Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
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!
Do we measure functional size or do we count thomas fehlmannIWSM Mensura
This document contains the slides from a presentation given by Dr. Thomas Fehlmann on October 7, 2015 in Kraków, Poland. The presentation discusses using transfer functions and the solution profile to combine multiple functional size measurement methods into a single measurement in accordance with international standards for measurement. This allows defining a functional size unit specific to the transfer function and measurement goals. The solution profile shows the contribution of each size count to measurement precision and goals.
"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.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
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!
Do we measure functional size or do we count thomas fehlmannIWSM Mensura
This document contains the slides from a presentation given by Dr. Thomas Fehlmann on October 7, 2015 in Kraków, Poland. The presentation discusses using transfer functions and the solution profile to combine multiple functional size measurement methods into a single measurement in accordance with international standards for measurement. This allows defining a functional size unit specific to the transfer function and measurement goals. The solution profile shows the contribution of each size count to measurement precision and goals.
"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.
The document describes using Bayesian networks for modeling relationships between variables and performing inference. It discusses constructing and learning the structure and parameters of a Bayesian network using the bnlearn package in R. It also presents an example of performing inference on a large dataset with R and Hadoop by parallelizing the computations across multiple reducers.
This document discusses Bayesian networks and their use with R and Hadoop. It begins with an introduction to Bayesian networks, providing an example called the "Asia" network. It then discusses constructing and learning Bayesian networks with the bnlearn package in R, including learning the structure and conditional probability tables. The document covers using Bayesian networks for inference with R. It discusses performing approximate inference at scale with R and Hadoop using RMR to parallelize queries over MapReduce. Finally, it provides an example of learning a network for the Adult dataset and performing inference using RMR.
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
An empirical study of the evolution of Eclipse third-party plug-insAlexander Serebrenik
This document summarizes a study on the evolution of third-party plugins for the Eclipse framework. The study analyzed 21 plugins over multiple versions using metrics related to dependencies, size, and quality. Most of Lehman's laws of software evolution were assessed, including continuing change, self-regulation, continuing growth, and conservation of familiarity. While some plugins supported the laws, others showed exceptions, and the results were generally inconclusive. The study represents a first step toward analyzing framework-constrained evolution, but a broader analysis is needed to fully validate Lehman's laws in this context.
PRACE is an international not-for-profit association based in Brussels that provides top-tier supercomputing resources and services to researchers. It has 25 member countries and hosts 6 supercomputers located in France, Germany, Italy, and Spain. PRACE supports scientific computing projects through peer-reviewed allocations of supercomputing resources. It also provides training programs and initiatives like SHAPE to support industry adoption of HPC. Since 2010, PRACE has awarded over 10 thousand million core hours of supercomputing time to nearly 400 projects across a wide range of scientific disciplines.
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.
The document summarizes a project that published linked geospatial data from several pan-European datasets. The project transformed datasets into RDF, loaded them into Virtuoso, and linked the datasets. This resulted in a Virtuoso instance with over 700 million triples. The project also developed three interfaces for navigating and visualizing the data: a SPARQL endpoint, faceted search browser, and map visualization. The linked data can be reused for applications in areas like real estate, tourism, and agriculture.
Vertex Centric Asynchronous Belief Propagation Algorithm for Large-Scale GraphsUniversidade de São Paulo
This document presents a vertex-centric asynchronous belief propagation algorithm for large-scale graphs. It proposes running belief propagation in a single computational node by processing vertices in parallel through an asynchronous vertex-centric approach. The algorithm is shown to converge faster than previous approaches and can scale to larger graphs by utilizing multicore architectures. Experiments on graphs with millions of edges demonstrate that the algorithm achieves similar accuracy to previous methods but with significantly better runtime performance, making it suitable for inference on very large real-world graphs.
- Fraunhofer is a leading applied research organization in Germany that works closely with industry and academia. It has over 60 institutes and 18,000 employees conducting contract research.
- Fraunhofer has a representative office in Seoul to collaborate with Korean partners on issues like renewable energy, electronics, and new materials. The document discusses opportunities to learn from each other's innovation systems.
- Both Germany and Korea invest heavily in research and development, though each country also faces challenges like rigid labor markets or maintaining competitiveness in global markets. The Fraunhofer model of applied research and technology transfer provides lessons for national innovation.
This document describes ProtOLAP, a methodology for rapid OLAP prototyping when source data is not initially available. ProtOLAP enables designers to create a conceptual schema based on user requirements, which is then automatically translated into a logical schema and prototype. Users manually input sample data and validate the prototype using simple pivot tables. If validated, source data is collected, ETL is designed, and the prototype is finalized. ProtOLAP allows for agile design approaches even when source data availability is delayed, by facilitating early user validation through sample data exploration.
The Frankfurt Big Data Lab carries out research in big data and data analytics from the perspective of information systems and computer science. The lab is located in Frankfurt and targets research applications for both industry and academia. Current research areas include big data management technologies, data analytics, graph databases/linked open data, and using big data for social good. One project focuses on using data to help with the inclusion of refugees in Frankfurt.
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.
Capgemini and SAP trends in warehouse automationJoe Vernon
This document discusses trends in warehouse automation. It covers innovations like wearable devices, machine-to-machine communication, and intelligent agents. Examples of automation innovations are highlighted, like hands-free order picking using augmented reality and automated forklift and pallet tracking. The document also reviews SAP's warehouse management system roadmap and how technologies like the Internet of Things can optimize distribution center operations and analytics.
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.
TUW - Quality of data-aware data analytics workflowsHong-Linh Truong
The document discusses quality of data-aware data analytics workflows. It begins with outlining the topics to be covered, which include data analytics workflows structures and systems, issues with quality of data-aware workflows, and quality of data-aware simulation workflows. It then provides examples of different workflow systems and frameworks for data analytics workflows. Key points discussed are the need to understand hierarchical workflow structures, addressing data and service concerns, importance of quality of data for data analytics workflows, and approaches to modeling quality of data metrics and optimizing workflows based on quality of data.
This curriculum vitae outlines the qualifications and experience of Dr. Andrew Paul Nisbet. He is currently a Senior Lecturer in Computer Science at Manchester Metropolitan University, where he has worked since 2004. His research focuses on compiler optimization techniques for parallel, multicore, and embedded systems. He has supervised several PhD students to completion and currently supervises two PhD students.
The document discusses network analysis and provides an overview of the topic. It covers that networks are found everywhere, from social and biological systems to financial networks. It also discusses complex networks, influential nodes, communities, and software tools for network analysis. A variety of network analysis methods and algorithms are presented, including approaches for identifying communities, central nodes, and other network properties. Examples of real-world networks and potential projects for network analysis are also mentioned.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
Contenu connexe
Similaire à Deployment of Randomised Optimisation Algorithms Benchmarking in DAPHNE
The document describes using Bayesian networks for modeling relationships between variables and performing inference. It discusses constructing and learning the structure and parameters of a Bayesian network using the bnlearn package in R. It also presents an example of performing inference on a large dataset with R and Hadoop by parallelizing the computations across multiple reducers.
This document discusses Bayesian networks and their use with R and Hadoop. It begins with an introduction to Bayesian networks, providing an example called the "Asia" network. It then discusses constructing and learning Bayesian networks with the bnlearn package in R, including learning the structure and conditional probability tables. The document covers using Bayesian networks for inference with R. It discusses performing approximate inference at scale with R and Hadoop using RMR to parallelize queries over MapReduce. Finally, it provides an example of learning a network for the Adult dataset and performing inference using RMR.
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
An empirical study of the evolution of Eclipse third-party plug-insAlexander Serebrenik
This document summarizes a study on the evolution of third-party plugins for the Eclipse framework. The study analyzed 21 plugins over multiple versions using metrics related to dependencies, size, and quality. Most of Lehman's laws of software evolution were assessed, including continuing change, self-regulation, continuing growth, and conservation of familiarity. While some plugins supported the laws, others showed exceptions, and the results were generally inconclusive. The study represents a first step toward analyzing framework-constrained evolution, but a broader analysis is needed to fully validate Lehman's laws in this context.
PRACE is an international not-for-profit association based in Brussels that provides top-tier supercomputing resources and services to researchers. It has 25 member countries and hosts 6 supercomputers located in France, Germany, Italy, and Spain. PRACE supports scientific computing projects through peer-reviewed allocations of supercomputing resources. It also provides training programs and initiatives like SHAPE to support industry adoption of HPC. Since 2010, PRACE has awarded over 10 thousand million core hours of supercomputing time to nearly 400 projects across a wide range of scientific disciplines.
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.
The document summarizes a project that published linked geospatial data from several pan-European datasets. The project transformed datasets into RDF, loaded them into Virtuoso, and linked the datasets. This resulted in a Virtuoso instance with over 700 million triples. The project also developed three interfaces for navigating and visualizing the data: a SPARQL endpoint, faceted search browser, and map visualization. The linked data can be reused for applications in areas like real estate, tourism, and agriculture.
Vertex Centric Asynchronous Belief Propagation Algorithm for Large-Scale GraphsUniversidade de São Paulo
This document presents a vertex-centric asynchronous belief propagation algorithm for large-scale graphs. It proposes running belief propagation in a single computational node by processing vertices in parallel through an asynchronous vertex-centric approach. The algorithm is shown to converge faster than previous approaches and can scale to larger graphs by utilizing multicore architectures. Experiments on graphs with millions of edges demonstrate that the algorithm achieves similar accuracy to previous methods but with significantly better runtime performance, making it suitable for inference on very large real-world graphs.
- Fraunhofer is a leading applied research organization in Germany that works closely with industry and academia. It has over 60 institutes and 18,000 employees conducting contract research.
- Fraunhofer has a representative office in Seoul to collaborate with Korean partners on issues like renewable energy, electronics, and new materials. The document discusses opportunities to learn from each other's innovation systems.
- Both Germany and Korea invest heavily in research and development, though each country also faces challenges like rigid labor markets or maintaining competitiveness in global markets. The Fraunhofer model of applied research and technology transfer provides lessons for national innovation.
This document describes ProtOLAP, a methodology for rapid OLAP prototyping when source data is not initially available. ProtOLAP enables designers to create a conceptual schema based on user requirements, which is then automatically translated into a logical schema and prototype. Users manually input sample data and validate the prototype using simple pivot tables. If validated, source data is collected, ETL is designed, and the prototype is finalized. ProtOLAP allows for agile design approaches even when source data availability is delayed, by facilitating early user validation through sample data exploration.
The Frankfurt Big Data Lab carries out research in big data and data analytics from the perspective of information systems and computer science. The lab is located in Frankfurt and targets research applications for both industry and academia. Current research areas include big data management technologies, data analytics, graph databases/linked open data, and using big data for social good. One project focuses on using data to help with the inclusion of refugees in Frankfurt.
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.
Capgemini and SAP trends in warehouse automationJoe Vernon
This document discusses trends in warehouse automation. It covers innovations like wearable devices, machine-to-machine communication, and intelligent agents. Examples of automation innovations are highlighted, like hands-free order picking using augmented reality and automated forklift and pallet tracking. The document also reviews SAP's warehouse management system roadmap and how technologies like the Internet of Things can optimize distribution center operations and analytics.
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.
TUW - Quality of data-aware data analytics workflowsHong-Linh Truong
The document discusses quality of data-aware data analytics workflows. It begins with outlining the topics to be covered, which include data analytics workflows structures and systems, issues with quality of data-aware workflows, and quality of data-aware simulation workflows. It then provides examples of different workflow systems and frameworks for data analytics workflows. Key points discussed are the need to understand hierarchical workflow structures, addressing data and service concerns, importance of quality of data for data analytics workflows, and approaches to modeling quality of data metrics and optimizing workflows based on quality of data.
This curriculum vitae outlines the qualifications and experience of Dr. Andrew Paul Nisbet. He is currently a Senior Lecturer in Computer Science at Manchester Metropolitan University, where he has worked since 2004. His research focuses on compiler optimization techniques for parallel, multicore, and embedded systems. He has supervised several PhD students to completion and currently supervises two PhD students.
The document discusses network analysis and provides an overview of the topic. It covers that networks are found everywhere, from social and biological systems to financial networks. It also discusses complex networks, influential nodes, communities, and software tools for network analysis. A variety of network analysis methods and algorithms are presented, including approaches for identifying communities, central nodes, and other network properties. Examples of real-world networks and potential projects for network analysis are also mentioned.
Similaire à Deployment of Randomised Optimisation Algorithms Benchmarking in DAPHNE (20)
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
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
Evolutionary Optimization Algorithms & Large-Scale Machine LearningUniversity of Maribor
The document discusses a workshop organized by the DAPHNE project on evolutionary optimization algorithms and large-scale machine learning. It includes an agenda for a use case workshop on September 26th, 2023 in Graz, Austria. The document also provides background on differential evolution methods, including descriptions of the algorithm, control parameters, applications, and related work on improvements.
Solving 100-Digit Challenge with Score 100 by Extended Running Time and Paral...University of Maribor
The document discusses solving a 100-digit challenge using an optimization algorithm called Differential Evolution (DE). It begins with background on optimization algorithms and introduces the 100-digit challenge. It then describes the DE algorithm and variants developed by the author. The method section explains the DISHchain3e+12 algorithm used. Results and conclusions are then briefly mentioned. The document aims to explain how the 100-digit challenge was solved using an extended runtime and parallel benchmarking with DE.
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 document discusses the IEEE Slovenia Section. It has 300 members and has established technical committees in various fields including power engineering, communications, computing, signal processing, and more. It organizes an annual international conference on electrical engineering and computing. The section holds monthly meetings to discuss plans, affinity groups, and awards.
The document reports on the activities of the IEEE Slovenia Computational Intelligence Society (CIS) Chapter in 2021. Key activities included:
1) Organizing sessions at the ERK 2021 conference on computational intelligence.
2) Continued collaboration on joint events with other IEEE chapters and participation in benchmarking task forces for international computational intelligence conferences.
3) Initiatives to recruit new volunteers and sponsor additional events.
The chapter was active in celebrating anniversaries of IEEE Slovenia and related organizations, and increasing its visibility through news and social media.
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).
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdfSelcen Ozturkcan
Ozturkcan, S., Berndt, A., & Angelakis, A. (2024). Mending clothing to support sustainable fashion. Presented at the 31st Annual Conference by the Consortium for International Marketing Research (CIMaR), 10-13 Jun 2024, University of Gävle, Sweden.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
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).
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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
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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, ...
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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}.
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Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 14/141
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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
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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.
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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).
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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
—
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19. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
DISH - a Population-based Optimizer at SWEVO (Q1)
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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
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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.
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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
—
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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
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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)
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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).
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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
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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
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28. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix
Next Steps
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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
—
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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
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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).
—
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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
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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
—
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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).
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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
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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
—
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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
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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 —
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38. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
TOP500: EuroHPC Vega (tour at ASHPC23)
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39. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
TOP500: EuroHPC Vega (tour at ASHPC23)
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40. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
TOP500: EuroHPC Vega (tour at ASHPC23)
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41. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
TOP500: EuroHPC Vega (tour at ASHPC23)
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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.
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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
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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
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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
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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
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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.
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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]
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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).
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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/
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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.
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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.
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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).
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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
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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
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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
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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 . $SLURM PROCID
Text summarization/generation systems
are getting more and more useful
and accessible on deployed systems
(e.g. OpenAI’s ChatGPT, Microsoft’s Bing AI part,
NVIDIA’s (Fin)Megatron, BLOOM,
LaMDA, BERT, VALL-E, Point-E, etc.). -0.65
-0.6
-0.55
-0.5
-0.45
-0.4
-0.35
-0.3
-0.25
-0.2
-0.15
-0.1
1 10 100
Evaluation
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Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 58/141
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59. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
MODA at First EuroCC HPC Vega Supercomputer
Listing 2: Runnig at Vega MODA.
1 ===================================================================== GMAX=200 =====
2 [ ales . zamuda@vglogin0002 ˜]$ 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 [ ”$SLURM PROCID” = 0 ] . / runme . sh
25 real 6m14.687 s
26 user 590m45.641 s
27 sys 1m40.930 s
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Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 59/141
Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 59/141
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60. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
More Output: SLURM accounting
Listing 3: Example accounting tool at Vega: sacct.
[ ales . zamuda@vglogin0002 ˜]$ 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/.
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Aleš Zamuda 7@aleszamuda Deployment of ROAR Benchmarking in DAPHNE @ HiPEAC, Munich, 19 January 2024 60/141
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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
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62. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
SLURM
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63. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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64. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE HPC GenAI Language Video Machine Power Opportunities References
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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.
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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
—
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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.
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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