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)

Abstract NLP UGPP CI References
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)
ASHPC21, Austrian-Slovenian High-Performance Computing Meeting
Institute of Information Science in Maribor, Slovenia
1 May – 2 June, 2021 (online exclusively via livestream)
Aleš Zamuda
ales.zamuda@um.si
Aleš Zamuda 7@aleszamuda Parallelization of Benchmarking using HPC: NLP, UGPP & CI 1/ 40

Introduction: Overview
This contribution focuses on
I recent developments and trends
I in terms of efficient and successful
I high-performance computing (HPC) applications.
Real examples: science and HPC

Introduction: Aims of this Talk at ASHPC21 Meeting
I First (5 minutes), a collaboration benefiting from HPC:
a state-of-the-art topic of text summarization for NLP
(part of ”Big Data”),
I spanning over the time curse of successful HPC initiatives
including Slovenia: SLING → SIHPC → ImAppNIO →
cHiPSet → HPC RIVR → TFoB → EuroCC → DAPHNE
I Second (+10 minutes), additional success stories are listed,
like those developed through program funded unit P2-0041,
HPC RIVR project, and projects by EU under programs like
Erasmus+, COST (Cooperation in Science and Technology)
and H2020 (Horizon 2020).
I Third (5 minutes), this contribution aims at leveraging
potential opportunities in NLP and HPC for shared and
collaborative synergies, enhancing sustainable HPC
development.

HPC Application 1:
Text Summarization
I NLP and computational linguistics for Text Summarization:
I Multi-Document Text Summarization is a hard CI challenge.
I Basically, an evolutionary algorithm is applied for
summarization,
I 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.
I we add: self-adaptation of optimization control parameters;
analysis through benchmarking using HPC, and
apply additional NLP tools.
I How it works: for the abstract, sentences from original text
are selected for full inclusion (extraction).
I To extract a combination of sentences:
I can be computationally demanding,
I we use heuristic optimization,
I 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.
I Terms across sentences are determined using a semantic analysis
using both:
I coreference resolution (using WordNet) and
I a Concept Matrix (from Freeling).

1 – Preprocessing (environment sensing, knowledge
representation) (2/2)
I 3) For each i-th term (wi ), during indexing
I number of occurences in the text is gathered, and
I number of occurences (nk ) of a term in some k-th statement,
I 4) For each term wi in the document, inverse frequency:
isfw i = log(
n
nk
),
I where n denotes number of statements in the document, and
I nk number of statements including a term wi .
I 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)
I Sentence combination (X) is optimized using jDE algorithm:
I as a 0/1 knapsack problem, we want to include optimal
selection of statements in the final output
I an i-th sentence si is selected (xi = 1) or unselected (xi = 0).
I a) Price of a knapsack (its fitness) should be maximized,
I the fitness represents a ratio between content coverage, V (X),
and redundancy, R(X):
f (X) =
V (X)
R(X)
,
I considering a constraint: the summary length is L ± words.
I Constraint handling with solutions:
I each feasable solution is better than unfeasable,
I unfeasable compared by constraint value (lower better),
I feasable compared by fitness (higher better).
Aleš Zamuda 7@aleszamuda Parallelization of Benchmarking using HPC: NLP, UGPP CI 7/ 40

I 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 ,
I where xi,j denotes inclusion of both statement, si , and sj ,
I xi,j is only 1 if xi = xj = 1, otherwise 0,
I 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
.
I 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 ,
I where xi,j denotes inclusion of both statement, si , and sj ,
I again, xi,j is only 1 if xi = xj = 1, otherwise 0.

I 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
pPm
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.
I e) When concluded:
I the selected statements from the best assessed combination
are printed,
I in order as they appear in the text, and
I the summary is stored.

Summary Optimization — Algorithm Pseudocode
The detailed new method called
CaBiSDETS is developed in the
HPC approach comprising of:
I a version of evolutionary
algorithm (Differential
Evolution, DE),
I self-adaptation, binarization,
constraint adjusting, and
some more pre-computation,
I 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.

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
I the fitness of the NLP model keeps increasing with prolonging
the dedicated HPC resources (see below) and that
I 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.

HPC Application 2:
Underwater Glider: Autonomous, Unmanned, Robotic
I underwater glider – navigating sea oceans,
I Autonomous Underwater Vehicle (AUV)
6=
Unmanned Aerial Vehicle (UAV)
I 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
I 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
I Corridor-constrained optimization:
eddy border region sampling
I new challenge for UGPP DE
I Feasible path area is constrained
I 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
I Improved underwater glider path
planning mission scenarios:
optimization with L-SHADE.
I Several configured algorithms
are also compared to, analysed,
and further improved.
I Outranked all other previous
results from literature and
ranked first in comparison.
I New algorithm yielded
practically stable and
competitive output trajectories.
I UGPP unconstrained scenarios —
contributed significantly to the capacity of
the decision-makers for mission plannings.

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

HPC Application 3:
Multi-application domain generalized
Computational Intelligence (CI) through HPC
I With the ubiquity of HPC and Cloud Computing,
I connectible using versatile interfaces for their utilisation,
I example successes of these systems is computational
intelligence.
I One of designs for these services is:
I using evolutionary optimization approaches,
I needing much efficiently parallelizable data processing power.
I There are several application domains of this approach:
I generalized numerical functions problems and
I other parallel real world problems, such as
I text processing,
I molecular modelling,
I evolutionary computer vision, and
I robotics.

CI Algorithm Design: Control Parameters Self-Adaptation
I Through more suitable values of control parameters the search
process exhibits a better convergence (CI — Computational
Intelligence),
I therefore the search converges faster to better solutions,
which survive with greater probability and they create more
offspring and propagate their control parameters
I 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
I Deployed using arcsub.
I Update: DISH algorithm —
https://doi.org/10.1016/j.swevo.2018.10.013
(Outperforms on CEC 2017 functions.)

Existing Challenges: Benchmarks for Known Environments
Inspired by previous computational optimization competitions in continuous settings
that used test functions for optimization application domains:
I single-objective: CEC 2005, 2013, 2014, 2015
I constrained: CEC 2006, CEC 2007, CEC 2010
I multi-modal: CEC 2010, SWEVO 2016
I black-box (target value): BBOB 2009, COCO 2016
I noisy optimization: BBOB 2009
I large-scale: CEC 2008, CEC 2010
I dynamic: CEC 2009, CEC 2014
I real-world: CEC 2011
I computationally expensive: CEC 2013, CEC 2015
I learning-based: CEC 2015
I 100-digit (50% targets): 2019 joined CEC, SEMCCO, GECCO
I multi-objective: CEC 2002, CEC 2007, CEC 2009, CEC 2014
I bi-objective: CEC 2008
I many objective: CEC 2018
Tuning/ranking/hyperheuristics use. → DEs as usual winner algorithms.

IEEE Congress on Evolutionary Computation (CEC)
Competitions (1/4)
I Storn, Rainer, and Kenneth V. Price. ”Minimizing the Real Functions of
the ICEC’96 Contest by Differential Evolution.”
International Conference on Evolutionary Computation. 1996.
I ...
I CEC 2005 Special Session / Competition on
Evolutionary Real Parameter single objective optimization
Evolutionary Constrained Real Parameter single objective optimization
Performance Assessment of real-parameter MOEAs
large scale single objective global optimization with bound constraints
I CEC 2008 Scale-Invariant Optimisation Competition ”Mountains or Molehills”
Dynamic Optimization (Primarily composition functions were used)
Performance Assessment of real-parameter MOEAs

Competitions (2/4)
large-scale single objective global optimization with bound constraints
Evolutionary Constrained Real Parameter single objective optimization
I CEC 2010 Special Session on
Niching Introduces novel scalable test problems
I CEC 2011 Competition on Testing Evolutionary Algorithms on Real-world
Numerical Optimization Problems
Real Parameter Single Objective Optimization
Real Parameter Single Objective Optimization
(incorporates expensive functions)
I CEC 2014: Dynamic MOEA Benchmark Problems

Competitions (3/4)
Real Parameter Single Objective Optimization (incorporates 3 scenarios)
I CEC 2015 Black Box Optimization Competition
I CEC 2015 Dynamic Multi-Objective Optimization
I CEC 2015 Optimization of Big Data
I CEC 2015 Large Scale Global Optimization
I CEC 2015 Bound Constrained Single-Objective Numerical Optimization
I CEC 2015
Optimisation of Problems with Multiple Interdependent Components
I CEC 2015 Niching Methods for Multimodal Optimization

Competitions (4/4)
Real Parameter Single Objective Optimization (incorporates 4 scenarios)
I CEC 2016 Big Optimization (BigOpt2016)
I CEC 2016 Niching Methods for Multimodal Optimization
I CEC 2016 Special Session Associated with Competition on
Bound Constrained Single Objective Numerical Optimization
I CEC2017 Special Session / Competition on Real Parameter Single
Objective Optimization (also constrained)
I CEC2018 Special Session / Competition on Real Parameter Single
Objective Optimization
I CEC2019 Special Session / Competition on 100-Digit Challenge on Single
Objective Numerical Optimization
I CEC 2020, CEC 2021, ...: see IEEE Task Force on Benchmarking listings
(incl. BOCIA, TEVC, MDPI SIs etc.)

Some More Benchmark Function Sets
The Genetic and Evolutionary Computation Conference (GECCO):
I GECCO 2014 Windfarm Layout Optimization Competition
I GECCO 2014 Permutation-based Combinatorial Optimization Problems
I GECCO 2015 Black Box Optimization (BBComp)
I GECCO 2015 Combinatorial Black-Box Optimization (CBBOC)
I GECCO 2019 Workshop Competition on Numerical Optimization
I Swarm and Evolutionary Computation: Novel benchmark functions for
continuous multimodal optimization with comparative results (2015)
I Benchmarks for natural architecture design:
Spatial Tree Morphology Reconstruction
(seeded/pre-processed/vectorized/multi-objective)
I We prepare benchmarks for ocean exploration with underwater robots:
Underwater Glider Path Planning
(unconstraint/constraint/variable/multi-objective)

Real World Industry Challenges: Motivation
I Optimization of Real World Industry Challenges (RWIC),
I selected as CEC 2011 Real World Optimization Problems,
I a benchmark set contains functions modelling the problems,
I assessment on all 22 functions of CEC 2011 set,
I functions with constraints are handled with additional care.

Selected Challenges in the CEC 2011 Benchmark (1/2)
I Decomposition of radio FM signals,
I determination of ternary protein structure
I Lennard-Jones inter-atom energy potential,
I parameterization of a chemical process,
I methylcyclopentane → benzene,
I control parameterization for chemical reaction in a continuous
stirred tank reactor,
I inter-atom potential in covalent Silicon systems
I Tersoff energy potential,
I radar spectrum signal broadcast parameterization,
I spread spectrum radar polly phase code design,
I electrical transmission network expansion planning,
I new lines for transmission selection.

Selected Challenges in the CEC 2011 Benchmark (2/2)
I Large scale transmission pricing,
I circular antenna array design,
I dynamic economic dispatch (with power generator control),
I static economic load dispatch (of power generated),
I hydrothermal scheduling (among hydro/thermal units),
I spacecraft trajectory optimization,
I Mercury (Messenger),
I Saturn (Cassini).
I The collection includes 22 functions, with constraints as:
1. non-feasible evaluation is NaN when constraints are not met, or
2. the constraints are included in the function evaluation value.

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

Biography and References: Organizations
I Associate Professorat University of Maribor, Slovenia
I Continuous research programme funded by Slovenian Research Agency,
P2-0041: Computer Systems, Methodologies, and Intelligent Services
I Associate Editor: Swarm and Evolutionary Computation (SWEVO)
I IEEE (Institute of Electrical and Electronics Engineers) senior
I IEEE Computational Intelligence Society (CIS), senior member
I IEEE CIS Task Force on Benchmarking, chair
I IEEE CIS, Slovenia Section Chapter (CH08873), chair
I IEEE Slovenia Section, vice chair
I IEEE Young Professionals Slovenia, past chair
I ACM SIGEVO (Special Interest Group on Genetic and Evolutionary Computation); EurAI; SLAIS
I Co-operation in Science and Techology (COST) Association
Management Committee, member:
I CA COST Action CA15140: Improving Applicability of Nature-Inspired
Optimisation by Joining Theory and Practice (ImAppNIO), WG3 VC
I ICT COST Action IC1406 High-Performance Modelling and Simulation
for Big Data Applications (cHiPSet); SI-HPC; HPC-RIVR user
I 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

Biography and References: Top Publications
I 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.
I 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
I 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.
I 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.
I 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.
I 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.
I 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.
I 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.
I 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.
I 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.
I 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:
I DAPHNE: Integrated Data Analysis Pipelines for Large-Scale Data
Management, HPC, and Machine Learning
I ICT COST Action IC1406 High-Performance Modelling and Simulation for Big
Data Applications
I SLING: Slovenian national supercomputing network
I SI-HPC: Slovenian corsortium for High-Performance Computing
I UM HPC-RIVR: Supercomputer at UM, https://www.hpc-rivr.si/
I SmartVillages: Smart digital transformation of villages in the Alpine Space
I Interreg Alpine Space,
https://www.alpine-space.eu/projects/smartvillages/en/home
I Interactive multimedia digital signage (PKP, Adin DS)
EDITOR:
I SWEVO (Top Journal), Associate Editor
I Mathematics-MDPI, Special Issue: Evolutionary Algorithms in Engineering Design Optimization
I Journal of advanced engineering and computation (member of editorial board since 2019). Viet Nam: Ton
Duc Thang University, 2017-. ISSN 2588-123X.
I Cloud Computing and Data Science (Associate Editor, since 2019). Universal Wiser Publisher Pte.Ltd.
I D. Gleich, P. Planinšič, A. Zamuda. 2018 25th International Conference on Systems, Signals and Image
Processing (IWSSIP). IEEE Xplore, Maribor, 20-22 June 2018.
I 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.

Biography and References: More on HPC
RESEARCH PUBLICATIONS:
I 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.
I 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. Kolodziej 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.
I 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.
I 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. Kolodziej 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.
I 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.
I 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.
I ... several more experiments for papers run using HPCs.
I ... also, pedagogic materials in Slovenian and English — see Conclusion .

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

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)

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