Deploying DAPHNE Computational Intelligence on EuroHPC Vega for Benchmarking Randomised Optimisation Algorithms

Introduction ROA DAPHNE Results Conclusion
2024 International Conference on Broadband Communications for Next
Generation Networks and Multimedia Applications (CoBCom)
9–11 July 2024, Graz, Austria
Broadband Communications for Next Generation Networks and Multimedia Applications, in the Special Session context of an
International CEEPUS and ERASMUS Workshop on Microwave Technologies, Radars, Remote Sensing, and Communications
Graz University of Technology, Faculty of Electrical and Information Engineering, Institute of Microwave and Photonic Engineering
Room HF01092 (Seminarraum), Inffeldgasse 12, 1st ﬂoor (HF01) 14:30–15:30 (CEEPUS 2) Tuesday, 9 July 2024
Deploying DAPHNE Computational Intelligence on
EuroHPC Vega for Benchmarking
Randomised Optimisation Algorithms
Aleš Zamuda Mark Dokter
University of Maribor KNOW-CENTER GmbH
<ales.zamuda@um.si>
Acknowledgement. This work is supported by project DAPHNE (Integrated Data Analysis Pipelines for Large-Scale Data Management, HPC and Machine Learning) funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 957407 and ARIS (Slovenian Research And
Innovation Agency) programme P2-0041 (Computer Systems, Methodologies, and Intelligent Services). AZ gratefully acknowledges the HPC RIVR consortium (www.hpc-rivr.si) and EuroHPC JU (eurohpc-ju.europa.eu) for funding this research by providing computing resources of the HPC system Vega at the Institute of
Information Science (www.izum.si). Part of expenses to present the contribution is supported through IEEE, speciﬁcally IEEE GRSS Inter Society Networking (ISN) Activities grant involving IEEE Slovenia GRSS (Geoscience and Remote Sensing Society) and IEEE Slovenia CIS (Computational Intelligence Society) chapters.
Aleš Zamuda, Mark Dokter 7@aleszamuda Deploying DAPHNE CI on EuroHPC Vega for Benchmarking ROAs @ CoBCom 2024 (TU Graz, Austria) 1/29

—
Introduction
& Outline
—

Introduction & Outline: Content
1 (2 minutes) Part I: Background — Randomised
Optimisation Algorithms (ROA)
2 (3 minutes) Part II: DAPHNE
3 (5 minutes) Part III: CoBCom: ROA in DAPHNE on Vega
4 (2 minutes) Part IV: Conclusion with Takeaways
5 (3+ minutes) Questions, Misc
6 (Appendix) Business, Marketing

Introduction: Aims of this Talk — HPC & Benchmarking
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.
Warmup Highlights on (Generative) AI w/ ChatGPT+Synthesia: visiting Canaries/ASHPC/WCCI
Photo/video: 1) generative animation 2) HPC generated introduction (ASHPC23); 3) with underwater glider at
ULPGC SIANI; 4) infront SIANI; 5) with autonomous sailboat at SIANI;
If 2023 was about Generative AI, is 2024 on CI omnia Robotics?
• Therefore, it is useful to consider speeding up of benchmarking through
vectorization of the tasks that a benchmark is comprised of — 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.
• The speeding up focus will be on optimization algorithms within
such ML pipelines.
[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).

—
Background — Randomised Optimisation
Algorithms (ROA)
—

Differential Evolution (DE) and Implementations in DAPHNE
• 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 DE/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
,
Previously, at ASHPC24 (in June):
• ROA in DAPHNE Benchmarked (on
Apple M1/M3, not yet Vega/Slurm)
• Testing: convergence of a ML system;
ROA: Randomised Optimisation
Algorithm
• As seen from the plots, the ﬁtness
values are convergent, optimizing.
-350
-300
-250
-200
-150
-100
-50
0 50 100 150 200 250 300
Fitness, run 1
Fitness, run 2
Fitness, run 3
Fitness, run 4
Fitness, run 5
Fitness, run 6
Fitness, run 7
Fitness, run 8
Fitness, run 9
A convergence plot for
the function f2, on
different independent
runs.
-50
-40
-30
-20
-10
0
10
0 50 100 150 200 250 300
Fitness, run 1
Fitness, run 2
Fitness, run 3
Fitness, run 4
Fitness, run 5
Fitness, run 6
Fitness, run 7
Fitness, run 8
Fitness, run 9
An example ROA run, with convergence
plot for the HappyCat function (f6),
on different independent runs.
f2 =
P
x5 + 1 + max(x, 0), 0

f6 =
P
x2, 0

− 10
2
0.125
+
P
x2, 0

/2 +
P
(x)

/10 + 0.5

—
DAPHNE
—

DAPHNE Partners: Project Consortium
Project Consortium
13 partner institutions
from 7 countries
• DM, ML, HPC
• Academia industry
• Different application
domains
14
• Technical University Berlin
University of Maribor (UM): UM FERI research team DAPHNE (lead: A. Zamuda), SLING connection (EuroHPC Vega).
https://feri.um.si/en/research/international-and-structural-funds-projects/
integrated-data-analysis-pipelines-for-large-scale-data-management-hpc-and-machine-learning/

DAPHNE: Overview (Generic Aspect of the Project)
Overview: Generic Aspect of the Project
• Deployment Challenges
• Hardware Challenges
• DM+ML+HPC share compilation
and runtime techniques /
converging cluster hardware
• End of Dennard scaling:
P = α CFV2 (power density 1)
• End of Moore’s law
• Amdahl’s law: sp = 1/s
 Increasing Specialization
#1 Data
Representations
Sparsity Exploitation
from Algorithms to HW
dense
graph
sparse
compressed
#2 Data
Placement
Local vs distributed
CPUs/
NUMA
GPUs
FPGAs/
ASICs
#3 Data
(Value) Types
FP32, FP64, INT8,
INT32, INT64, UINT8,
BF16, TF32, FlexPoint
[NVIDIA
A100]
 DAPHNE Overall Objective:
Open and extensible system infrastructure
Different
Systems/
Libraries
Dev Teams Programming Models
Resource
Managers
Cluster
Under-
utilization
Data/File
Exchange
3 lessons learnt so far
choices made, methodology

DAPHNE: Functionalities (from Language Abstractions to
Distributed Vectorized Execution and Use Cases)
y
Functionality Introduction: from Language Abstractions to
Distributed Vectorized Execution and Use Cases
• Federated matrices/frames + distribution primitives
• Hierarchical vectorized pipelines and scheduling
• Coordinator
(spawns distributed fused pipeline)
• #1 Prepare Inputs
(N/A, repartition, broadcasts,
slices broadcasts as necessary)
• #2 Coarse-grained Tasks
(tasks run vectorized pipeline)
• #3 Combine Outputs
(N/A, all-reduce, rbind/cbind)
Node 1
X
[1:
100M]
Node 2
X
[100M:
200M]
colmu
colsd
y
y
(X)
XTX
XTy
dc = DaphneContext()
G = dc.from_numpy(npG)
G = (G != 0)
c = components(G, 100, True).compute()
Python API DaphneLib
def components(G, maxi, verbose) {
n = nrow(G); // get the number of vertexes
maxi = 100;
c = seq(1, n); // init vertex IDs
diff = inf; // init diff to +Infinity
iter = 1;
// iterative computation of connected components
while(diff0 iter=maxi) {
u = max(rowMaxs(G * t(c)), c); // neighbor prop
diff = sum(u != c); // # of changed vertexes
c = u; // update assignment
iter = iter + 1;
}
}
Domain-specific Language DaphneDSL
Multiple dispatch of functions/kernels
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

About HPC: Vega Supercomputer (TOP500) other EuroHPCs
Demo at Euro-PAR 20231, also on EuroHPC Vega2 OpenStack; ASHPCs 2021-233
ASHPC23: EuroHPC Vega tour
Ales Zamuda
@a�eszamuda
While visiting today I had the honor visiting the spectacular
MareNostrum supercomputers and their installation. From observing
afar in ‘06 as v1v2 were deployed w/ 4294 Tflops and v3 passing the
Pflop, this tour ‘23 to v4 and v5 was sourcely. Thanks
#sors
�BSC_CNS
@rosabadia
Ales Zamuda ·
@a�eszamuda Sep 12
Show this thread
Today I am delighted to present a Severo Ochoa Research Seminar (SORS) at
Barcelona Supercomputing Center #BSC, titled: EuroHPC AI in DAPHNE
(host: Rosa Badia @rosabadia, Workflows and Distributed Computing Group
Manager, CS, BSC) bsc.es/research-and-d… #presenting @daphne_eu
4:20 PM · Sep 12, 2023 · Views
165
View post engagements
5
Post your reply Reply
Post
Ales Zamuda on X: While visiting @BSC_CNS today I had the hono... https://twitter.com/aleszamuda/status/1701601792952512938
1 od 2 19. 09. 23, 11:14
• Towards HPC ROA in DAPHNE: June 2023, ASHPC23: 2 extended abstract
submitted (ROA are sought in DAPHNE; ROA role in GenAI),
• August 2023, Euro-Par 2023: initial demo created for ROA, conﬁrming that ROA
can run in DAPHNE (Apple M1)
• September 2023, seminars at Alicante (UA) and Barcelona (BSC): role of ROA in
NLP (incl. GenAI), ROA deployment preparations base start for Mare Nostrum 5
(not opened yet at that time)
• January 2024, HiPEAC 2024: presentation of the initial benchmarking of ROA in
DAPHNE (Apple M3)
• June 2024, ASHPC 2024: presentation of the further benchmarking of ROA in
DAPHNE (Apple M3)
• July 2024, CoBCom 2024: Vega ROA in DAPHNE
1
A. Vontzalidis et al. “DAPHNE Runtime: Harnessing Parallelism for Integrated Data Analysis Pipelines”. In: Euro-Par 2023: Parallel Processing
Workshops. Ed. by Demetris Zeinalipour et al. Lecture Notes in Computer Science, vol. 14352. Cham: Springer, 2024, pp. 242–246.
2
Aleš Zamuda and Mark Dokter. “Deploying DAPHNE Computational Intelligence on EuroHPC Vega for Benchmarking Randomised Optimisation
Algorithms”. In: International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (CoBCom).
2024, (to appear).
3
A. Zamuda. “Parallelization of benchmarking using HPC: text summarization in natural language processing (NLP), glider piloting in deep-sea
missions, and search algorithms in computational intelligence (CI)”. In: Austrian-Slovenian HPC Meeting 2021 - ASHPC21. 2021, p. 35; Aleš Zamuda.
“Generative AI Using HPC in Text Summarization and Energy Plants”. In: Austrian-Slovenian HPC Meeting 2023–ASHPC23. 2023, p. 5.

CoBCom 2024: Identified Opportunities
• Deploying Randomised Optimisation Algorithms
(ROA) in DAPHNE on EuroHPC Vega allows
• benchmarking and research in novel and
innovative models for Artificial Intelligece
(AI).
• The methods that are supported in DAPHNE
allow seamless distribution of AI memory
• that is required when an AI algorithm run requires a
large memory that can be distributed across different
HPC nodes.
• Using DAPHNE, the benchmarking can be not
only run in
• the Runtime Environment on an HPC that is much larger
than a regular laptop computer,
• but also gather monitoring data of the workload while
the algorithm is running,
• to obtain a benchmarking profile, allowing an
informed scientific observation of a novel
algorithm under test.
To discuss these results of the proposed approach from
a more distant context:
• we provide listing the main advantages (potentials for scaling)
and limitations (newly establishing language).
• Namely, the potential for scaling the ROA was successfully
benchmarked (scaling through tasks in Slurm) and as a limitation,
• we can mention that DAPHNE is a new language and the ROA
deployed still has limitations and does not include more
advanced fitness functions
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
-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)
Real examples: science and HPC
• improved scheduling of workload in distributed multi-node
Slurm tasks, and comparisons of benchmarking resultsa
, which
are among our ongoing research work.
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
a
Zamuda, “Parallelization of benchmarking using HPC: text
summarization in natural language processing (NLP), glider piloting in
deep-sea missions, and search algorithms in computational intelligence
(CI)”; Zamuda, “Generative AI Using HPC in Text Summarization and
Energy Plants”.

—
CoBCom: ROA in DAPHNE on Vega — Contribution,
Results Discussion
—

CoBCom 2024: Deployment Setup
Deployment command full instruction text:

CoBCom 2024: Fitness Function in LLVM
Example LLVM code for lowering of the fitness function implementation
(HappyCat function):
In line 1 the eval f6 function definition begins and in line 108 it ends, including
the constants definition (lines 2–7), allocations (e.g. in lines 8 and 14), and the
specific calls to the implemented kernels (e.g. for matrix operations like
addition/subtraction in lines 23/29 or multiplication/division in lines 17/83).

CoBCom 2024: Code Setup
Cloning the DAPHNE main repository,
from daphne-eu repository of daphne-eu at GitHub.
• The DAPHNE system is downloaded as source code,
cloning DAPHNE main repository:
In line 1, the git command is invoked, then the remote code is cloned into the
local ﬁle system in lines 2–9; and a change of directory ends it in line 11.

CoBCom 2024: Container Setup (Singularity)
A singularity image is compiled locally and transferred to the Vega so that it can
be used later for compilation of DAPHNE.
• Building the build environment image (Singularity container):
In line 1, the singularity command is invoked, then the build proceeds at lines
2–22 and completes by creating the daphneeu.sif image until line 24.

CoBCom 2024: Container Transfer to Target Machine
When the container image is compiled, the image is copied to Vega, where the
two-factor authentication and checking of the user access certificate take place.
• Transferring the built Singularity container image:
In line 1, the image file is specified, while authentication takes place in lines 2–3,
then copying proceeds in line 4.

CoBCom 2024: Compilation on Target Machine Within Singularity
Container
With the singularity image and source code inplace, the DAPHNE system is then
compiled on the target system (Vega) from source code.
• Building the DAPHNE system on Vega:
In line 1, the compilation is started, then there are thousands of lines of output
(not printed here), and the build then ﬁnishes.

CoBCom 2024: ROA Deployment on Target Machine
After the images are prepared, a ROA is implemented in roa.d and run with different
conﬁgurations.
• Deployment of ROAs with DAPHNE using Slurm on Vega:
Sample conﬁgurations with for...do are seen in lines surrounding the srun command
in lines 6–10 and saving of outputs and timings at lines 11 and 12, respectively.
For each task, up to 1 GB memory and 10 minutes node use are requested to run the
workload using the container.

CoBCom 2024: Data Flowchart
• To further explain the deployment and benchmarking of ROA in DAPHNE for our
use case, we also provide a data flowchart
• it is seen in the Figure on the right and shows how the data flows in the ROA use case.
• The ROA@HappyCat center part in red color
• is completely addressed by the DAPHNE system, within the core of the use case.
• as the DE parameters D, G, NP, and function for fitness
evaluation (HappyCat), are provided
• The pipeline generates data analysis reports
• (e.g. in PDF format, in the bottom of the flowchart),
• after it builds: the Singularity image from Docker platform
and DAPHNE from GitHub source;
• and runs the ROA tasks over Slurm (flowchart top).
• Then contribute in generating the reports:
• The collection of logs and cleanup (after waiting of the
tasks completion)
• and lookup into the Slurm database to see resource use.

CoBCom 2024: Flowchart Details (Code Recap)
• First, the DAPHNE system is downloaded as source code, cloning DAPHNE
main repository as daphne-eu repository by daphne-eu at GitHub
• see Figure on page 16: in line 1, the git command is invoked, then the remote code is cloned into
the local file system in lines 2–9; and a change of directory ends it in line 11.
• Then, a singularity image is compiled locally and transferred to the Vega
so that it can be used later for compilation of DAPHNE
• see Figure on page 17: in line 1, the singularity command is invoked,
then the build proceeds at lines 2–22
and completes by creating the daphneeu.sif image until line 24.
• When the container image is compiled, the image is copied to Vega, where the
two-factor authentication and checking of the user access certificate take place
• see Figure on page 18: in line 1, the image file is specified,
while authentication takes place in lines 2–3, then copying proceeds in line 4.
• With the singularity image and source code inplace, the DAPHNE system is then compiled on the target
system (Vega)
• from source code, see Figure on page 19: in line 1, the compilation is started,
then there are thousands of lines of output (not printed here), and the build then finishes.
• After the images are prepared, a ROA is implemented in roa.d
and run with different configurations, as seen in Figure on page 20:
• sample configurations with for...do are seen in lines surrounding the srun command in lines
6–10 and saving of outputs and timings at lines 11 and 12, respectively.
• For each task, up to 1 GB memory and 10 minutes node use are requested to run the workload using the
container.

CoBCom 2024: Experiment Setup and Convergence Results
• The optimisation results from the
ROA runs (fitness convergence
through generations) as
explained in the above
deployment preparation,
• as a set of convergence graphs, in
configurations with dimensions
D ∈ {10, 100, 1000} and population
sizes NP ∈ {10, 100, 1000}.
• For each of the runs plotted, we
observe that the fitness function
optimised by the ROA is successfully
improving, hence, the ROA CI is
performing its main functionality
of optimisation.
• The respective timings of the real
time to allocate and execute
different job variations as reported
by time command.
Convergent optimisation runs (fitness on vertical axis) using DAPHNE ROA on
different independent seeds:

CoBCom 2024: Experimental Results — Timing
• We can observe that the configuration of
D = 1000 and NP = 1000 in
case (i) has the far highest time
requirements overall for these cases.
• When observing each of the subfigures
separately, we see some limited degree of
variation in job allocation and execution
waiting time from 2 to 22 seconds, but
these are much less than the case (i) that
always reported timings above 100 seconds
(with only run 5 and 7 above 200 seconds,
but still below maximum requested
allocation of 10 minutes).
• We also further inspected the Slurm
database to profile run 7 and see that
while it consumed 92.139 Wh in 9m 42s,
• the task has spent only 8 seconds waiting to
be allocated, on empty current user queue,
which further demonstrates fast Vega task
allocations, practical in this use case.
Times (in seconds) of running optimisation runs, for different configurations:
The respective timings of the real time to allocate and execute different job variations
as reported by time command.

CoBCom 2024: Experimental Results — Timing (Combined)
• Also, when running just a subset of the jobs
with much more similar timings (e.g. jobs
with D = 100, NP = 100) for much more
independent runs,
• the speed up is mostly capped by
the longest running job.
• While the responsiveness of the Slurm
scheduler varies slightly due to HPC
workload of all running jobs,
• the batching of the set of jobs however greatly
reduces the time required to execute a batch,
• compared to just sequentially running each job.
• Also, as allocation time is important for HPC
users so that they do not wait for their
results longer than running sequentially,
• the allocation times by far did not exceed the
combined time,
• i.e. the speed up was signiﬁcant also from the
user perspective.
Combined time (left bar in the plot) vs. batched time (right):
500
1000
1500
2000
2500
3000
Time
To compare timings, we observe the combined time of
processing all batched jobs,
• compared to running them with Slurm,
demonstrating the speedup of real time needed by
runnning the tasks in parallell, and hence, scaling.

—
IV: Conclusion
with Takeaways
—

CoBCom 2024: Conclusion Takeaways
• Presented a deployment of DAPHNE (Integrated Data Analysis Pipelines for
Large-Scale Data Management, HPC and Machine Learning)
on EuroHPC Vega, running ROA CI tasks with Slurm.
• An example ROA benchmarking scenario was benchmarked
using HappyCat function
• and comparing batching times was discussed.
• In further detail:
• the insight explanation to LLVM code generation
of the applied HappyCat function was presented,
• the deployment preparation was provided step by step and a ﬂowchart, and
• running of ROA was discussed
• from CI conﬁguration and convergence perspective,
• from HPC perspective (e.g. timing and other resource use),
• as well as in perspective from current main advantages and limitations for
prospective users and ongoing work.

Further Research — After CoBCom 2024
• Future work includes
• further deployment (e.g. to additional hardware),
• benchmarking, and
• extending the ROA scenario and library
• as well as research in other use cases,
• especially from CI and remote sensing, including
• underwater missions like ocean glider path planning
• and text summarization.
-50
-40
-30
-20
-10
0
10
0 50 100 150 200 250 300
Fitness, run 1
Fitness, run 2
Fitness, run 3
Fitness, run 4
Fitness, run 5
Fitness, run 6
Fitness, run 7
Fitness, run 8
Fitness, run 9
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
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
-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)
150
200
250
300
350
400
450
500
550
16 32 48 64 80
Seconds
to
compute
a
workload
Summarizer workload

References
Thank you. Questions?
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.
-50
-40
-30
-20
-10
0
10
0 50 100 150 200 250 300
Fitness, run 1
Fitness, run 2
Fitness, run 3
Fitness, run 4
Fitness, run 5
Fitness, run 6
Fitness, run 7
Fitness, run 8
Fitness, run 9
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
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
-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)
150
200
250
300
350
400
450
500
550
16 32 48 64 80
Seconds
to
compute
a
workload
Summarizer workload

References
—
Appendix: Additional Business, Marketing
—

References
Speaker’s 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 ISATC (Intelligent Systems Applications Technical Committee) — Vice Chair
Conferences; IEEE CIS ECTC (Evolutionary Computation TC)
• IEEE CIS Task Force on Benchmarking, chair Website link
• IEEE CIS, Slovenia Section Chapter (CH08873), founding 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), 2.10.2023–1.10.1027
• 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);

References
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.

References
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 consortium 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

References
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 .

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

Deploying DAPHNE Computational Intelligence on EuroHPC Vega for Benchmarking Randomised Optimisation Algorithms

More Related Content

Similar to Deploying DAPHNE Computational Intelligence on EuroHPC Vega for Benchmarking Randomised Optimisation Algorithms (20)

More from University of Maribor (18)

Recently uploaded (20)

Deploying DAPHNE Computational Intelligence on EuroHPC Vega for Benchmarking Randomised Optimisation Algorithms