Search Results

Documents authored by Pacut, Maciej

Document
Invited Talk
DOI: 10.4230/LIPIcs.SAND.2026.3
More Bang for the Buck: Superlinear Scaling with Distributed Self-Adjusting Systems (Invited Talk)

Authors: Jonas Köppeler, Maciej Pacut, Tamás Lévai, Vamsi Addanki, Stefan Schmid, and Gábor Rétvári

Published in: LIPIcs, Volume 373, 5th Symposium on Algorithmic Foundations of Dynamic Networks (SAND 2026)

Abstract
Extracting maximum performance from a limited pool of parallel compute resources remains a central challenge. In this paper, we show an optimization technique that allows certain distributed systems to attain faster-than-linear (superlinear) performance improvement with only a linear scaling of the worker pool. Our insight is that (1) dispatching jobs to parallel workers so that the locality of reference in the workers' input increases and (2) implementing the workers with a self-adjusting algorithm to take advantage of the higher locality can yield superlinear scaling in many practical applications. First, we demonstrate our technique in simulations: by scaling textbook self-adjusting algorithms, we obtain 100-3,300x speedup using only 48 CPU cores - up to 70x beyond linear scaling. After that, we re-engineer the default Linux packet classifier to attain a 5-25x raw performance improvement as compared to the vanilla kernel. We demonstrate 800x speedup on synthetic traces and 220x speedup on real firewall traces with 32 CPU cores. Given these insights, we develop a formal model and a set of design guidelines to help understand the applicability of our optimization strategy for particular distributed system workloads.
Cite as

Jonas Köppeler, Maciej Pacut, Tamás Lévai, Vamsi Addanki, Stefan Schmid, and Gábor Rétvári. More Bang for the Buck: Superlinear Scaling with Distributed Self-Adjusting Systems (Invited Talk). In 5th Symposium on Algorithmic Foundations of Dynamic Networks (SAND 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 373, pp. 3:1-3:28, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@InProceedings{koppeler_et_al:LIPIcs.SAND.2026.3,
  author =	{K\"{o}ppeler, Jonas and Pacut, Maciej and L\'{e}vai, Tam\'{a}s and Addanki, Vamsi and Schmid, Stefan and R\'{e}tv\'{a}ri, G\'{a}bor},
  title =	{{More Bang for the Buck: Superlinear Scaling with Distributed Self-Adjusting Systems}},
  booktitle =	{5th Symposium on Algorithmic Foundations of Dynamic Networks (SAND 2026)},
  pages =	{3:1--3:28},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-427-7},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{373},
  editor =	{Mertzios, George B. and Richa, Andr\'{e}a W.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAND.2026.3},
  URN =		{urn:nbn:de:0030-drops-262377},
  doi =		{10.4230/LIPIcs.SAND.2026.3},
  annote =	{Keywords: self-adjusting systems, superlinear scaling, packet classification}
}
Document
DOI: 10.4230/LIPIcs.ESA.2023.44
Online Algorithms with Randomly Infused Advice

Authors: Yuval Emek, Yuval Gil, Maciej Pacut, and Stefan Schmid

Published in: LIPIcs, Volume 274, 31st Annual European Symposium on Algorithms (ESA 2023)

Abstract
We introduce a novel method for the rigorous quantitative evaluation of online algorithms that relaxes the "radical worst-case" perspective of classic competitive analysis. In contrast to prior work, our method, referred to as randomly infused advice (RIA), does not make any assumptions about the input sequence and does not rely on the development of designated online algorithms. Rather, it can be applied to existing online randomized algorithms, introducing a means to evaluate their performance in scenarios that lie outside the radical worst-case regime. More concretely, an online algorithm ALG with RIA benefits from pieces of advice generated by an omniscient but not entirely reliable oracle. The crux of the new method is that the advice is provided to ALG by writing it into the buffer ℬ from which ALG normally reads its random bits, hence allowing us to augment it through a very simple and non-intrusive interface. The (un)reliability of the oracle is captured via a parameter 0 ≤ α ≤ 1 that determines the probability (per round) that the advice is successfully infused by the oracle; if the advice is not infused, which occurs with probability 1 - α, then the buffer ℬ contains fresh random bits (as in the classic online setting). The applicability of the new RIA method is demonstrated by applying it to three extensively studied online problems: paging, uniform metrical task systems, and online set cover. For these problems, we establish new upper bounds on the competitive ratio of classic online algorithms that improve as the infusion parameter α increases. These are complemented with (often tight) lower bounds on the competitive ratio of online algorithms with RIA for the three problems.
Cite as

Yuval Emek, Yuval Gil, Maciej Pacut, and Stefan Schmid. Online Algorithms with Randomly Infused Advice. In 31st Annual European Symposium on Algorithms (ESA 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 274, pp. 44:1-44:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

Copy BibTex To Clipboard

@InProceedings{emek_et_al:LIPIcs.ESA.2023.44,
  author =	{Emek, Yuval and Gil, Yuval and Pacut, Maciej and Schmid, Stefan},
  title =	{{Online Algorithms with Randomly Infused Advice}},
  booktitle =	{31st Annual European Symposium on Algorithms (ESA 2023)},
  pages =	{44:1--44:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-295-2},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{274},
  editor =	{G{\o}rtz, Inge Li and Farach-Colton, Martin and Puglisi, Simon J. and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2023.44},
  URN =		{urn:nbn:de:0030-drops-186970},
  doi =		{10.4230/LIPIcs.ESA.2023.44},
  annote =	{Keywords: Online algorithms, competitive analysis, advice}
}
Document
Brief Announcement
DOI: 10.4230/LIPIcs.DISC.2022.52
Brief Announcement: Temporal Locality in Online Algorithms

Authors: Maciej Pacut, Mahmoud Parham, Joel Rybicki, Stefan Schmid, Jukka Suomela, and Aleksandr Tereshchenko

Published in: LIPIcs, Volume 246, 36th International Symposium on Distributed Computing (DISC 2022)

Abstract
Online algorithms make decisions based on past inputs, with the goal of being competitive against an algorithm that sees also future inputs. In this work, we introduce time-local online algorithms; these are online algorithms in which the output at any given time is a function of only T latest inputs. Our main observation is that time-local online algorithms are closely connected to local distributed graph algorithms: distributed algorithms make decisions based on the local information in the spatial dimension, while time-local online algorithms make decisions based on the local information in the temporal dimension. We formalize this connection, and show how we can directly use the tools developed to study distributed approximability of graph optimization problems to prove upper and lower bounds on the competitive ratio achieved with time-local online algorithms. Moreover, we show how to use computational techniques to synthesize optimal time-local algorithms.
Cite as

Maciej Pacut, Mahmoud Parham, Joel Rybicki, Stefan Schmid, Jukka Suomela, and Aleksandr Tereshchenko. Brief Announcement: Temporal Locality in Online Algorithms. In 36th International Symposium on Distributed Computing (DISC 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 246, pp. 52:1-52:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

Copy BibTex To Clipboard

@InProceedings{pacut_et_al:LIPIcs.DISC.2022.52,
  author =	{Pacut, Maciej and Parham, Mahmoud and Rybicki, Joel and Schmid, Stefan and Suomela, Jukka and Tereshchenko, Aleksandr},
  title =	{{Brief Announcement: Temporal Locality in Online Algorithms}},
  booktitle =	{36th International Symposium on Distributed Computing (DISC 2022)},
  pages =	{52:1--52:3},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-255-6},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{246},
  editor =	{Scheideler, Christian},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DISC.2022.52},
  URN =		{urn:nbn:de:0030-drops-172431},
  doi =		{10.4230/LIPIcs.DISC.2022.52},
  annote =	{Keywords: Online algorithms, distributed algorithms}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2020.13
An Optimal Algorithm for Online Multiple Knapsack

Authors: Marcin Bienkowski, Maciej Pacut, and Krzysztof Piecuch

Published in: LIPIcs, Volume 168, 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020)

Abstract
In the online multiple knapsack problem, an algorithm faces a stream of items, and each item has to be either rejected or stored irrevocably in one of n bins (knapsacks) of equal size. The gain of an algorithm is equal to the sum of sizes of accepted items and the goal is to maximize the total gain. So far, for this natural problem, the best solution was the 0.5-competitive algorithm FirstFit (the result holds for any n ≥ 2). We present the first algorithm that beats this ratio, achieving the competitive ratio of 1/(1+ln(2))-O(1/n) ≈ 0.5906 - O(1/n). Our algorithm is deterministic and optimal up to lower-order terms, as the upper bound of 1/(1+ln(2)) for randomized solutions was given previously by Cygan et al. [TOCS 2016].
Cite as

Marcin Bienkowski, Maciej Pacut, and Krzysztof Piecuch. An Optimal Algorithm for Online Multiple Knapsack. In 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 168, pp. 13:1-13:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

Copy BibTex To Clipboard

@InProceedings{bienkowski_et_al:LIPIcs.ICALP.2020.13,
  author =	{Bienkowski, Marcin and Pacut, Maciej and Piecuch, Krzysztof},
  title =	{{An Optimal Algorithm for Online Multiple Knapsack}},
  booktitle =	{47th International Colloquium on Automata, Languages, and Programming (ICALP 2020)},
  pages =	{13:1--13:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-138-2},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{168},
  editor =	{Czumaj, Artur and Dawar, Anuj and Merelli, Emanuela},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2020.13},
  URN =		{urn:nbn:de:0030-drops-124207},
  doi =		{10.4230/LIPIcs.ICALP.2020.13},
  annote =	{Keywords: online knapsack, multiple knapsacks, bin packing, competitive analysis}
}
Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2026 Schloss Dagstuhl – LZI GmbH Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact