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Documents authored by Kiziltan, Zeynep

Artifact
Software
DOI: 10.4230/artifacts.24086
EFE repository

Authors: Alessio Pellegrino, Özgür Akgün, Nguyen Dang, Zeynep Kiziltan, and Ian Miguel

Abstract
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Alessio Pellegrino, Özgür Akgün, Nguyen Dang, Zeynep Kiziltan, Ian Miguel. EFE repository (Software, Source Code). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@misc{dagstuhl-artifact-24086,
   title = {{EFE repository}}, 
   author = {Pellegrino, Alessio and Akg\"{u}n, \"{O}zg\"{u}r and Dang, Nguyen and Kiziltan, Zeynep and Miguel, Ian},
   note = {Software, version 1.0., swhId: \href{https://archive.softwareheritage.org/swh:1:dir:0d5708bbc3b0395ddcd80b52bbb6ed8da6ffe252;origin=https://github.com/SeppiaBrilla/EFE_project;visit=swh:1:snp:d6c381103db3c1b63eb2574073e4466639a93ef3;anchor=swh:1:rev:5124050c380534eb9c0dcb49763e034a844aef1b}{\texttt{swh:1:dir:0d5708bbc3b0395ddcd80b52bbb6ed8da6ffe252}} (visited on 2025-08-08)},
   url = {https://github.com/SeppiaBrilla/EFE_project},
   doi = {10.4230/artifacts.24086},
}
Document
DOI: 10.4230/LIPIcs.CP.2025.31
Transformer-Based Feature Learning for Algorithm Selection in Combinatorial Optimisation

Authors: Alessio Pellegrino, Özgür Akgün, Nguyen Dang, Zeynep Kiziltan, and Ian Miguel

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Given a combinatorial optimisation problem, there are typically multiple ways of modelling it for presentation to an automated solver. Choosing the right combination of model and target solver can have a significant impact on the effectiveness of the solving process. The best combination of model and solver can also be instance-dependent: there may not exist a single combination that works best for all instances of the same problem. We consider the task of building machine learning models to automatically select the best combination for a problem instance. Critical to the learning process is to define instance features, which serve as input to the selection model. Our contribution is the automatic learning of instance features directly from the high-level representation of a problem instance using a transformer encoder. We evaluate the performance of our approach using the Essence modelling language via a case study of three problem classes.
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Alessio Pellegrino, Özgür Akgün, Nguyen Dang, Zeynep Kiziltan, and Ian Miguel. Transformer-Based Feature Learning for Algorithm Selection in Combinatorial Optimisation. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 31:1-31:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{pellegrino_et_al:LIPIcs.CP.2025.31,
  author =	{Pellegrino, Alessio and Akg\"{u}n, \"{O}zg\"{u}r and Dang, Nguyen and Kiziltan, Zeynep and Miguel, Ian},
  title =	{{Transformer-Based Feature Learning for Algorithm Selection in Combinatorial Optimisation}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{31:1--31:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.31},
  URN =		{urn:nbn:de:0030-drops-238928},
  doi =		{10.4230/LIPIcs.CP.2025.31},
  annote =	{Keywords: Constraint modelling, algorithm selection, feature extraction, machine learning, transformer architecture}
}
Document
DOI: 10.4230/LIPIcs.CP.2021.26
A Job Dispatcher for Large and Heterogeneous HPC Systems Running Modern Applications

Authors: Cristian Galleguillos, Zeynep Kiziltan, and Ricardo Soto

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)

Abstract
High-performance Computing (HPC) systems have become essential instruments in our modern society. As they get closer to exascale performance, HPC systems become larger in size and more heterogeneous in their computing resources. With recent advances in AI, HPC systems are also increasingly being used for applications that employ many short jobs with strict timing requirements. HPC job dispatchers need to therefore adopt techniques to go beyond the capabilities of those developed for small or homogeneous systems, or for traditional compute-intensive applications. In this paper, we present a job dispatcher suitable for today’s large and heterogeneous systems running modern applications. Unlike its predecessors, our dispatcher solves the entire dispatching problem using Constraint Programming (CP) with a model size independent of the system size. Experimental results based on a simulation study show that our approach can bring about significant performance gains over the existing CP-based dispatchers in a large or heterogeneous system.
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Cristian Galleguillos, Zeynep Kiziltan, and Ricardo Soto. A Job Dispatcher for Large and Heterogeneous HPC Systems Running Modern Applications. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 26:1-26:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{galleguillos_et_al:LIPIcs.CP.2021.26,
  author =	{Galleguillos, Cristian and Kiziltan, Zeynep and Soto, Ricardo},
  title =	{{A Job Dispatcher for Large and Heterogeneous HPC Systems Running Modern Applications}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{26:1--26:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.26},
  URN =		{urn:nbn:de:0030-drops-153171},
  doi =		{10.4230/LIPIcs.CP.2021.26},
  annote =	{Keywords: Constraint programming, HPC systems, heterogeneous systems, large systems, on-line job dispatching, resource allocation}
}
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