 2010
• multi-modal: CEC 2010, SWEVO 2016
• black-box (target value): BBOB 2009, COCO
2016
• noisy optimization: BBOB 2009
• large-scale: CEC 2008, CEC 2010
• dynamic: CEC 2009, CEC 2014
• real-world: CEC 2011
• computationally expensive: CEC 2013, CEC
2015
• learning-based: CEC 2015
• 100-digit (50% targets): 2019 joined CEC,
SEMCCO, GECCO
• multi-objective: CEC 2002, CEC 2007, CEC
2009, CEC 2014
• bi-objective: CEC 2008
• many objective: CEC 2018
Tuning/ranking/hyperheuristics use. → DEs as usual
winner algorithms.

Challenges 6: new DAPHNE Use Cases
Example Use Cases
• DLR Earth Observation
• ESA Sentinel-1/2 datasets  4PB/year
• Training of local climate zone classifiers on So2Sat LCZ42
(15 experts, 400K instances, 10 labels each, ~55GB HDF5)
• ML pipeline: preprocessing,
ResNet-20, climate models
• IFAT Semiconductor Ion Beam Tuning
• KAI Semiconductor Material Degradation
• AVL Vehicle Development Process (ejector geometries, KPIs)
• ML-assisted simulations, data cleaning, augmentation
• Cleaning during exploratory query processing
[So2Sat LC42: https://mediatum.ub.tum.de/1454690]
[Xiao Xiang Zhu et al: So2Sat LCZ42: A
Benchmark Dataset for the Classification of
Global Local Climate Zones. GRSM 8(3) 2020]

HPC Initiatives
(Part II: Second subpart)
Timeline (as member) of recent impactful HPC initiatives including Slovenia:
• SLING: Slovenian national supercomputing network, 2010-05-03–,
• SIHPC: Slovenian High-Performance Computing Centre, 2016-03-04–
• ImAppNIO: Improving Applicability of Nature-Inspired Optimisation
by Joining Theory and Practice, 2016-03-09–2020-10-31
• cHiPSet: High-Performance Modelling and Simulation for Big Data
Applications, 2015-04-08–2019-04-07,
• HPC RIVR: UPGRADING OF NATIONAL RESEARCH INFRASTRUCTURES,
Investment Program, 2018-03-01–2020-09-15,
• TFoB: IEEE CIS Task Force on Benchmarking, January 2020–,
• EuroCC: National Competence Centres in the framework of EuroHPC,
2020-09-01–(2022-08-31),
• DAPHNE: Integrated Data Analysis Pipelines for Large-Scale Data
Management, HPC, and Machine Learning, 2020-12-01–(2024-11-30).

Initiatives: SLING, SIHPC, HPC RIVR, EuroCC
• There is a federated and orchestrated aim
towards HPC infrastructure in Slovenia,
especially through:
• SLING: Slovenian national supercomputing network
→ has federated the initiative push towards
orchestration of HPC resources across the country.
• SIHPC: Slovenian High-Performance Computing Centre
→ has orchestrated the ﬁrst EU funds application
towards HPC Teaming in the country
(and Participation of Slovenia in PRACE 2).
• HPC RIVR: UPGRADING OF NATIONAL RESEARCH
INFRASTRUCTURES, Investment Program
→ has provided an investment in experimental HPC
infrastructure.
• EuroCC: National Competence Centres in the framework of
EuroHPC
→ has secured a National Competence Centre (EuroHPC).
Vega supercomputer online
Consortium Slovenian High-Performance Computing Centre
Aškerčeva ulica 6
SI-1000 Ljubljana
Slovenia
Ljubljana, 22. 2. 2017
prof. dr. Anwar Osseyran
PRACE Council Chair
Subject: Participation of Slovenia in PRACE 2
Dear professor Osseyran,
In March 2016 a consortium Slovenski superračunalniški center (Slovenian High-Performance
Computing Centre - SIHPC) was established with a founding act (Attachment 1) where article 6
claims that the consortium will join PRACE and that University of Ljubljana, Faculty of
mechanical engineering (ULFME) will represent the consortium in the PRACE. The legal
representative of ULFME in the consortium and therefore, also in the PRACE (i.e., the delegate
in PRACE Council with full authorization) is prof. dr. Jožef Duhovnik, as follows from
appointment of the dean of ULFME (Attachment 2).
The consortium has also agreed that it joins PRACE 2 optional programme as contributing GP.
With best regards,
assist. prof. dr. Aleš Zamuda, vice-president of SIHPC

Initiatives: ImAppNIO, cHiPSet, TFoB, DAPHNE
Aim towards software to run HPC and improve capabilities:
• ImAppNIO: Improving Applicability of Nature-Inspired Optimisation
by Joining Theory and Practice,
→ improve capabilities through benchmarking (to understand (and to
learn to learn)) CI algorithms
• cHiPSet: High-Performance Modelling and Simulation for Big Data
Applications,
→ include HPC in Modelling and Simulation (of the process to be
learned)
• TFoB: IEEE CIS Task Force on Benchmarking,
→ includes CI benchmarking opportunities, where HPC would enable
new capabilities.
• DAPHNE: Integrated Data Analysis Pipelines for Large-Scale Data
Management, HPC, and Machine Learning.
→ to deﬁne and build an open and extensible system infrastructure
for integrated data analysis pipelines, including data management and
processing, high-performance computing (HPC), and machine learning
(ML) training and scoring https://daphne-eu.github.io/

EuroHPC Vega
Deploying DAPHNE
(Part II: Third subpart)
MODA (Monitoring and Operational Data Analytics) tools for
• collecting, analyzing, and visualizing
• rich system and application data, and
• my opinion on how one can make sense of the data for
actionable insights.
• Explained through previous examples:
from a HPC User Perspective.

MODA Actionable Insights, Explained From a HPC
User Perspective, Through the Example of
Summarization
Most interesting findings of summarization on HPC example
are
• the fitness of the NLP model keeps increasing with prolonging
the dedicated HPC resources (see below) and that
• the fitness improvement correlates with ROUGE evaluation in
the benchmark, i.e. better summaries.
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
1 10 100 1000 10000
ROUGE-1R
ROUGE-2R
ROUGE-LR
ROUGE-SU4R
Fitness (scaled)
Hence, the use of HPC
significantly
contributes to
capability of this NLP
challenge.
However, the MODA insight also provided the
useful task running times and resource
usage.

Running the Tasks on HPC: ARC Job Preparation
Parallel summarization tasks on grid through ARC.

Running the Tasks on HPC: ARC Job Submission,
Results Retrieval Merging [JoCS2020]
Through an HPC
approach and by
parallelization of tasks,
a data-driven
summarization model
optimization yields
improved benchmark
metric results (drawn
using gnuplot merge).
MODA is needed
to run again and
improve upon, to
forecast how to
set required task
running time and
resources
(predicting system
response).

Monitoring and Operational Data Analytics
• Monitor used (jobs, CPU/wall time, etc.):
Smirnova, Oxana. The Grid Monitor. Usage manual,
Tech. Rep. NORDUGRID-MANUAL-5, The NorduGrid
Collaboration, 2003.
http://www.nordugrid.org/documents/
http://www.nordugrid.org/manuals.html
http://www.nordugrid.org/documents/monitor.pdf
• Deployed at:
www.nordugrid.org/monitor/
• NorduGrid Grid Monitor
Sampled: 2021-06-28 at 17-57-08
• Nation-wide in Slovenia:
https://www.sling.si/gridmonitor/loadmon.php
http://www.nordugrid.org/monitor/index.php?
display=vo=Slovenia

MODA Example From: ARC at Jost
Example experiments from DOI: 10.1016/j.swevo.2015.10.007 (SPSRDEMMS)
– job YYULDmGOXpmnmmR0Xox1SiGmABFKDmABFKDmrtMKDmABFKDm66faPo.
Sample ARC ﬁle gridlog/diag (2–3 day Wall Times).
runtimeenvironments=APPS/ARNES/MPI−1.6−R ;
CPUUsage=99%
MaxResidentMemory=5824kB
AverageUnsharedMemory=0kB
AverageUnsharedStack=0kB
AverageSharedMemory=0kB
PageSize=4096B
MajorPageFaults=4
MinorPageFaults=1213758
Swaps=0
ForcedSwitches=36371494
WaitSwitches=170435
Inputs=45608
Outputs=477168
SocketReceived=0
SocketSent=0
Signals =0
nodename=wn003 . arnes . s i
WallTime=148332s
Processors=16
UserTime=147921.14s
KernelTime =2.54 s
AverageTotalMemory=0kB
AverageResidentMemory=0kB
LRMSStartTime=20150906104626Z
LRMSEndTime=20150908035838Z
exitcode=0

EuroCC HPC: Vega (TOP500 #106, HPCG #56 — June 2021)
• Researchers apply to EuroHPC JU calls for access.
• Regular calls opened in 2021 fall (Benchmark Development).
• https://prace-ri.eu/benchmark-and-development-access-information-for-applicants/
• 60% capacities for national share (70% OA, 20% commercial, 10% host (community, urgent
priority of national importance, maintenance)) + 35% EuroHPC JU share (approved applications)
• Has a SLURM dev partition for SSH login (SLURM partitions w/ CPUs: login[0001-0004]=128;
login[0005-0008]=64; cn[0001-384,0577-0960]=256; cn[0385-0576]=256; gn[01-60]=256).
Listing 1: Setting up at Vega — slurm dev partition access (login).
1 [ ales . zamuda@vglogin0007 ˜]$ s i n g u l a r i t y pull qmake . s i f docker : / / ak352 /qmake−opencv
2 [ ales . zamuda@vglogin0007 ˜]$ s i n g u l a r i t y run qmake . s i f bash
3 cd sum; qmake ; make clean ; make
4
5 [ ales . zamuda@vglogin0007 ˜]$ cat runme . sh
6 # ! / bin / bash
7 cd sum time mpirun
8 −
−mca btl openib warn no device params found 0
9 . / summarizer
10 −
−useBinaryDEMPI −
−i n p u t f i l e mRNA−1273−t x t
11 −
−withoutStatementMarkersInput
12 −
−printPreprocessProgress calcInverseTermFrequencyndTermWeights
13 −
−printOptimizationBestInGeneration
14 −
−summarylength 600 −
−NP 200
15 −
−GMAX 400
16 summarizer . out . $SLURM PROCID
17 2 summarizer . err . $SLURM PROCID
Text summarization/generation systems
are getting more and more useful
and accessible on deployed systems
(e.g. OpenAI’s ChatGPT, Microsoft’s Bing AI part,
NVIDIA’s (Fin)Megatron, BLOOM,
LaMDA, BERT, VALL-E, Point-E, etc.). -0.65
-0.6
-0.55
-0.5
-0.45
-0.4
-0.35
-0.3
-0.25
-0.2
-0.15
-0.1
1 10 100
Evaluation

MODA at First EuroCC HPC Vega Supercomputer
Listing 2: Runnig at Vega MODA.
1 ===================================================================== GMAX=200 =====
2 [ ales . zamuda@vglogin0002 ˜]$ srun −
−cpu−bind=cores −
−nodes=1 −
−ntasks−per−node=101
3 −
−cpus−per−task=2 −
−
mem=180G s i n g u l a r i t y run qmake . s i f bash
4 srun : job 4531374 queued and waiting for resources
5 srun : job 4531374 has been allocated resources
6 [ ”$SLURM PROCID” = 0 ] . / runme . sh
7 real 5m22.475 s
8 user 484m42.262 s
9 sys 1m38.304 s
10 ===================================================================== NODES=51 =====
−nodes=1 −
12 −
−
16 real 13m57.851 s
17 user 431m25.833 s
18 sys 0m29.272 s
19 ===================================================================== GMAX=400 =====
−nodes=1 −
21 −
−
25 real 6m14.687 s
26 user 590m45.641 s
27 sys 1m40.930 s

More Output: SLURM accounting
Listing 3: Example accounting tool at Vega: sacct.
[ ales . zamuda@vglogin0002 ˜]$ sacct
4531374. ext+ extern vega−users 202 COMPLETED 0:0
[ ales . zamuda@vglogin0002 ˜]$ sacct −j 4531374 −j 4531746 −j 4532697
−o MaxRSS , MaxVMSize , AvePages
MaxRSS MaxVMSize AvePages
−
−
−
−
−
−
−
−
−
− −
−
−
−
−
−
−
−
−
− −
−
−
−
−
−
−
−
−
−
0 217052K 0
26403828K 1264384K 22
0 217052K 0
13325268K 1264380K 0
0 217052K 0
26404356K 1264384K 30
Future MODA testings:
• testing the web interface for job analysis (as available from HPC RIVR);
• proﬁling MPI inter-node communication;
• use proﬁlers and monitoring tools available
— in the context of heterogeneous setups, like e.g.
• TAU Performance System — http://www.cs.uoregon.edu/research/tau/home.php,
• LIKWID Performance Tools — https://hpc.fau.de/research/tools/likwid/.

Deploying DAPHNE on Vega
Main documentation ﬁle:
Deploy.md

SLURM

More HPC User Perspective Nation-wide in Slovenia
More: at University of Maribor, Bologna study courses for
teaching (training) of Computer Science at cycles — click URL:
• level 1 (BSc)
• year 1: Programming I – e.g. C++ syntax
• year 2: Computer Architectures – e.g. assembly, microcode, ILP
• year 3: Parallell and Distributed Computing – e.g. OpenMP, MPI, CUDA
• level 2 (MSc)
• year 1: Cloud Computing Deployment and Management – e.g. arc, slurm,
Hadoop, containers (docker, singularity) through
virtualization
• level 3 (PhD)
• EU and other national projects research:
HPC RIVR, EuroCC, DAPHNE, ... – e.g. scaling new systems
of CI Operational Research of ... over HPC
• IEEE Computational Intelligence Task Force on Benchmarking
• Scientiﬁc Journals (e.g. SWEVO, TEVC, JoCS, ASOC, INS)
These contribute towards Sustainable Development of HPC.

Benchmarking
in DAPHNE
—
Part A.I: HPC and AI Generative
Models
—

Benchmarking
in DAPHNE
—
Part I: Generative AI
—

Generative AI — Modalities Access (HPC, H100)
• Generative AI (GenAI) is being
used for modalities such as
• text generation using
Transformers (like
ChatGPT),
• image generation using
Stable Diffusion (like
Midjouney and DALL-E),
• and video speech
generation (like Synthesia)
• GenAI provided recent interesting applications served by
HPC deployments (supported by e.g. NVIDIA H100).
• Therefore, two of my models for Generative AI,
• from Summarizer and TPP-PSADE@NPdynϵjDE,
• extended to support HPC deployment using MPI,
• are described in following some results are presented.

Generative AI — Some Background
• Early Learning to Learn, Google DeepMind after AlphaZero, deep RL algorithms
• https://gecco-2019.sigevo.org/index.html/Keynotes [@aleszamuda/status/1150672932588462081: ”Learning to learn ...”]
• Recent: with Reinforcement Learning (RL) trained Large Language Models (LLMs)
using Deep Neural Networks (DNNs) — Transformers (replacing RNN LSTMs; by Google —
2017, Attention Is All You Need: https://arxiv.org/abs/1706.03762, Submitted on 12 Jun 2017 (v1) — for NIPS’17 in December
(Jakob proposed replacing RNNs with self-attention and startedthe effort to evaluate this idea))
• A deployed LLM (Free Research
Preview of ChatGPT May 24
Version, 2023.) GPT-4 Technical Report:
https://arxiv.org/pdf/2303.08774.pdf
• Sample LLM code (Transformers by Hugging
Face), using Python3, AutoTokenizer, and
google/flan-t5-base
Transformers
architecture
Wikipedia (CC BY-SA
3.0), File:The-
Transformer-model-
architecture.png
• My GenAI backgrounds come from (evolutionary) generation of 3D scenery sequences (animation, AL — Artiﬁcial Life)
• In my 2020 journal article published with University of Alicante (w/ Elena Lloret), we
demonstrated HPC importance in NLP performance impact (Summarizer — developed on SLING)
• cites e.g. Salesforce Research’s NN paper on A Deep Reinforced Model for Abstractive Summarization, Submitted on 11 May
2017 (v1), https://arxiv.org/abs/1705.04304

Benchmarking
in DAPHNE
—
Part A.II: Language (1)
—

HPC Application 1:
Text Summarization
• NLP and computational linguistics for Text Summarization:
• Multi-Document Text Summarization is a hard CI challenge.
• Basically, an evolutionary algorithm is applied for
summarization,
• it is a state-of-the-art topic of text summarization for NLP (part of
”Big Data”) and presented as a collaboration [JoCS2020],
acknowledging several efforts.
• we add: self-adaptation of optimization control parameters;
analysis through benchmarking using HPC, and
apply additional NLP tools.
• How it works: for the abstract, sentences from original text are
selected for full inclusion (extraction).
• To extract a combination of sentences:
• can be computationally demanding,
• we use heuristic optimization,
• the time to run optimization can be limited.

1 – Preprocessing (environment sensing, knowledge
representation) (1/2)
1) The ﬁles of documents are each taken through the following
process using NLP (Natural Language Processing) tools:
INPUT
CORPUS
NATURAL
LANGUAGE
PROCESSING
ANALYSIS
CONCEPTS
DISTRIBUTION
PER
SENTENCES
MATRIX OF
CONCEPTS
PROCESSING
CORPUS
PREPROCESSING
PHASE
OPTIMIZATION
TASKS
EXECUTION
PHASE
ASSEMBLE
TASK
DESCRIPTION
SUBMIT
TASKS
TO PARALLEL
EXECUTION
OPTIMIZER
+
TASK DATA
ROUGE
EVALUATION
2) For each document is D, sentences are indexed using NLP
tools.
• Terms across sentences are determined using a semantic
analysis using both:
• coreference resolution (using WordNet) and
• a Concept Matrix (from Freeling).

1 – Preprocessing (environment sensing, knowledge
representation) (2/2)
• 3) For each i-th term (wi), during indexing
• number of occurences in the text is gathered, and
• number of occurences (nk) of a term in some k-th
statement,
• 4) For each term wi in the document, inverse frequency:
isfw i = log(
n
nk
),
• where n denotes number of statements in the document,
and
• nk number of statements including a term wi.
• 5) To conclude preprocessing, for each term in the
document, a weight is calculated:
wi,k = tfi,kisfk,
where tfik is number of occurences (term frequency) of a
term wk in a statement si.

2 – Summary Optimization (1/3)
• Sentence combination (X) is optimized using jDE
algorithm:
• as a 0/1 knapsack problem, we want to include optimal
selection of statements in the final output
• an i-th sentence si is selected (xi = 1) or unselected
(xi = 0).
• a) Price of a knapsack (its fitness) should be maximized,
• the fitness represents a ratio between content coverage,
V(X), and redundancy, R(X):
f(X) =
V(X)
R(X)
,
• considering a constraint: the summary length is L ± ϵ
words.
• Constraint handling with solutions:
• each feasable solution is better than unfeasable,
• unfeasable compared by constraint value (lower better),
• feasable compared by fitness (higher better).

• b) Content coverage V(X) is computed as a double sum of
similarities (deﬁned at d)):
V(X) =
n−1
X
i=1
n
X
j=i+1
(sim(si, O) + sim(sj, O))xi,j,
• where xi,j denotes inclusion of both statement, si, and sj,
• xi,j is only 1 if xi = xj = 1, otherwise 0,
• and O is a vector of average term weights wi,k:
O = (o1, o2, ..., om) for all i = {1..m} different text terms:
oi =
Pn
j=1 wi,j
n
.
• c) Redundance R(X) is also measured as double similarity
(deﬁned at d)) sum for all statements:
R(X) =
n−1
X
i=1
n
X
j=i+1
sim(si, sj)xi,j,
• where xi,j denotes inclusion of both statement, si, and sj,
• again, xi,j is only 1 if xi = xj = 1, otherwise 0.

• d) Similarity between statements si = [wi,1, wi,2, ..., wi,m]
and sj = [wj,1, wj,2, ..., wj,m] is computed:
sim(si, sj) =
m
X
k=1
wi,kwj,k
qPm
k=1 wi,kwi,k
Pm
k=1 wj,kwj,k
,
where wi,k is term weight (deﬁned in 5)) and m number of
all terms in text.
• e) When concluded:
• the selected statements from the best assessed
combination are printed,
• in order as they appear in the text, and
• the summary is stored.

Summary Optimization — Algorithm Pseudocode
The detailed new method called
CaBiSDETS is developed in the
HPC approach comprising of:
• a version of evolutionary
algorithm (Differential
Evolution, DE),
• self-adaptation, binarization,
constraint adjusting, and
some more pre-computation,
• optimizing the inputs to
deﬁne the summarization
optimization model.
Aleš Zamuda, Elena Lloret. Optimizing
Data-Driven Models for Summarization
as Parallel Tasks. Journal of
Computational Science, 2020, vol. 42, pp.
101101. DOI 10.1016/j.jocs.2020.101101.

Running the Tasks on HPC: ARC Job Preparation
Submission, Results Retrieval Merging [JoCS2020]
Through an HPC approach and by parallelization of tasks,
a data-driven summarization model optimization yields
improved benchmark metric results (drawn using gnuplot merge).

Results Published in Journal of Computational Science
The most interesting finding of the HPC study though is that
• the fitness of the NLP model keeps increasing with
prolonging the dedicated HPC resources (see below) and
that
• the fitness improvement correlates with ROUGE
evaluation in the benchmark, i.e. better summaries.
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
1 10 100 1000 10000
ROUGE-1R
ROUGE-2R
ROUGE-LR
ROUGE-SU4R
Fitness (scaled)
Hence, the use of HPC significantly contributes to
capability of this NLP challenge.

Summarization Implementation — Parallel
• In text summarization,
once the text model has been built,
• different lengths of summaries can be created with
Summarizer using CaBiSDETS algorithm in parallel.
• Generates large summaries
(more output tokens than single prebuilt LLMs)
accepts long task inputs (no pre-clustering).
• Results are explainable, tracable (no hallucinated
content/citations), and automatic (no manual RL scoring).
• But moreover from HPC Big-Data perspective,
• the input text can be preprocessed in parallel
• by computing the cosine sentence similarity pairs
in parallel using MPI in an integrated pipeline,
• before the generation of summaries commences and
• just before a complete parameterization of the summary
generation process.
• During summary optimization,
the ﬁtness function evaluations can be run in parallel.

Summarization Task Conﬁguration Execution
• An example task for Summarizer computational architecture
• based on a recent lecturing material,
PDF −→ text (language), on:
• Optimization Algorithms and Autonomous Systems
• Las Palmas de Gran Canaria, March 2023.
• https://www.slideshare.net/AlesZamuda/ulpgc2023erasmuslecturesaleszamuda
systemstheoryintelligentautonomoussystemseswamllsgohpcdeugpppdf
• for Summarizer runs, the parameters were:
--GMAX 1000
--NP 319
--summarylength 500
--epsilonLengthSummary 20
• For the job execution, the conﬁguration for SLURM was:
srun --ntasks-per-node=16
--mpi=pmix
./summarizer.sif
./summarizer

Summarization Job Speedup Results
• Speedup comparison: SLURM --nodes parameter = 1, 2, 3, 4, or 5
• obtained timings on this scaling as seen on the graphs below.
150
200
250
300
350
400
450
500
550
16 32 48 64 80
Seconds
to
compute
a
workload
Summarizer workload
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
16 32 48 64 80
Seconds
to
compute
a
workload
Summarizer workload
1
2
3
4
5
16 32 48 64 80
Workload
scaling
(wall
time)
Summarizer workload
Resources

Benchmarking
in DAPHNE
—
Part A.III: Video (2)
—

Introduction and Main Goals
• Plant animation in emergent ecosystems
• Plant morphology reconstruction
• from real photography (through evolutionary optimization)

Ecosystem Animation and Simulation:
Thousands of Trees
1: algorithm ecosystem simulation
Require: v - plant species list;
r - plant list for each species;
f - living condition factors on terrain;
Ensure: ecosystem afforestation simulation
2: loop
3: add new plants to species(v, r);
4: grow all plants(r, f);
5: remove dead plants(r);
6: end loop

Ecosystem Afforestation: Terrain Models
• Tree models are put to terrain based on ecosystem
growth
pi,k =

xi yi,k zk
T
, i, k ∈ [0, 99].
• Power: ﬁtness (height, slope, moisture, sunniness, windiness), age,
growth.
as,p = vs,phs,par;p, ar;p =
tf;s
max
s
{tf;s}
, vs,p =
ky;s,p + ms,p + ls,p + ws,p + ss,p
5
.

Another Ecosystem Scenario: Interactive Breeding
Motivation: interactive ecosystems breeding (von Mammen, 2009)
Vir: von Mammen (2009)
The approach – 6 modeled operators in EcoMod.
Operators of selection, crossover, and mutation.
Operators over terrain and environmental conditions.

Spatial Morphological Tree Model Reconstruction
• New approach for construction of trees
• three-dimensional spatial models,
• in computer graphics and animation,
• the user had to sketch basic branches.
• Our tree reconstruction includes:
• evolutionary algorithms and
• procedural modelling of trees.
Source:
→ CEC 2009
• An L-systems approach used
procedural models in a 2D plane, we
extend it:
• on 3D procedural models and
• more complex trees.
• Our approach combines open-sources:
• ecosystem framework EcoMod and
• algorithm MOjDE (DEMOwSA + jDE).

Woody Plants Procedural Model
• 3D tree models are compactly represented using a procedure
• our EcoMod framework uses a numerically coded procedural
model with ﬁxed dimensionality
• suitable for parameter estimation using DE/MOjDE.
• Parameterized procedural model builds a 3D structure of a tree
and all its building parts:
• by recursively executing a ﬁxed procedure,
• over a set of numerically coded input
parameters,
• e.g. branch thickness, relative length, and
branching.

Trees Representable by EcoMod Framework
• Foliage or coniferous trees with very different branching
structures,
• each branch and each leaf can be animated in real time to show
the growth of a tree or its sway in the wind.

1: procedure branchsegment(g, w, S0, L0, l0, M0, M−1
m;0)
Require: g, w - Gravelius and Weibull index of base branch; S0 - number of strands in base branch; L0, l0 - base
branch relative and actual length; M0 - base branch coordinate system; M−1
m;0
- inverse matrix of rotations
for gravimorphism in coordinate system for base branch; global (i.e. part of breeder) kd, kc, ltype, k
g,w
s ,
Mg,w
, mg,w
, k
g,w
l
, α
g,w
m , αg,w
, t, kf, ws, wg
Ensure: rendered tree image
2: d := kd
p
S0; {thickness calculation from Mandelbrot}
3: render base branch(M0, l0, d);
4: if S0 = 1 then
5: render leaves(ltype); return;
6: end if
7: S1 :=
l
1 + k
g,w
s (S0 − 2)
m
, S2 = S0 − S1; {strands}
8: r1 := max

min
r
S1
S0
, Mg,w

, mg,w

; {branch length}
9: r2 := max

min
r
S2
S0
, Mg,w

, mg,w

;
10: L1 := r1L0, L2 := r2L0; {relative lengths of subbranches}
11: l1 := k
g,w
l
L1, l2 := k
g,w
l
L2; {active subbranch lengths}
12: α1 := kc
r
S2
S0
αg,w
, α2 := αg,w
− α1; {branching angles}
13: M1 := Rz(α1)Ry(αp)Ry×ym (α
g,w
m )Ty(l0)M0; {transform}
14: M2 := Rz(α2)Ry(αp)Ry×ym (α
g,w
m )Ty(l0)M0;
15: M−1
m;1
:= Ry×ym (−α
g,w
m )Ry(−αp)Rx(−αx(t))Rz(−α1 − αz(t))M−1
m;0
; {refreshing inverse matrix}
16: M−1
m;2
:= Ry×ym (−α
g,w
m )Ry(−αp)Rx(−αx(t))Rz(−α2 − αz(t))M−1
m;0
;
17: branchsegment(g + 1, w + 1, S2, L2, l2, M2, M−1
m;2
); {minor branch development}
18: branchsegment(g, w + 1, S1, L1, l1, M1, M−1
m;1
); {major branch development}
19: return; {from recursive procedure call}

Ecosystem Afforestation Geometry Video

Image-based Approaches to Automatic Tree Modeling
• Image-based approaches have the best potential to
produce realistically looking plants
• they rely on images of real plants.
• Little work has been done to design trees with the use of a
general reconstruction from images without user
interaction
• use of sketch based guide techniques or
• the procedural models reconstructed were
two-dimensional.
• We now extended this recognition to the domain of 3D
procedural models
• suitable to model woody plants without user interaction.

Tree Model Reconstruction Innovization Using
Multi-objective Differential Evolution
• Based on an optimization procedure with three main
parts:
• Part I: genotype encoding,
• Part II: genotype-phenotype mapping, and
• Part III: ﬁtness evaluation:
• phenotype and reference image comparison.

Basic Genotype Encoding
• An individual genotype vector x of a DE population
represents a set of procedural model parameters,
• by computing recursive procedure using a set of
parameters, EcoMod renders a tree (woody plant),
• dimensionality of the genotype x is D = 4509,
• where g ∈ {0, G = 15}, w ∈ {0, W = 50}, and
• each local G × W = 750 real-coded parameter encodes:
one matrix of a Gravelius and Weibull ordered parameter
for recursive calculations, and
• all xi,j ∈ [0, 1], i ∈ {1, 2, ..., NP} and j ∈ {1, 2, ..., D} are
linearly normalized by scaling in the [0,1] interval.

Bounds and Scaling of Genotype-encoded Parameters
xi,j
Parameter Formula Interval
Number of strands in a tree (tree com-
plexity)
S = 400xi,0 + 10 S ∈ [10, 410]
Height of base trunk l
0,0
0
= 10xi,1 l
0,0
0
∈ [0 m, 10 m]
Coefﬁcient of branch thickness kd = 0.05xi,2 kd ∈ [0, 0.05]
Phyllotaxis angle αp = 360xi,3 αp ∈ [0◦
, 360◦
]
Branching ratio of subbranch strands
distribution
k
g,w
s = 0.5xi,j + 0.5, ∀j ∈ [4, 753] k
g,w
s ∈ [ 1
2
, 1]
Branching angle between dividing sub-
branches
αg,w
= 180xi,j ∀j ∈ [754, 1503] αg,w
∈ 0◦
, 180◦
Maximum relative sub-branch to base
branch length
Mg,w
= 20xi,j ∀j ∈ [1504, 2253] Mg,w
∈ [0, 20]
Minimum relative sub-branch to base
branch length
mg,w
= 20xi,j ∀j ∈ [2254, 3003] mg,w
∈ [0, 20]
Branch length scaling factor k
g,w
l
= 20xi,j, ∀j ∈ [3004, 3753] k
g,w
l
∈ [0, 20]
Gravicentralism impact kc = xi,3754 kc ∈ [0, 1]
Gravimorphism impact (i.e. gravitational
bending of branches)
α
g,w
m = 360xi,j − 180, ∀j ∈ [3755, 4504] α
g,w
m ∈ [−180◦
, 180◦
]
Enabling leaves display on a tree Bl = xi,4505 0.5?0 : 1 Bl ∈ {0, 1}
Size of leaves ll = 0.3xi,4506 ll ∈ [0, 0.3]
Density of leaves ρl = 30xi,4507 ρl ∈ [0, 30]
Leaf distribution type ltype = 5xi,4508 Spiral, Stacked, Stagg-
ered, Bunched, or Conif-
erous

Advanced Genotype Encoding:
Auxiliary Local Parameters get Vectorized

Genotype-phenotype Mapping
• Reconstruction method is based on reconstruction of
two-dimensional images of woody plants z∗ (photo),
• to compare the three-dimensional tree evolved with the
use of genotype x to the reference image z∗, genotype x
must be transformed to its phenotype ﬁrst,
• phenotype is a rendered two-dimensional image z,
• images z∗
and z are all of dimensionality X × Y pixels,
• the reference image is scaled to the given resolution,
if necessary.
• both images are converted to black and white, where
white (0) pixels mark background and black (1) pixels mark
material, e.g. wood,
• An evolved procedural model is rendered for comparison
twice
• to favor three-dimensional procedural models generation,
• projections differ by β = 90◦
camera view angle along the
trunk base (i.e. z axis for OpenGL).

Phenotype and Reference Image Comparison
• The recognition success is measured by similarity of
• the reference original images (2D) and
• the rendered image (2D) projections of evolved
parametrized procedural models.
• Images are compared pixel-wise by e.g. two criteria:
1 in the evolved image, for each pixel rendered as material
(1):
• the Manhattan distance to the nearest material pixel in the
reference image is computed
• and vice-versa, for each material (1) pixel of an evolved
model image,
2 count of differing pixels (0/1) among comparing images.

1: procedure MO reconstruction(z∗)
Require: S0 - maximum number of strands in base branch; also, other default parameters for MOjDE and
EcoMod
Ensure: Pareto set of reconstructed parameterized procedural 3D woody plant models
2: uniform randomly generate DE initial population xi,0 ∈ [0, 1] for i = 1..NP;
3: for DE generation loop g (while FEs 10000) do
4: for DE iteration loop i (for all individuals xi,g of a population) do
5: DE individual xi,g creation (adaptation, mutation, crossover):
6: Fi,G+1 =
(
Fl + rand1 × Fu if rand2 τ1,
Fi,G otherwise
; CRi,G+1 =
(
rand3 if rand4 τ2,
CRi,G otherwise
;
8: vi,G+1 = xr1,G + Fi,G+1(xr2,G − xr3,G);
9: ui,j,G+1 =
(
vi,j,G+1 if rand(0, 1) ≤ CRi,G+1 or j = jrand
xi,j,G otherwise
;
10: DE ﬁtness evaluation (genotype-phenotype mapping, rendering, and comparison):
11: z1 = g(ui,g, β1), z2 = g(ui,g, β2) {Execute Algorithm branchsegment twice}
12: h1(z1) =
P
x,y m1(z1
x,y, z∗
x,y) +
P
x,y m1(z∗
x,y, z1
x,y); {First difference metric, at 0◦
}
13: h1(z2) =
P
x,y m1(z2
x,y, z∗
x,y) +
P
x,y m1(z∗
x,y, z2
x,y); {First difference metric, at 90◦
}
14: f1(x) = f(g(x, β1), g(x, β2)) = h1(z1) + h1(z2); {Fitness evaluation, 1st criterion}
15: h2(z1) =
P
x,y w(z1
x,y, z∗
x,y) +
P
x,y w(z∗
x,y, z1
x,y); {Second difference metric, 0◦
}
16: h2(z2) =
P
x,y w(z2
x,y, z∗
x,y) +
P
x,y w(z∗
x,y, z2
x,y); {Second difference metric, 90◦
}
17: f2(x) = f(g(x, β1), g(x, β2)) = h2(z1) + h2(z2); {Fitness evaluation, 2nd criterion}
18: f(x) = {f1(x), f2(x)}; {Fitness evaluation, all criteria combined done}
19: DE selection:
20: xi,G+1 =
(
ui,G+1 if f(ui,G+1) ⪯ f(xi,G)
xi,G otherwise
; {Multi-objective comparison operator}
21: if not (ui,G+1 ⪯ xi,G or xi,G ⪯ ui,G+1 ) then add ui,G+1 to population archive;
22: end for
23: Truncate DE population archive to a size of NP using SPEA2 mechanism.
24: end for
25: return the best individuals obtained;

Advanced Approach (INS2014): Overview
→

More: See Published Articles
INS2014 A. Zamuda and J. Brest. Vectorized Procedural Models for Animated
Trees Reconstruction using Differential Evolution. Information
Sciences 2014, vol. 278, pp. 1-21. DOI: 10.1016/j.ins.2014.04.037.
IF2014 = 4.038 (Q1).
INS2013 A. Zamuda and J. Brest. Environmental Framework to Visualize
Emergent Artiﬁcial Forest Ecosystems. Information Sciences
220:522–540. 2013. DOI: 10.1016/j.ins.2012.07.031. IF2013 = 3.893
(Q1).
ASC2011 A. Zamuda, J. Brest, B. Bošković and V. Žumer. Differential Evolution
for Parameterized Procedural Woody Plant Models
Reconstruction. Applied Soft Computing 11(8):4904–4912. 2011.
DOI: 10.1016/j.asoc.2011.06.009. IF2011 = 2.612 (Q1).
CEC2012 A. Zamuda, J. Brest. Tree Model Reconstruction Innovization Using
Multi-objective Differential Evolution. 2012 IEEE World Congress on
Computational Intelligence (IEEE WCCI 2012), Brisbane, Australia,
2012, pp. 575-582.

Tree Reconstruction: Summary Conclusions
• An approach to design woody plant geometrical models
[ASOC2011],
• rendered images are compared to the reference source
images, for reconstruction, to guide the optimization
process,
• sampled randomly to reconstruct geometrical models,
• procedural models are rendered using EcoMod
framework [INS2013],
• renderings of sample evolved models,
• parameters of the procedural model are iteratively
evolved using multi-objective differential evolution MOjDE
algorithm [CEC2012]
• ﬁtness is evaluated by two criteria, which are not
pre-weighted,
• multi-objective optimization obtains multiple criteria
trade-offs shown using:
• attainment surfaces (trade-offs distribution), and
• rendered ﬁnal approximation set models.
• The advanced approach [INS2014] adds
• real images preprocessing (tree trunk and crown
extraction),
• post-effects on reconstructed geometry (growth, wind,
leafs).

Benchmarking
in DAPHNE
—
Part A.IV: Machine (3)
—

HPC Application 2:
Underwater Glider: Autonomous, Unmanned, Robotic
• underwater glider – navigating sea oceans,
• Autonomous Underwater Vehicle (AUV)
̸=
Unmanned Aerial Vehicle (UAV)
• AUV Slocum model (expertise in domain of ULPGC, work
with J. D. Hernández Sosa)
Images:
”Photo: Richard Watt/MOD” (License: OGL v1.0)
Slocum-Glider-Auvpicture 5.jpg (License: Public Domain)
MiniU.jpg (License: CC-BY-SA 3.0)

The Buoyancy Drive and Submarine Probes
Usefulness
• Driving ”yoyo” uses little energy, most only on descent
and rise (pump); also for maintaining direction little
power is consumed.
+ Use: improving ocean models with real data,
+ the real data at the point of capture,
+ sampling ﬂow of oil discharges,
+ monitoring cable lines, and
+ real-time monitoring of different
sensor data.
1
http://spectrum.ieee.org/image/1523708

Preparations – Simulation Scenarios
https://www.google.si/maps/@28.059806,-15.998355,650054m/data=!3m1!1e3

Trajectory Optimization: P201,ESTOC2013 3
+ BigData, MyOcean IBI,
satelite link, GPS location
The real trajectory and collected data is available in a Google Earth KML ﬁle at the EGO network:
http://www.ego-network.org/dokuwiki/doku.php?glider=P201,ESTOC2013_3

Constrained Differential Evolution Optimization
for Underwater Glider Path Planning
in Sub-mesoscale Eddy Sampling
• Corridor-constrained optimization:
eddy border region sampling
• new challenge for UGPP DE
• Feasible path area is constrained
• trajectory in corridor around
the border of an ocean eddy
The objective of the glider here is to
sample the oceanographic variables
more efﬁciently,
while keeping a bounded trajectory

HoP — New Trajectories:
Success history applied to expert system for
underwater glider path planning using differential
evolution
• Improved underwater glider path
planning mission scenarios:
optimization with L-SHADE.
• Several conﬁgured algorithms
are also compared to,
analysed, and further
improved.
• Outranked all other previous
results from literature and
ranked ﬁrst in comparison.
• New algorithm yielded
practically stable and
competitive output
trajectories.

Ranking UGPP —
Benchmarking
Aggregation
• Statistically,
all results
from previous paper
were outperformed.
• Main reasons:
tuning (NP),
parameter control
(L-SHADE).

Benchmarking
in DAPHNE
—
Part III: Power (4)
—

HTS: Hydro Power Plants (HPPs) and Thermal Power
Plants (TPPs) Scheduling – Introduction

Hydro and Thermal Power Plants Systems Model:
Nomenclature

Deﬁnitions

Models
• The application
contributes to algorithms
development,
with the aims of:
• improving performance
of the electrical energy
production and
• emissions and carbon
footprint reduction
(thermal units),
• while simultaneously
satisfying a 24-hour
system demands in
scheduling energy
demand and all other
operational
requirements.

Nomenclature and Deﬁnitions
• Equality constraints for energy production (scheduling).
• Constraints handling during optimization: ϵ-comparison.
• Algorithm output: 24-hour settings plan for TPPs and
HPPs.

Additional DE Mechanisms and Parallelization of HTS
• Population size (NP) adjustment, multilevel parallelization,
• sub-population gathering and BmW offspring strategy:
• A. Glotić, A. Glotić, P. Kitak, J. Pihler, I. Tičar. Parallel self-adaptive differential evolution
algorithm for solving short-term hydro scheduling problem. IEEE Transactions on Power Systems
2014, 29 (5), pp. 2347–2358.

HTS Parallelization and Pre-processing (1/3)
• New approach in optimization for scheduling energy
production among units of HPPs (hydro) and TPPs
(thermo).
• The approach allows a faster computation than before:
• 1) The DE for HTS is parallelized.
• 2) The TPPs optimization part features a novel
architecture, including a pre-computed surrogate model,
• this model is same during optimization of the whole HTS
optimized model for hydro and thermo units
(pre-processing), and
• obtained parameter values (x) of the TPPs surrogate model
on practical accuracy are stored for re-use in the global HTS
optimization.

• Two algorithms are built for this approach:
• the ﬁrst algorithm (NPdynϵjDE) addresses a specialized
treatment of constraints handling and optimizes
scheduling for TPPs – thermo units parameters are
pre-computed up to practical accuracy,
• the second algorithm (PSADEs) utilizes the output of the
ﬁrst algorithm, in order to optimize combined
production of hydro units, where hydro units settings are
probed and thermo units settings are looked-up in the
surrogate model matrix output from the NPdynϵjDE;
• here, both algorithms use
practical accuracy of the parameters for time schedule
of the thermo units load.

• The results of testing this approach on established HTS
benchmarks from literature show:
a larger performance improvement on all scenarios
under all criteria, compared to the approaches known
before. (Still.)
• Literature article with detailed results coverage follows:
A. Glotić, A. Zamuda. Short-term combined economic and
emission hydrothermal optimization by surrogate
differential evolution. Applied Energy, 1 March 2015, vol.
141, pp. 42-56. DOI 10.1016/j.apenergy.2014.12.020.
IF=5.613

HTS Optimization: New DE Algorithms Architecture
Surrogate Matrix – Input and Output

Fitness Function and Practical Accuracy (discretized)

Fitness Function and Practical Accuracy: the Algorithm

Results and Comparisons (1/2)
ELS
@NPdynϵjDE
EES
@NPdynϵjDE
CEES
@NPdynϵjDE

Results and Comparisons (2/2)
• Results on different types of scheduling, compared to best
works from literature: the proposed approach
outperforms all by far, for all models: ELS, EES, and CEES,
• This approach exhibits as well the lowest time latency (due
to PSADEs parallelization and NPdynϵjDE pre-processing).

A. Glotić, A. Zamuda. Short-term combined economic and emission
hydrothermal optimization by surrogate differential evolution.
Applied Energy, 1 March 2015, vol. 141, pp. 42-56.
DOI 10.1016/j.apenergy.2014.12.020. IF=5.613

HTS — HPC-deployable Results
• When generating energy plans,
• like load balancing schedules for energy power plants,
• data analytics and machine learning algorithm
can be used (PSADE@NPdynϵjDE [1]).
• Here, a surrogated [1] part of computation for the whole
problem is split non-trivially [1]
• and offloaded to an offline pre-computation phase.
• In this initial phase, an HPC can be used to generate a
lookup matrix, included as a surrogate matrix
during the next, online optimization phase.
• Experiment: an example set of 86500 long runs
(each 1 million fitness evaluations)
• to generate a surrogate matrix
with 0.01 MW granularity step
• for power demands
from 110 MW to 975 MW and 0.01 MW apart
• from 106
simulations for each of
86500 power demand scenarios.
0 1 2 3 4 5 6 7 8 9
0
100
200
300
400
500
600
700
Individual
output
(power
[MW]
or
unit
total
cost
[$])
Cost, TC / 3
Powerplant P1 power
Powerplant P2 power
Powerplant P3 power
[1] A. Glotić, A. Zamuda. Short-term combined economic and emission hydrothermal optimization by surrogate
differential evolution. Applied Energy, 1 March 2015, vol. 141, pp. 42-56. DOI 10.1016/j.apenergy.2014.12.020.

Benchmarking
in DAPHNE
—
Part V: Collaboration
—

Opportunities
• Collaboration — Leading from the Front (Work)
• Summary from Part III - Backgrounds
• Generative AI using HPC
• in Language (Text Summarization), Video (3D Ecosystems),
Machines (UGPP), and Power (Energy Plants)
• Future Work - Collaboration?
• additional integrated pipelines deployments using
DAPHNE runtime would be interesting,
• e.g. using micro-benchmarks for
• multi-document text-summarization,
• 3D forest scenery,
• energy scheduling, and
• trajectory optimization.
• further use of e.g. LLMs, generative AI Sors in Leadership
(AL discovery digitalization?)

Biography and References: Organizations
• Associate Professor at University of Maribor, Slovenia
• Continuous research programme funded by Slovenian Research Agency,
P2-0041: Computer Systems, Methodologies, and Intelligent Services
• EU H2020 Research and Innovation project, holder for UM part: Integrated
Data Analysis Pipelines for Large-Scale Data Management, HPC, and
Machine Learning (DAPHNE), https://cordis.europa.eu/project/id/957407
• IEEE (Institute of Electrical and Electronics Engineers) SM
• IEEE Computational Intelligence Society (CIS), senior member
• IEEE CIS Task Force on Benchmarking, chair Website link
• IEEE CIS, Slovenia Section Chapter (CH08873), chair
• IEEE Slovenia Section, 2018–2021 vice chair, 2018-21
• IEEE Young Professionals Slovenia, 2016-19 chair
• ACM SIGEVO (Special Interest Group on Genetic and Evolutionary Computation); EurAI; SLAIS
• Associate Editor: Swarm and Evolutionary Computation (2016-’22), Human-centric Computing and
Information Sciences, Frontiers in robotics and AI (section Robot Learning and Evolution)
• Co-operation in Science and Techology (COST) Association Management Committee, member:
• CA COST Action CA22137: Randomised Optimisation Algorithms Research
Network (ROAR-NET), 2.10.2023
2.10.2023–1.10.1027
• CA COST Action CA15140: Improving Applicability of Nature-Inspired
Optimisation by Joining Theory and Practice (ImAppNIO), WG3 VC
• ICT COST Action IC1406 High-Performance Modelling and Simulation for
Big Data Applications (cHiPSet);
• More: SI-HPC vice-chair; HPC-RIVR user; EuroHPC Vega user; SLAIS Honorary Tribunal; SLING KO member;
2.10.2023

Biography and References: Top Publications
• Aleš Zamuda, Elena Lloret. Optimizing Data-Driven Models for Summarization as Parallel Tasks.
Journal of Computational Science, 2020, vol. 42, pp. 101101. DOI 10.1016/j.jocs.2020.101101.
• A. Zamuda, J. D. Hernández Sosa. Success history applied to expert system for underwater glider path
planning using differential evolution. Expert Systems with Applications, 2019, vol. 119, pp. 155-170. DOI
10.1016/j.eswa.2018.10.048
• C. Lucas, D. Hernández-Sosa, D. Greiner, A. Zamuda, R. Caldeira. An Approach to Multi-Objective Path
Planning Optimization for Underwater Gliders. Sensors, 2019, vol. 19, no. 24, pp. 5506. DOI
10.3390/s19245506.
• A. Viktorin, R. Senkerik, M. Pluhacek, T. Kadavy, A. Zamuda. Distance Based Parameter Adaptation for
Success-History based Differential Evolution. Swarm and Evolutionary Computation, 2019, vol. 50, pp.
100462. DOI 10.1016/j.swevo.2018.10.013.
• A. Zamuda, J. Brest. Self-adaptive control parameters’ randomization frequency and propagations
in differential evolution. Swarm and Evolutionary Computation, 2015, vol. 25C, pp. 72-99.
DOI 10.1016/j.swevo.2015.10.007.
• A. Zamuda, J. D. Hernández Sosa, L. Adler. Constrained Differential Evolution Optimization for
Underwater Glider Path Planning in Sub-mesoscale Eddy Sampling. Applied Soft Computing, 2016,
vol. 42, pp. 93-118. DOI 10.1016/j.asoc.2016.01.038.
• A. Zamuda, J. D. Hernández Sosa. Differential Evolution and Underwater Glider Path Planning
Applied to the Short-Term Opportunistic Sampling of Dynamic Mesoscale Ocean Structures.
Applied Soft Computing, vol. 24, November 2014, pp. 95-108. DOI 10.1016/j.asoc.2014.06.048.
• A. Zamuda, J. Brest. Vectorized Procedural Models for Animated Trees Reconstruction using
Differential Evolution. Information Sciences, vol. 278, pp. 1-21, 2014. DOI 10.1016/j.ins.2014.04.037.
• A. Zamuda, J. Brest. Environmental Framework to Visualize Emergent Artiﬁcial Forest Ecosystems.
Information Sciences, vol. 220, pp. 522-540, 2013. DOI 10.1016/j.ins.2012.07.031.
• A. Glotić, A. Zamuda. Short-term combined economic and emission hydrothermal optimization by
surrogate differential evolution. Applied Energy, 1 March 2015, vol. 141, pp. 42-56.
DOI 10.1016/j.apenergy.2014.12.020.
• H. Hamann, Y. Khaluf, J. Botev, M. Divband Soorati, E. Ferrante, O. Kosak, J.-M. Montanier, S. Mostaghim,
R. Redpath, J. Timmis, F. Veenstra, M. Wahby and A. Zamuda. Hybrid Societies: Challenges and
Perspectives in the Design of Collective Behavior in Self-organizing Systems. Frontiers in Robotics
and AI, 2016, vol. 3, no. 14. DOI 10.3389/frobt.2016.00014.
• J. Šilc, A. Zamuda. Special Issue on ”Bioinspired Optimization” (guest editors). Informatica - An
International Journal of Computing and Informatics, 2015, vol. 39, no. 2, pp. 1-122.

Biography and References: Bound Speciﬁc to HPC
PROJECTS:
• DAPHNE: Integrated Data Analysis Pipelines for Large-Scale Data Management, HPC, and Machine Learning
• ICT COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications
• SLING: Slovenian national supercomputing network
• SI-HPC: Slovenian corsortium for High-Performance Computing
• UM HPC-RIVR: Supercomputer at UM, https://www.hpc-rivr.si/
• SmartVillages: Smart digital transformation of villages in the Alpine Space
• Interreg Alpine Space, https://www.alpine-space.eu/projects/smartvillages/en/home
• Interactive multimedia digital signage (PKP, Adin DS)
EDITOR:
• Associate Editor in journals:
• Swarm and Evolutionary Computation (2016-2022),
• Human-centric Computing and Information Sciences (2020-2023),
• Frontiers in robotics and AI, section Robot Learning and Evolution (2021-2023),
• etc.
• Mathematics-MDPI, Special Issue Guest Editor: ”Innovations in High-Performance Computing”
• Mathematics-MDPI, Special Issue Guest Editor: ”Evolutionary Algorithms in Engineering Design
Optimization”
• Journal of advanced engineering and computation (member of editorial board since 2019). Viet Nam: Ton
Duc Thang University, 2017-. ISSN 2588-123X.
• Cloud Computing and Data Science (Associate Editor, since 2019). Universal Wiser Publisher Pte.Ltd.
• D. Gleich, P. Planinšič, A. Zamuda. 2018 25th International Conference on Systems, Signals and Image
Processing (IWSSIP). IEEE Xplore, Maribor, 20-22 June 2018.
• General Chair: 7-th Joint International Conferences on Swarm, Evolutionary and Memetic Computing
Conference (SEMCCO 2019) Fuzzy And Neural Computing Conference (FANCCO 2019), Maribor, Slovenia,
EU, 10-12 July 2019; co-editors: Aleš Zamuda, Swagatam Das, Ponnuthurai Nagaratnam Suganthan, Bijaya
Ketan Panigrahi.
• Organizers member: GECCO 2022, GECCO 2023

Biography and References: More Publications on HPC
• Aleš Zamuda, Elena Lloret. Optimizing Data-Driven Models for Summarization as Parallel Tasks.
Journal of Computational Science, 2020, vol. 42, pp. 101101. DOI 10.1016/j.jocs.2020.101101.
• Patrick Damme, Marius Birkenbach, Constantinos Bitsakos, Matthias Boehm, Philippe Bonnet, Florina
Ciorba, Mark Dokter, Pawel Dowgiallo, Ahmed Eleliemy, Christian Faerber, Georgios Goumas, Dirk Habich,
Niclas Hedam, Marlies Hofer, Wenjun Huang, Kevin Innerebner, Vasileios Karakostas, Roman Kern, Tomaž
Kosar, Alexander Krause, Daniel Krems, Andreas Laber, Wolfgang Lehner, Eric Mier, Marcus Paradies,
Bernhard Peischl, Gabrielle Poerwawinata, Stratos Psomadakis, Tilmann Rabl, Piotr Ratuszniak, Pedro
Silva, Nikolai Skuppin, Andreas Starzacher, Benjamin Steinwender, Ilin Tolovski, Pınar Tözün, Wojciech
Ulatowski, Yuanyuan Wang, Izajasz Wrosz, Aleš Zamuda, Ce Zhang, Xiao Xiang Zhu. DAPHNE: An Open
and Extensible System Infrastructure for Integrated Data Analysis Pipelines. 12th Conference on
Innovative Data Systems Research, CIDR 2022, Chaminade, CA, January 9-12, 2022.
• Aleš Zamuda, Vincenzo Crescimanna, Juan C. Burguillo, Joana Matos Dias, Katarzyna Wegrzyn-Wolska,
Imen Rached, Horacio González-Vélez, Roman Senkerik, Claudia Pop, Tudor Cioara, Ioan Salomie, Andrea
Bracciali. Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the
State-of-the-Art in the Cloud Era. Kołodziej J., González-Vélez H. (eds) High-Performance Modelling and
Simulation for Big Data Applications. Lecture Notes in Computer Science, vol 11400, 2019, pp. 325-349.
DOI 10.1007/978-3-030-16272-6 12.
• Nenad Karolija, Aleš Zamuda. On cloud-supported web-based integrated development environment
for programming dataﬂow architectures. MILUTINOVIĆ, Veljko (ur.), KOTLAR, Milos (ur.). Exploring the
DataFlow supercomputing paradigm: example algorithms for selected applications, (Computer
communications and networks (Internet), ISSN 2197-8433), 2019, pp. 41-51. DOI
10.1007/978-3-030-13803-5 2.
• Simone Spolaor, Marco Gribaudo, Mauro Iacono, Tomas Kadavy, Zuzana Komı́nková Oplatková, Giancarlo
Mauri, Sabri Pllana, Roman Senkerik, Natalija Stojanovic, Esko Turunen, Adam Viktorin, Salvatore Vitabile,
Aleš Zamuda, Marco S. Nobile. Towards Human Cell Simulation. Kołodziej J., González-Vélez H. (eds)
High-Performance Modelling and Simulation for Big Data Applications. Lecture Notes in Computer
Science, vol 11400, 2019, pp. 221-249. DOI 10.1007/978-3-030-16272-6 8.
• A. Zamuda, J. D. Hernandez Sosa, L. Adler. Improving Constrained Glider Trajectories for Ocean Eddy
Border Sampling within Extended Mission Planning Time. IEEE Congress on Evolutionary Computation
(CEC) 2016, 2016, pp. 1727-1734.
• A. Zamuda. Function evaluations upto 1e+12 and large population sizes assessed in distance-based
success history differential evolution for 100-digit challenge and numerical optimization scenarios
(DISHchain 1e+12): a competition entry for ”100-digit challenge, and four other numerical optimization
competitions” at the genetic and evolutionary computation conference (GECCO) 2019. Proceedings of the
Genetic and Evolutionary Computation Conference Companion (GECCO 2019), 2019, pp. 11-12.
• ... several more experiments for papers run using HPCs.
• ... also, pedagogic materials in Slovenian and English — see Conclusion .

Promo materials: Calls for Papers, Websites
CS FERI WWW
CIS TFoB
CFPs WWW LI Twitter

Takeaways
Takeaways: Generative AI scaling on HPC, Generative AI,
modalities (language, video, machines, power, sors)
Thanks!
Acknowledgement: this work is supported by DAPHNE, funded by the European
Union’s Horizon 2020 research and innovation programme under grant agreement
No 957407.
150
200
250
300
350
400
450
500
550
16 32 48 64 80
Seconds
to
compute
a
workload
Summarizer workload
0 1 2 3 4 5 6 7 8 9
0
100
200
300
400
500
600
700
Individual
output
(power
[MW]
or
unit
total
cost
[$])
Cost, TC / 3
Powerplant P1 power
Powerplant P2 power
Powerplant P3 power
Real examples: science and HPC

Benchmarking
in DAPHNE
—
Final Slide: Questions, Misc
—

Deployment of Randomised Optimisation Algorithms Benchmarking in DAPHNE

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