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Dagstuhl Reports, Volume 13, Issue 8
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Dagstuhl Reports, Volume 13
Journal: Dagstuhl Reports (DagRep)
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- published at: 2024-03-13
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
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Complete Issue
Dagstuhl Reports, Volume 13, Issue 8, August 2023, Complete Issue
Abstract
Dagstuhl Reports, Volume 13, Issue 8, August 2023, Complete Issue
Cite as
Dagstuhl Reports, Volume 13, Issue 8, pp. 1-166, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{DagRep.13.8,
title = {{Dagstuhl Reports, Volume 13, Issue 8, August 2023, Complete Issue}},
pages = {1--166},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2024},
volume = {13},
number = {8},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.8},
URN = {urn:nbn:de:0030-drops-198092},
doi = {10.4230/DagRep.13.8},
annote = {Keywords: Dagstuhl Reports, Volume 13, Issue 8, August 2023, Complete Issue}
}
Document
Front Matter
Dagstuhl Reports, Table of Contents, Volume 13, Issue 8, 2023
Abstract
Dagstuhl Reports, Table of Contents, Volume 13, Issue 8, 2023
Cite as
Dagstuhl Reports, Volume 13, Issue 8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{DagRep.13.8.i,
title = {{Dagstuhl Reports, Table of Contents, Volume 13, Issue 8, 2023}},
pages = {i--},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2024},
volume = {13},
number = {8},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.8.i},
URN = {urn:nbn:de:0030-drops-198102},
doi = {10.4230/DagRep.13.8.i},
annote = {Keywords: Table of Contents, Frontmatter}
}
Document
Recent Trends in Graph Decomposition (Dagstuhl Seminar 23331)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 23331 "Recent Trends in Graph Decomposition", which took place from 13. August to 18. August, 2023. The seminar brought together 33 experts from academia and industry to discuss graph decomposition, a pivotal technique for handling massive graphs in applications such as social networks and scientific simulations. The seminar addressed the challenges posed by contemporary hardware designs, the potential of deep neural networks and reinforcement learning in developing heuristics, the unique optimization requirements of large sparse data, and the need for scalable algorithms suitable for emerging architectures. Through presentations, discussions, and collaborative sessions, the event fostered an exchange of innovative ideas, leading to the creation of community notes highlighting key open problems in the field.
Cite as
George Karypis, Christian Schulz, Darren Strash, Deepak Ajwani, Rob H. Bisseling, Katrin Casel, Ümit V. Çatalyürek, Cédric Chevalier, Florian Chudigiewitsch, Marcelo Fonseca Faraj, Michael Fellows, Lars Gottesbüren, Tobias Heuer, Kamer Kaya, Jakub Lacki, Johannes Langguth, Xiaoye Sherry Li, Ruben Mayer, Johannes Meintrup, Yosuke Mizutani, François Pellegrini, Fabrizio Petrini, Frances Rosamond, Ilya Safro, Sebastian Schlag, Roohani Sharma, Blair D. Sullivan, Bora Uçar, and Albert-Jan Yzelman. Recent Trends in Graph Decomposition (Dagstuhl Seminar 23331). In Dagstuhl Reports, Volume 13, Issue 8, pp. 1-45, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{karypis_et_al:DagRep.13.8.1,
author = {Karypis, George and Schulz, Christian and Strash, Darren and Ajwani, Deepak and Bisseling, Rob H. and Casel, Katrin and \c{C}ataly\"{u}rek, \"{U}mit V. and Chevalier, C\'{e}dric and Chudigiewitsch, Florian and Faraj, Marcelo Fonseca and Fellows, Michael and Gottesb\"{u}ren, Lars and Heuer, Tobias and Kaya, Kamer and Lacki, Jakub and Langguth, Johannes and Li, Xiaoye Sherry and Mayer, Ruben and Meintrup, Johannes and Mizutani, Yosuke and Pellegrini, Fran\c{c}ois and Petrini, Fabrizio and Rosamond, Frances and Safro, Ilya and Schlag, Sebastian and Sharma, Roohani and Sullivan, Blair D. and U\c{c}ar, Bora and Yzelman, Albert-Jan},
title = {{Recent Trends in Graph Decomposition (Dagstuhl Seminar 23331)}},
pages = {1--45},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2024},
volume = {13},
number = {8},
editor = {Karypis, George and Schulz, Christian and Strash, Darren},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.8.1},
URN = {urn:nbn:de:0030-drops-198114},
doi = {10.4230/DagRep.13.8.1},
annote = {Keywords: combinatorial optimization, experimental algorithmics, parallel algorithms}
}
Document
Synergizing Theory and Practice of Automated Algorithm Design for Optimization (Dagstuhl Seminar 23332)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 23332, which focused on automated algorithm design (AAD) for optimization. AAD aims to propose good algorithms and/or parameters thereof for optimization problems in an automated fashion, instead of forcing this decision on the user. As such, AAD is applicable in a variety of domains. The seminar brought together a diverse, international set of researchers from AAD and closely related fields. Especially, we invited people from both the empirical and the theoretical domain. A main goal of the seminar was to enable vivid discussions between these two groups in order to synergize the knowledge from either domain, thus advancing the area of AAD as a whole, and to reduce the gap between theory and practice. Over the course of the seminar, a good mix of breakout sessions and talks took place, which were very well received and which we detail in this report. Efforts to synergize theory and practice bore some fruit, and other important aspects of AAD were highlighted and discussed. Overall, the seminar was a huge success.
Cite as
Diederick Vermetten, Martin S. Krejca, Marius Lindauer, Manuel López-Ibáñez, and Katherine M. Malan. Synergizing Theory and Practice of Automated Algorithm Design for Optimization (Dagstuhl Seminar 23332). In Dagstuhl Reports, Volume 13, Issue 8, pp. 46-70, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{vermetten_et_al:DagRep.13.8.46,
author = {Vermetten, Diederick and Krejca, Martin S. and Lindauer, Marius and L\'{o}pez-Ib\'{a}\~{n}ez, Manuel and Malan, Katherine M.},
title = {{Synergizing Theory and Practice of Automated Algorithm Design for Optimization (Dagstuhl Seminar 23332)}},
pages = {46--70},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2024},
volume = {13},
number = {8},
editor = {Vermetten, Diederick and Krejca, Martin S. and Lindauer, Marius and L\'{o}pez-Ib\'{a}\~{n}ez, Manuel and Malan, Katherine M.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.8.46},
URN = {urn:nbn:de:0030-drops-198128},
doi = {10.4230/DagRep.13.8.46},
annote = {Keywords: automated algorithm design, hyper-parameter tuning, parameter control, heuristic optimization, black-box optimization}
}
Document
Functionally Safe Multi-Core Systems (Dagstuhl Seminar 23341)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 23341 "Functionally Safe Multi-Core Systems". The seminar took place at a time when there is significant debate and disagreement in both academia and industry on how future safety-critical systems can be developed, certified, and deployed. This process is increasingly complex, as on the one hand, modern systems must provide more services while, on the other hand, analysing such systems becomes significantly more challenging in a multi-core scenario than it was in the single-core era. It is therefore a vital question to determine how the same level of certainty can be provided in the future.
The seminar brought together experts from academia and industry for the three major layers involved in safety-critical systems: application, middleware, and platform. They discussed the different perspectives, which problems are deemed specifically important, and potential solutions. One main focus when organizing the seminar was to not only present the different positions but also to provide space for lengthy discussion and disagreement.
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Georg von der Brüggen, Ian Gray, and Catherine Nemitz. Functionally Safe Multi-Core Systems (Dagstuhl Seminar 23341). In Dagstuhl Reports, Volume 13, Issue 8, pp. 71-90, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{vonderbruggen_et_al:DagRep.13.8.71,
author = {von der Br\"{u}ggen, Georg and Gray, Ian and Nemitz, Catherine},
title = {{Functionally Safe Multi-Core Systems (Dagstuhl Seminar 23341)}},
pages = {71--90},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2024},
volume = {13},
number = {8},
editor = {von der Br\"{u}ggen, Georg and Gray, Ian and Nemitz, Catherine},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.8.71},
URN = {urn:nbn:de:0030-drops-198135},
doi = {10.4230/DagRep.13.8.71},
annote = {Keywords: eda and micro-architectures, middleware, multi-core, safety-critical applications}
}
Document
Computational Geometry of Earth System Analysis (Dagstuhl Seminar 23342)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 23342 "Computational Geometry of Earth System Analysis". This seminar brought together experts of algorithms and the Earth sciences to foster collaborations that can tackle algorithmic problems in the Earth system by the crossover of expertise in these different areas. The Earth sciences include a manifold of disciplines that deal with atmospheric, oceanic and terrestrial observations to further our understanding of climate processes. New generations of observation systems that are being developed right now provide novel data about the atmospheric and surface conditions at increasing spatial and temporal resolution. This provides unique information to improve weather and climate prediction but cannot always be handled by traditional numerical models. Computational Geometry is rooted in a strong tradition of algorithm and complexity analysis applied to practical geometric problems. Efficient algorithmic methods developed in this field are often tailored to the low-dimensional geometric settings that arise in a multitude of application areas, but have until recently not been applied to problems arising in the Earth system sciences - and in particular not in meteorology.
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Susanne Crewell, Anne Driemel, Jeff M. Phillips, and Dwaipayan Chatterjee. Computational Geometry of Earth System Analysis (Dagstuhl Seminar 23342). In Dagstuhl Reports, Volume 13, Issue 8, pp. 91-105, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{crewell_et_al:DagRep.13.8.91,
author = {Crewell, Susanne and Driemel, Anne and Phillips, Jeff M. and Chatterjee, Dwaipayan},
title = {{Computational Geometry of Earth System Analysis (Dagstuhl Seminar 23342)}},
pages = {91--105},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2024},
volume = {13},
number = {8},
editor = {Crewell, Susanne and Driemel, Anne and Phillips, Jeff M. and Chatterjee, Dwaipayan},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.8.91},
URN = {urn:nbn:de:0030-drops-198147},
doi = {10.4230/DagRep.13.8.91},
annote = {Keywords: Data reduction, Event detection, Feature tracking, Geometric algorithms, Interpolation methods, Sensor placement}
}
Document
Algorithms and Complexity for Continuous Problems (Dagstuhl Seminar 23351)
Abstract
The Dagstuhl Seminar 23351 was held at the Leibniz Center for Informatics, Schloss Dagstuhl, from August 27 to September 1, 2023. This event was the 14th in a series of Dagstuhl Seminars, starting in 1991. During the seminar, researchers presented overview talks, recent research results, work in progress and open problems. The first section of this report describes the goal of the seminar, the main seminar topics, and the general structure of the seminar. The third section contains the abstracts of the talks given during the seminar and the forth section the problems presented at the problem session.
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Dmitriy Bilyk, Michael Gnewuch, Jan Vybíral, Larisa Yaroslavtseva, and Kumar Harsha. Algorithms and Complexity for Continuous Problems (Dagstuhl Seminar 23351). In Dagstuhl Reports, Volume 13, Issue 8, pp. 106-128, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{bilyk_et_al:DagRep.13.8.106,
author = {Bilyk, Dmitriy and Gnewuch, Michael and Vyb{\'\i}ral, Jan and Yaroslavtseva, Larisa and Harsha, Kumar},
title = {{Algorithms and Complexity for Continuous Problems (Dagstuhl Seminar 23351)}},
pages = {106--128},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2024},
volume = {13},
number = {8},
editor = {Bilyk, Dmitriy and Gnewuch, Michael and Vyb{\'\i}ral, Jan and Yaroslavtseva, Larisa and Harsha, Kumar},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.8.106},
URN = {urn:nbn:de:0030-drops-198152},
doi = {10.4230/DagRep.13.8.106},
annote = {Keywords: computational stochastics, infinite-variate problems, quasi-\{M\}onte \{C\}arlo, sampling, tractability analysis}
}
Document
Integrating HPC, AI, and Workflows for Scientific Data Analysis (Dagstuhl Seminar 23352)
Abstract
The Dagstuhl Seminar 23352, titled "Integrating HPC, AI, and Workflows for Scientific Data Analysis," held from August 27 to September 1, 2023, was a significant event focusing on the synergy between High-Performance Computing (HPC), Artificial Intelligence (AI), and scientific workflow technologies. The seminar recognized that modern Big Data analysis in science rests on three pillars: workflow technologies for reproducibility and steering, AI and Machine Learning (ML) for versatile analysis, and HPC for handling large data sets. These elements, while crucial, have traditionally been researched separately, leading to gaps in their integration. The seminar aimed to bridge these gaps, acknowledging the challenges and opportunities at the intersection of these technologies. The event highlighted the complex interplay between HPC, workflows, and ML, noting how ML has increasingly been integrated into scientific workflows, thereby enhancing resource demands and bringing new requirements to HPC architectures, like support for GPUs and iterative computations. The seminar also addressed the challenges in adapting HPC for large-scale ML tasks, including in areas like deep learning, and the need for workflow systems to evolve to leverage ML in data analysis fully. Moreover, the seminar explored how ML could optimize scientific workflow systems and HPC operations, such as through improved scheduling and fault tolerance. A key focus was on identifying prestigious use cases of ML in HPC and understanding their unique, unmet requirements. The stochastic nature of ML and its impact on the reproducibility of data analysis on HPC systems was also a topic of discussion.
Cite as
Rosa M. Badia, Laure Berti-Equille, Rafael Ferreira da Silva, and Ulf Leser. Integrating HPC, AI, and Workflows for Scientific Data Analysis (Dagstuhl Seminar 23352). In Dagstuhl Reports, Volume 13, Issue 8, pp. 129-164, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
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@Article{badia_et_al:DagRep.13.8.129,
author = {Badia, Rosa M. and Berti-Equille, Laure and da Silva, Rafael Ferreira and Leser, Ulf},
title = {{Integrating HPC, AI, and Workflows for Scientific Data Analysis (Dagstuhl Seminar 23352)}},
pages = {129--164},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2024},
volume = {13},
number = {8},
editor = {Badia, Rosa M. and Berti-Equille, Laure and da Silva, Rafael Ferreira and Leser, Ulf},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.8.129},
URN = {urn:nbn:de:0030-drops-198162},
doi = {10.4230/DagRep.13.8.129},
annote = {Keywords: Large scale data presentation and analysis, Exascale class machine optimization, Performance data analysis and root cause detection, High dimensional data representation}
}