3 Search Results for "Land, Kati"

Document

DOI: 10.4230/LIPIcs.SEA.2024.13

3/2-Dual Approximation for CPU/GPU Scheduling

Authors: Bernhard Sebastian Germann, Klaus Jansen, Felix Ohnesorge, and Malte Tutas

Published in: LIPIcs, Volume 301, 22nd International Symposium on Experimental Algorithms (SEA 2024)

Abstract

We present a fast and efficient 3/2 dual approximation algorithm for CPU/GPU scheduling under the objective of makespan minimization. In CPU/GPU scheduling tasks can be scheduled on two different architectures. When executed on the CPU, a task is moldable and can be assigned to multiple cores. The running time becomes a function in the assigned cores. On a GPU, the task is a classical job with a set processing time. Both settings have drawn recent independent scientific interest. For the moldable CPU scheduling, the current best known constant rate approximation is a 3/2 approximation algorithm [Wu et al. EJOR volume 306]. The best efficient algorithm for this setting is a 3/2+ε approximation [Mounie et al. SIAM '07] whereas GPU scheduling admits a 13/11 approximation [Coffman, Garey, Johnson SIAM'78]. We improve upon the current best known algorithms for CPU/GPU scheduling by Bleuse et al. by formulating a novel multidimensional multiple choice knapsack to allot tasks to either architecture and schedule them there with known algorithms. This yields an improved running time over the current state of the art. We complement our theoretical results with experimentation that shows a significant speedup by using practical optimizations and explore their efficacy.

Cite as

Bernhard Sebastian Germann, Klaus Jansen, Felix Ohnesorge, and Malte Tutas. 3/2-Dual Approximation for CPU/GPU Scheduling. In 22nd International Symposium on Experimental Algorithms (SEA 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 301, pp. 13:1-13:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{germann_et_al:LIPIcs.SEA.2024.13,
  author =	{Germann, Bernhard Sebastian and Jansen, Klaus and Ohnesorge, Felix and Tutas, Malte},
  title =	{{3/2-Dual Approximation for CPU/GPU Scheduling}},
  booktitle =	{22nd International Symposium on Experimental Algorithms (SEA 2024)},
  pages =	{13:1--13:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-325-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{301},
  editor =	{Liberti, Leo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2024.13},
  URN =		{urn:nbn:de:0030-drops-203782},
  doi =		{10.4230/LIPIcs.SEA.2024.13},
  annote =	{Keywords: computing, machine scheduling, moldable, CPU/GPU}
}

Document

Track A: Algorithms, Complexity and Games

DOI: 10.4230/LIPIcs.ICALP.2024.83

No Polynomial Kernels for Knapsack

Authors: Klaus Heeger, Danny Hermelin, Matthias Mnich, and Dvir Shabtay

Published in: LIPIcs, Volume 297, 51st International Colloquium on Automata, Languages, and Programming (ICALP 2024)

Abstract

This paper focuses on kernelization algorithms for the fundamental Knapsack problem. A kernelization algorithm (or kernel) is a polynomial-time reduction from a problem onto itself, where the output size is bounded by a function of some problem-specific parameter. Such algorithms provide a theoretical model for data reduction and preprocessing and are central in the area of parameterized complexity. In this way, a kernel for Knapsack for some parameter k reduces any instance of Knapsack to an equivalent instance of size at most f(k) in polynomial time, for some computable function f. When f(k) = k^{O(1)} then we call such a reduction a polynomial kernel. Our study focuses on two natural parameters for Knapsack: The number w_# of different item weights, and the number p_# of different item profits. Our main technical contribution is a proof showing that Knapsack does not admit a polynomial kernel for any of these two parameters under standard complexity-theoretic assumptions. Our proof discovers an elaborate application of the standard kernelization lower bound framework, and develops along the way novel ideas that should be useful for other problems as well. We complement our lower bounds by showing that Knapsack admits a polynomial kernel for the combined parameter w_# ⋅ p_#.

Cite as

Klaus Heeger, Danny Hermelin, Matthias Mnich, and Dvir Shabtay. No Polynomial Kernels for Knapsack. In 51st International Colloquium on Automata, Languages, and Programming (ICALP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 297, pp. 83:1-83:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{heeger_et_al:LIPIcs.ICALP.2024.83,
  author =	{Heeger, Klaus and Hermelin, Danny and Mnich, Matthias and Shabtay, Dvir},
  title =	{{No Polynomial Kernels for Knapsack}},
  booktitle =	{51st International Colloquium on Automata, Languages, and Programming (ICALP 2024)},
  pages =	{83:1--83:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-322-5},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{297},
  editor =	{Bringmann, Karl and Grohe, Martin and Puppis, Gabriele and Svensson, Ola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2024.83},
  URN =		{urn:nbn:de:0030-drops-202261},
  doi =		{10.4230/LIPIcs.ICALP.2024.83},
  annote =	{Keywords: Knapsack, polynomial kernels, compositions, number of different weights, number of different profits}
}

Document

DOI: 10.4230/LIPIcs.SWAT.2016.24

Estimating The Makespan of The Two-Valued Restricted Assignment Problem

Authors: Klaus Jansen, Kati Land, and Marten Maack

Published in: LIPIcs, Volume 53, 15th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2016)

Abstract

We consider a special case of the scheduling problem on unrelated machines, namely the Restricted Assignment Problem with two different processing times. We show that the configuration LP has an integrality gap of at most 5/3 ~ 1.667 for this problem. This allows us to estimate the optimal makespan within a factor of 5/3, improving upon the previously best known estimation algorithm with ratio 11/6 ~ 1.833 due to Chakrabarty, Khanna, and Li.

Cite as

Klaus Jansen, Kati Land, and Marten Maack. Estimating The Makespan of The Two-Valued Restricted Assignment Problem. In 15th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 53, pp. 24:1-24:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@InProceedings{jansen_et_al:LIPIcs.SWAT.2016.24,
  author =	{Jansen, Klaus and Land, Kati and Maack, Marten},
  title =	{{Estimating The Makespan of The Two-Valued Restricted Assignment Problem}},
  booktitle =	{15th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2016)},
  pages =	{24:1--24:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-011-8},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{53},
  editor =	{Pagh, Rasmus},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SWAT.2016.24},
  URN =		{urn:nbn:de:0030-drops-60467},
  doi =		{10.4230/LIPIcs.SWAT.2016.24},
  annote =	{Keywords: unrelated scheduling, restricted assignment, configuration LP, integrality gap, estimation algorithm}
}

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2 Jansen, Klaus
1 Germann, Bernhard Sebastian
1 Heeger, Klaus
1 Hermelin, Danny
1 Land, Kati
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1 Mathematics of computing → Combinatorial optimization
1 Mathematics of computing → Discrete optimization
1 Theory of computation → Parameterized complexity and exact algorithms
1 Theory of computation → Rounding techniques
1 Theory of computation → Scheduling algorithms

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1 CPU/GPU
1 Knapsack
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