In Slovene: predavanje na daljavo: SUPERRAČUNALNIŠTVO V MARIBORU (izr. prof. dr. Aleš Zamuda), torek, 21. decembra 2021 ob 20:00 na daljavo preko MS Teams.
The event report for IEEE CIS11:
https://events.vtools.ieee.org/m/295510
Stay up-to-date on the latest news, research, and resources. This month's edition covers 2024 predictions across the HPC and AI industry, NSF's National Artificial Intelligence Research Resource (NAIRR) pilot, the role of compilers in scientific computing, on-demand and upcoming webinars, and more!
"Introducing the HOBBIT platform into the Ontology Alignment Evaluation Campaign" was presented in Ontology Matching (OM) hosted by the 17th International Semantic Web Conference ISWC, 8th - 12th of October 2018, held in Monterey, California, USA
This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).
Current Trends and Challenges in Big Data BenchmarkingeXascale Infolab
Years ago, it was common to write a for-loop and call it benchmark. Nowadays, benchmarks are complex pieces of software and specifications. In this talk, the idea of benchmark engineering, trends in the area of benchmarking research and current efforts of the SPEC Research Group and the WBDB community focusing on Big Data will be discussed. The way in which benchmarks are used has changed. Traditionally, they were mostly used for generating throughput numbers. Today, benchmarks are, e.g., used as test frameworks to evaluate different aspects of systems such as scalability or performance. Since benchmarks provide standardized workloads and meaningful metrics, they are increasingly important for research.
The benchmark community is currently focusing on new trends such as cloud computing, big data, power-consumption and large scale, highly distributed systems. For several of these trends traditional benchmarking approaches fail: how can we benchmark a highly distributed system with thousands of nodes and data sources? What does a typical Big Data workload look like and how does it scale? How can we benchmark a real world setup in a realistic way on limited resources? What does performance mean in the context of Big Data? What is the right metric?
Speaker: Kai Sachs is a member of the Lifecycle & Cloud Management group at SAP AG. He received a joint Diploma degree in business administration and computer science as well as a PhD degree from Technische Universität Darmstadt. His PhD thesis was awarded with the SPEC Distinguished Dissertation Award 2011 for outstanding contributions in the area of performance evaluation and benchmarking. His research interests include software performance engineering, capacity planning, cloud computing and benchmarking. He is co-founder of ACM/SPEC International Conference on Performance Engineering (ICPE). He has served as member of several program and organization committees and as reviewer for many conferences and journals. Among others he was the PC Chair of the SPEC Benchmark Workshop 2010, Program Chair of the Workshop on Hot Topics on Cloud Services 2013 and the Industrial PC Chair of the ICPE 2011. Kai Sachs is currently serving on the editorial board of the CSI Transactions on ICT, as vice-chair of the SPEC Research Group, as PC Co-Chair of the ACM/SPEC ICPE 2015 and as Co-Chair of the Workshop on Big Data Benchmarking 2014.
ExtremeEarth: Hopsworks, a data-intensive AI platform for Deep Learning with ...Big Data Value Association
The main goal of the session is to showcase approaches that greatly simplify the work of a data analyst when performing data analytics, or when employing machine learning algorithms, over Big Data. The session will include presentations on
(a) How data analytics workflows can be easily and graphically composed, and then optimized for execution,
(b) How raw data with great variety can be easily queried using SQL interfaces, and
(c) How complex machine learning operations can be performed efficiently in distributed settings.
After these presentations, the speakers will participate in a discussion with the audience, in order to discuss further tools that could make the work of a data analyst more simple.
In Slovene: predavanje na daljavo: SUPERRAČUNALNIŠTVO V MARIBORU (izr. prof. dr. Aleš Zamuda), torek, 21. decembra 2021 ob 20:00 na daljavo preko MS Teams.
The event report for IEEE CIS11:
https://events.vtools.ieee.org/m/295510
Stay up-to-date on the latest news, research, and resources. This month's edition covers 2024 predictions across the HPC and AI industry, NSF's National Artificial Intelligence Research Resource (NAIRR) pilot, the role of compilers in scientific computing, on-demand and upcoming webinars, and more!
"Introducing the HOBBIT platform into the Ontology Alignment Evaluation Campaign" was presented in Ontology Matching (OM) hosted by the 17th International Semantic Web Conference ISWC, 8th - 12th of October 2018, held in Monterey, California, USA
This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).
Current Trends and Challenges in Big Data BenchmarkingeXascale Infolab
Years ago, it was common to write a for-loop and call it benchmark. Nowadays, benchmarks are complex pieces of software and specifications. In this talk, the idea of benchmark engineering, trends in the area of benchmarking research and current efforts of the SPEC Research Group and the WBDB community focusing on Big Data will be discussed. The way in which benchmarks are used has changed. Traditionally, they were mostly used for generating throughput numbers. Today, benchmarks are, e.g., used as test frameworks to evaluate different aspects of systems such as scalability or performance. Since benchmarks provide standardized workloads and meaningful metrics, they are increasingly important for research.
The benchmark community is currently focusing on new trends such as cloud computing, big data, power-consumption and large scale, highly distributed systems. For several of these trends traditional benchmarking approaches fail: how can we benchmark a highly distributed system with thousands of nodes and data sources? What does a typical Big Data workload look like and how does it scale? How can we benchmark a real world setup in a realistic way on limited resources? What does performance mean in the context of Big Data? What is the right metric?
Speaker: Kai Sachs is a member of the Lifecycle & Cloud Management group at SAP AG. He received a joint Diploma degree in business administration and computer science as well as a PhD degree from Technische Universität Darmstadt. His PhD thesis was awarded with the SPEC Distinguished Dissertation Award 2011 for outstanding contributions in the area of performance evaluation and benchmarking. His research interests include software performance engineering, capacity planning, cloud computing and benchmarking. He is co-founder of ACM/SPEC International Conference on Performance Engineering (ICPE). He has served as member of several program and organization committees and as reviewer for many conferences and journals. Among others he was the PC Chair of the SPEC Benchmark Workshop 2010, Program Chair of the Workshop on Hot Topics on Cloud Services 2013 and the Industrial PC Chair of the ICPE 2011. Kai Sachs is currently serving on the editorial board of the CSI Transactions on ICT, as vice-chair of the SPEC Research Group, as PC Co-Chair of the ACM/SPEC ICPE 2015 and as Co-Chair of the Workshop on Big Data Benchmarking 2014.
ExtremeEarth: Hopsworks, a data-intensive AI platform for Deep Learning with ...Big Data Value Association
The main goal of the session is to showcase approaches that greatly simplify the work of a data analyst when performing data analytics, or when employing machine learning algorithms, over Big Data. The session will include presentations on
(a) How data analytics workflows can be easily and graphically composed, and then optimized for execution,
(b) How raw data with great variety can be easily queried using SQL interfaces, and
(c) How complex machine learning operations can be performed efficiently in distributed settings.
After these presentations, the speakers will participate in a discussion with the audience, in order to discuss further tools that could make the work of a data analyst more simple.
HPC Deployment / Use Cases (EVEREST + DAPHNE: Workshop on Design and Programm...University of Maribor
Extended slides from the talk provided at:
High Performance Embedded Architectures and Compilers (HiPEAC) 2023
https://www.hipeac.net/2023/toulouse/
EVEREST + DAPHNE: Workshop on Design and Programming High-performance, distributed, reconfigurable and heterogeneous platforms for extreme-scale analytics
https://www.hipeac.net/2023/toulouse/#/program/sessions/8037/
Wednesday, January 18th 2023, 10:00 - 17:30
Argos (Level 1), Pierre Baudis Convention Centre, Toulouse, France
Tamir Huberman joined Yissum in 2004, he is VP Business
Development in the field of computer science and is further responsible for the technical infrastructure necessary to support Yissum’s business processes and application systems. In addition, he is also the IT Director at ITTN; the Israeli Technology Transfer Organization, InnerEye and BriefCam. Prior to joining Yissum, Mr. Huberman was the co-founder of Artigon, served as part of the R&D team at Orgenics and as the Head of IP and R&D at MedisEl.
He holds an MSc. in structural biology and a BSc. in biology from the Hebrew University, a diploma in computer & electronics and has continued his MBA studies at the Hebrew University. He is also a certified Trainer of NLP from ABNLP.
Vertex Centric Asynchronous Belief Propagation Algorithm for Large-Scale GraphsUniversidade de São Paulo
Inference problems on networks and their algorithms were always important subjects, but more so now with so much data available and so little time to make sense of it.
Common applications range from product recommendation to social networks and protein interaction.
One of the main inferences in this types of networks is the guilty-by-association method, where labeled nodes propagate their information throughout the network, towards unlabeled nodes.
While there is a widely used algorithm for this context, called Belief Propagation, it lacks the necessary convergence guarantees for loopy-networks.
More recently, a new alternative method was proposed, called LinBP and while it solved the convergence issue, the scalability for large graphs that do not fit memory remains a challenge.
Additionally, most works that try to use BP considering large scale graphs rely on specific infrastructure such as supercomputers and computational clusters.
Therefore we propose a new algorithm, that leverages state-of-the-art asynchronous vertex-centric parallel processing techniques in conjunction with the state-of-the-art BP alternative LinBP, to provide a scalable framework for large graph inference that runs on a single commodity machine.
Our results show that our algorithm is up to 200 times faster than LinBP's SQL implementation on tested networks, while achieving the same accuracy rate.
We also show that due to the asynchronous processing, our algorithm actually needs less iterations to converge when compared to LinBP when using the same parameters.
Finally, we believe that our methodology highlights the yet not fully explored parallelism available on commodity machines, leaning towards a more cost-efficient computational paradigm.
Crude-Oil Scheduling Technology: moving from simulation to optimizationBrenno Menezes
Scheduling technology either commercial or homegrown in today’s crude-oil refining industries relies on a complex simulation of scenarios where the user is solely responsible for making many different decisions manually in the search for feasible solutions over some limited time-horizon i.e., trial-and-error heuristics. As a normal outcome, schedulers abandon these solutions and then return to their simpler spreadsheet simulators due to: (i) time-consuming efforts to configure and manage numerous scheduling scenarios, and (ii) requirements of updating premises and situations that are constantly changing. Moving to solutions based in optimization rather than simulation, the lecture describes the future steps in the refactoring of the scheduling technology in PETROBRAS considering in separate the graphic user interface (GUI) and data communication developments (non-modeling related), and the modeling and process engineering related in an automated decision-making with built-in problem representation facilities and integrated data handling features among other techniques in a smart scheduling frontline.
Towards an e-infrastructure in agriculture?Blue BRIDGE
Donatella Castelli, CNR-ISTI & BlueBRIDGE Coordinator, gave an introductive talk in the "Towards an e-infrastructure in agriculture?" session at the Euragri workship in Inra, Paris discussing leading an e-infrastructure project in marine research e-Infrastructure and how it refers to a combination of digital technologies (hardware and software), resources (data, services, digital libraries), communications (protocols, access rights and networks), and the people and organisational structures needed to manage them.
Slides from the talk:
Aleš Zamuda. EuroHPC AI in DAPHNE. Severo Ochoa Research Seminars. 12/Sep/2023, 1-3-2 Room, BSC Main Building and Zoom. Barcelona Supercomputing Center, Barcelona, Spain.
Poročilo odbora CIS (CH08873) za leto 2023 na letni skupščini IEEE Slovenija ...University of Maribor
Slides for CIS chapter (CH08873) at Slovenia Section, reporting the activities done in year 2023. The report is for the event of the annual meeting of the IEEE Slovenia Section at Vransko, on 13.2.2024:
https://events.vtools.ieee.org/m/400903
HPC Deployment / Use Cases (EVEREST + DAPHNE: Workshop on Design and Programm...University of Maribor
Extended slides from the talk provided at:
High Performance Embedded Architectures and Compilers (HiPEAC) 2023
https://www.hipeac.net/2023/toulouse/
EVEREST + DAPHNE: Workshop on Design and Programming High-performance, distributed, reconfigurable and heterogeneous platforms for extreme-scale analytics
https://www.hipeac.net/2023/toulouse/#/program/sessions/8037/
Wednesday, January 18th 2023, 10:00 - 17:30
Argos (Level 1), Pierre Baudis Convention Centre, Toulouse, France
Tamir Huberman joined Yissum in 2004, he is VP Business
Development in the field of computer science and is further responsible for the technical infrastructure necessary to support Yissum’s business processes and application systems. In addition, he is also the IT Director at ITTN; the Israeli Technology Transfer Organization, InnerEye and BriefCam. Prior to joining Yissum, Mr. Huberman was the co-founder of Artigon, served as part of the R&D team at Orgenics and as the Head of IP and R&D at MedisEl.
He holds an MSc. in structural biology and a BSc. in biology from the Hebrew University, a diploma in computer & electronics and has continued his MBA studies at the Hebrew University. He is also a certified Trainer of NLP from ABNLP.
Vertex Centric Asynchronous Belief Propagation Algorithm for Large-Scale GraphsUniversidade de São Paulo
Inference problems on networks and their algorithms were always important subjects, but more so now with so much data available and so little time to make sense of it.
Common applications range from product recommendation to social networks and protein interaction.
One of the main inferences in this types of networks is the guilty-by-association method, where labeled nodes propagate their information throughout the network, towards unlabeled nodes.
While there is a widely used algorithm for this context, called Belief Propagation, it lacks the necessary convergence guarantees for loopy-networks.
More recently, a new alternative method was proposed, called LinBP and while it solved the convergence issue, the scalability for large graphs that do not fit memory remains a challenge.
Additionally, most works that try to use BP considering large scale graphs rely on specific infrastructure such as supercomputers and computational clusters.
Therefore we propose a new algorithm, that leverages state-of-the-art asynchronous vertex-centric parallel processing techniques in conjunction with the state-of-the-art BP alternative LinBP, to provide a scalable framework for large graph inference that runs on a single commodity machine.
Our results show that our algorithm is up to 200 times faster than LinBP's SQL implementation on tested networks, while achieving the same accuracy rate.
We also show that due to the asynchronous processing, our algorithm actually needs less iterations to converge when compared to LinBP when using the same parameters.
Finally, we believe that our methodology highlights the yet not fully explored parallelism available on commodity machines, leaning towards a more cost-efficient computational paradigm.
Crude-Oil Scheduling Technology: moving from simulation to optimizationBrenno Menezes
Scheduling technology either commercial or homegrown in today’s crude-oil refining industries relies on a complex simulation of scenarios where the user is solely responsible for making many different decisions manually in the search for feasible solutions over some limited time-horizon i.e., trial-and-error heuristics. As a normal outcome, schedulers abandon these solutions and then return to their simpler spreadsheet simulators due to: (i) time-consuming efforts to configure and manage numerous scheduling scenarios, and (ii) requirements of updating premises and situations that are constantly changing. Moving to solutions based in optimization rather than simulation, the lecture describes the future steps in the refactoring of the scheduling technology in PETROBRAS considering in separate the graphic user interface (GUI) and data communication developments (non-modeling related), and the modeling and process engineering related in an automated decision-making with built-in problem representation facilities and integrated data handling features among other techniques in a smart scheduling frontline.
Towards an e-infrastructure in agriculture?Blue BRIDGE
Donatella Castelli, CNR-ISTI & BlueBRIDGE Coordinator, gave an introductive talk in the "Towards an e-infrastructure in agriculture?" session at the Euragri workship in Inra, Paris discussing leading an e-infrastructure project in marine research e-Infrastructure and how it refers to a combination of digital technologies (hardware and software), resources (data, services, digital libraries), communications (protocols, access rights and networks), and the people and organisational structures needed to manage them.
Slides from the talk:
Aleš Zamuda. EuroHPC AI in DAPHNE. Severo Ochoa Research Seminars. 12/Sep/2023, 1-3-2 Room, BSC Main Building and Zoom. Barcelona Supercomputing Center, Barcelona, Spain.
Poročilo odbora CIS (CH08873) za leto 2023 na letni skupščini IEEE Slovenija ...University of Maribor
Slides for CIS chapter (CH08873) at Slovenia Section, reporting the activities done in year 2023. The report is for the event of the annual meeting of the IEEE Slovenia Section at Vransko, on 13.2.2024:
https://events.vtools.ieee.org/m/400903
Slides from the talk:
Aleš Zamuda. EuroHPC AI in DAPHNE and Text Summarization. Conferencia Invitada. 15/Sep/2023, Sala Ada Lovelace, Department of Software and Computing Systems, University of Alicante, Spain. https://www.dlsi.ua.es/eines/noticia.cgi?id=eng&idn=596
Load balancing energy power plants with high-performance data analytics (HPDA...University of Maribor
Slides from "Superpower for the power grid", 30 March 2023
https://vsc.ac.at//training/2023/superpower/
https://eurocc-austria.at/events/events-workshops/superpowergrid
ULPGC2023-Erasmus+lectures_Ales_Zamuda_SystemsTheory+IntelligentAutonomousSys...University of Maribor
Aleš Zamuda: ULPGC 2023 Erasmus+ Lecture Series from the Teaching Programme "Optimization Algorithms and Autonomous Systems".
ERASMUS+ STAFF MOBILITY FOR TEACHING (STA)
Staff mobility-Erasmus+ programme countries (KA103)
Faculty of Electrical Engineering and Computer Science (University of Maribor, SI MARIBOR01)
Escuela de Ingenierı́a Informática (Universiad de Las Palmas de Gran Canaria, E LAS-PAL01)
From 6/March/2023 to 24/March/2023 at Campus de Tafira, Las Palmas de Gran Canaria, Spain
Part I: Differential Evolution and Large-Scale Optimization Applications
Part II: HPC Integrated Data Analysis Pipelines for Underwater Glider Path Planning
Part III: Success history applied to expert system for underwater glider path planning
using differential evolution, with prospects for Machine Learning and Research
This is my presentation (in Slovene) about the IEEE CIS Slovenia report for 2022, presented at the IEEE Slovenia meeting at Vransko on February 17, 2023.
IEEE Slovenia: Introduction (in Slovene), with details in EnglishUniversity of Maribor
The slides are from my talks as IEEE Slovenia vice-chair from the event:
https://events.vtools.ieee.org/m/295510
More about IEEE Slovenia:
https://www.ieee.si/
Examples Implementing Black-Box Discrete Optimization Benchmarking Survey for...University of Maribor
PPSN XV: 15th International Conference on Parallel Problem Solving from Nature
Coimbra, Portugal, September 8–12, 2018
Session: Black Box Discrete Optimization Benchmarking (BB-DOB)
Saturday, 8 September, 14:00-15:30, Room 2.4
Aleš Zamuda, Goran Hrovat, Elena Lloret, Miguel Nicolau, Christine Zarges
Adaptive Constraint Handling and Success History Differential Evolution for C...University of Maribor
Talk given in: 2017 IEEE Congress on Evolutionary Computation (CEC), taking place at Donostia - San Sebastian, Spain, June 5-8, 2017. Associated special session at CEC: Associated with Competition on Bound Constrained Single Objective Numerical Optimization III (June 6, 14:30-16:30, Room 4).
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Gliese 12 b: A Temperate Earth-sized Planet at 12 pc Ideal for Atmospheric Tr...Sérgio Sacani
Recent discoveries of Earth-sized planets transiting nearby M dwarfs have made it possible to characterize the
atmospheres of terrestrial planets via follow-up spectroscopic observations. However, the number of such planets
receiving low insolation is still small, limiting our ability to understand the diversity of the atmospheric
composition and climates of temperate terrestrial planets. We report the discovery of an Earth-sized planet
transiting the nearby (12 pc) inactive M3.0 dwarf Gliese 12 (TOI-6251) with an orbital period (Porb) of 12.76 days.
The planet, Gliese 12 b, was initially identified as a candidate with an ambiguous Porb from TESS data. We
confirmed the transit signal and Porb using ground-based photometry with MuSCAT2 and MuSCAT3, and
validated the planetary nature of the signal using high-resolution images from Gemini/NIRI and Keck/NIRC2 as
well as radial velocity (RV) measurements from the InfraRed Doppler instrument on the Subaru 8.2 m telescope
and from CARMENES on the CAHA 3.5 m telescope. X-ray observations with XMM-Newton showed the host
star is inactive, with an X-ray-to-bolometric luminosity ratio of log 5.7 L L X bol » - . Joint analysis of the light
curves and RV measurements revealed that Gliese 12 b has a radius of 0.96 ± 0.05 R⊕,a3σ mass upper limit of
3.9 M⊕, and an equilibrium temperature of 315 ± 6 K assuming zero albedo. The transmission spectroscopy metric
(TSM) value of Gliese 12 b is close to the TSM values of the TRAPPIST-1 planets, adding Gliese 12 b to the small
list of potentially terrestrial, temperate planets amenable to atmospheric characterization with JWST.
Nutrition is the science that deals with the study of nutrients and their role in maintaining human health and well-being. It encompasses the various processes involved in the intake, absorption, and utilization of essential nutrients, such as carbohydrates, proteins, fats, vitamins, minerals, and water, by the human body.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
We present you a part of our Tampere University's team - FHAIVE!
Besides producing excellent science, they are in charge or coordinating this project as well Tampere University, Faculty of Medicine and Health Technology.
Deployment of Randomised Optimisation Algorithms Benchmarking in DAPHNE
1. 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).
-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
-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
-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
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 1/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 1/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 1/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 1/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 1/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 1/141
2. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 2/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 2/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 2/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 2/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 2/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 2/141
3. 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
—
I. Background: Optimization,
100-Digit Challenge
—
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 3/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 3/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 3/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 3/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 3/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 3/141
4. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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)
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 4/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 4/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 4/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 4/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 4/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 4/141
5. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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.
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 5/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 5/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 5/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 5/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 5/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 5/141
6. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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.
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 6/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 6/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 6/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 6/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 6/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 6/141
7. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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.
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 7/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 7/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 7/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 7/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 7/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 7/141
8. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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.
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 8/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 8/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 8/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 8/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 8/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 8/141
9. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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
,
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 9/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 9/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 9/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 9/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 9/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 9/141
10. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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);
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 10/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 10/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 10/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 10/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 10/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 10/141
11. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 11/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 11/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 11/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 11/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 11/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 11/141
12. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 12/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 12/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 12/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 12/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 12/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 12/141
13. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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, ...
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 13/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 13/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 13/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 13/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 13/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 13/141
14. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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}.
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 14/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 14/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 14/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 14/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 14/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 14/141
15. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 15/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 15/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 15/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 15/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 15/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 15/141
16. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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.
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 16/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 16/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 16/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 16/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 16/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 16/141
17. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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).
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 17/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 17/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 17/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 17/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 17/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 17/141
18. 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
—
II. Method: DISHchain3e+12
Algorithm
—
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 18/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 18/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 18/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 18/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 18/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 18/141
19. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
DISH - a Population-based Optimizer at SWEVO (Q1)
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 19/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 19/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 19/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 19/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 19/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 19/141
20. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 20/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 20/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 20/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 20/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 20/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 20/141
21. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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.
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 21/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 21/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 21/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 21/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 21/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 21/141
22. 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
—
III. Results – Scores, Comparison,
Impact
—
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 22/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 22/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 22/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 22/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 22/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 22/141
23. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 23/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 23/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 23/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 23/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 23/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 23/141
24. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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)
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 24/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 24/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 24/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 24/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 24/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 24/141
25. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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).
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 25/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 25/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 25/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 25/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 25/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 25/141
26. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
Impact: Comparing Score to Other Entries – Rank 1
https://github.com/P-N-Suganthan/CEC2019/blob/master/100-DigitChallengeAnalysisofResults.pdf
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 26/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 26/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 26/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 26/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 26/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 26/141
27. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
Preliminary ROAR in DAPHNE Benchmarked
Testing: convergence of a ML system
ROAR: Randomised Optimisation Algorithm
-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
-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
0
1
2
3
4
5
6
7
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
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 27/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 27/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 27/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 27/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 27/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 27/141
28. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
Next Steps
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 28/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 28/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 28/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 28/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 28/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 28/141
29. 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
—
IV: Conclusion
with Takeaways
—
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 29/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 29/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 29/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 29/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 29/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 29/141
30. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
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
-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
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
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 30/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 30/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 30/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 30/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 30/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 30/141
31. 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
—
Final Slide: Questions, Misc
Acknowledgement: this work is supported by EU project no. 957407 (DAPHNE).
—
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 31/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 31/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 31/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 31/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 31/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 31/141
32. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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
—
APPENDIX with Backgrounds
Marketing Materials
—
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 32/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 32/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 32/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 32/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 32/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 32/141
33. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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).
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 33/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 33/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 33/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 33/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 33/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 33/141
34. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 34/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 34/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 34/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 34/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 34/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 34/141
35. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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
—
Appendix Part I: Backgrounds
—
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 35/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 35/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 35/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 35/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 35/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 35/141
36. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
Background A:
HPC Workloads and
Cloud Computing
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 36/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 36/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 36/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 36/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 36/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 36/141
37. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
Appendix
(Vega supercomputer in TOP500)
— A Multimedia Tour —
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 37/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 37/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 37/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 37/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 37/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 37/141
38. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
TOP500: EuroHPC Vega (tour at ASHPC23)
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 38/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 38/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 38/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 38/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 38/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 38/141
39. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
TOP500: EuroHPC Vega (tour at ASHPC23)
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 39/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 39/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 39/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 39/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 39/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 39/141
40. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
TOP500: EuroHPC Vega (tour at ASHPC23)
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 40/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 40/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 40/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 40/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 40/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 40/141
41. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
TOP500: EuroHPC Vega (tour at ASHPC23)
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 41/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 41/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 41/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 41/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 41/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 41/141
42. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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.
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 42/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 42/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 42/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 42/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 42/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 42/141
43. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 43/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 43/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 43/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 43/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 43/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 43/141
44. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 44/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 44/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 44/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 44/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 44/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 44/141
45. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 45/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 45/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 45/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 45/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 45/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 45/141
46. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 46/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 46/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 46/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 46/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 46/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 46/141
47. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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.
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 47/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 47/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 47/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 47/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 47/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 47/141
48. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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]
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 48/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 48/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 48/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 48/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 48/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 48/141
49. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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).
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 49/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 49/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 49/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 49/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 49/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 49/141
50. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 50/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 50/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 50/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 50/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 50/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 50/141
51. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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/
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 51/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 51/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 51/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 51/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 51/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 51/141
52. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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.
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 52/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 52/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 52/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 52/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 52/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 52/141
53. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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.
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 53/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 53/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 53/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 53/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 53/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 53/141
54. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
Running the Tasks on HPC: ARC Job Preparation
Parallel summarization tasks on grid through ARC.
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 54/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 54/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 54/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 54/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 54/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 54/141
55. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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).
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 55/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 55/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 55/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 55/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 55/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 55/141
56. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 56/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 56/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 56/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 56/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 56/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 56/141
57. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 57/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 57/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 57/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 57/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 57/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 57/141
58. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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 . $SLURM PROCID
Text summarization/generation systems
are getting more and more useful
and accessible on deployed systems
(e.g. OpenAI’s ChatGPT, Microsoft’s Bing AI part,
NVIDIA’s (Fin)Megatron, BLOOM,
LaMDA, BERT, VALL-E, Point-E, etc.). -0.65
-0.6
-0.55
-0.5
-0.45
-0.4
-0.35
-0.3
-0.25
-0.2
-0.15
-0.1
1 10 100
Evaluation
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 58/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 58/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 58/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 58/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 58/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 58/141
59. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
MODA at First EuroCC HPC Vega Supercomputer
Listing 2: Runnig at Vega MODA.
1 ===================================================================== GMAX=200 =====
2 [ ales . zamuda@vglogin0002 ˜]$ 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 [ ”$SLURM PROCID” = 0 ] . / runme . sh
7 real 5m22.475 s
8 user 484m42.262 s
9 sys 1m38.304 s
10 ===================================================================== NODES=51 =====
11 [ ales . zamuda@vglogin0002 ˜]$ srun −
−cpu−bind=cores −
−nodes=1 −
−ntasks−per−node=51
12 −
−cpus−per−task=2 −
−
mem=180G s i n g u l a r i t y run qmake . s i f bash
13 srun : job 4531746 queued and waiting for resources
14 srun : job 4531746 has been allocated resources
15 [ ”$SLURM PROCID” = 0 ] . / runme . sh
16 real 13m57.851 s
17 user 431m25.833 s
18 sys 0m29.272 s
19 ===================================================================== GMAX=400 =====
20 [ ales . zamuda@vglogin0002 ˜]$ srun −
−cpu−bind=cores −
−nodes=1 −
−ntasks−per−node=101
21 −
−cpus−per−task=2 −
−
mem=180G s i n g u l a r i t y run qmake . s i f bash
22 srun : job 4532697 queued and waiting for resources
23 srun : job 4532697 has been allocated resources
24 [ ”$SLURM PROCID” = 0 ] . / runme . sh
25 real 6m14.687 s
26 user 590m45.641 s
27 sys 1m40.930 s
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 59/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 59/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 59/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 59/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 59/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 59/141
60. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
More Output: SLURM accounting
Listing 3: Example accounting tool at Vega: sacct.
[ ales . zamuda@vglogin0002 ˜]$ sacct
4531374. ext+ extern vega−users 202 COMPLETED 0:0
4531746. ext+ extern vega−users 102 COMPLETED 0:0
4532697. 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/.
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 60/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 60/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 60/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 60/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 60/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 60/141
61. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
Deploying DAPHNE on Vega
Main documentation file:
Deploy.md
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 61/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 61/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 61/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 61/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 61/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 61/141
62. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
SLURM
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 62/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 62/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 62/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 62/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 62/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 62/141
63. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 63/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 63/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 63/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 63/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 63/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 63/141
64. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 64/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 64/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 64/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 64/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 64/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 64/141
65. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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.
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 65/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 65/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 65/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 65/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 65/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 65/141
66. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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
—
Part A.I: HPC and AI Generative
Models
—
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 66/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 66/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 66/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 66/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 66/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 66/141
67. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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
—
Part I: Generative AI
—
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 67/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 67/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 67/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 67/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 67/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 67/141
68. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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.
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 68/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 68/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 68/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 68/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 68/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 68/141
69. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 69/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 69/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 69/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 69/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 69/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 69/141
70. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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
—
Part A.II: Language (1)
—
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 70/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 70/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 70/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 70/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 70/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 70/141
71. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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.
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 71/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 71/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 71/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 71/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 71/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 71/141