Volume

Dagstuhl Seminar Proceedings, Volume 10261

Publication Details

  • published at: 2010-11-23
  • Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik

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Document
DOI: 10.4230/DagSemProc.10261.1
10261 Abstracts Collection – Algorithm Engineering

Authors: Giuseppe F. Italiano, David S. Johnson, Petra Mutzel, and Peter Sanders

Abstract
From June 27 to July 2, the Dagstuhl Seminar 10261 ``Algorithm Engineering '' was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

Giuseppe F. Italiano, David S. Johnson, Petra Mutzel, and Peter Sanders. 10261 Abstracts Collection – Algorithm Engineering. In Algorithm Engineering. Dagstuhl Seminar Proceedings, Volume 10261, pp. 1-10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{italiano_et_al:DagSemProc.10261.1,
  author =	{Italiano, Giuseppe F. and Johnson, David S. and Mutzel, Petra and Sanders, Peter},
  title =	{{10261 Abstracts Collection – Algorithm Engineering}},
  booktitle =	{Algorithm Engineering},
  pages =	{1--10},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10261},
  editor =	{Giuseppe F. Italiano and David S. Johnson and Petra Mutzel and Peter Sanders},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10261.1},
  URN =		{urn:nbn:de:0030-drops-28179},
  doi =		{10.4230/DagSemProc.10261.1},
  annote =	{Keywords: Experimental algorithmics, Game theory, Parallel and distributed algorithms, Multi-core}
}
Document
DOI: 10.4230/DagSemProc.10261.2
10261 Executive Summary – Algorithm Engineering

Authors: Giuseppe F. Italiano, David S. Johnson, Petra Mutzel, and Peter Sanders

Abstract
Algorithm engineering (AE) consists of the design, theoretical analysis, implementation, and experimental evaluation of algorithms, with the aim of bridging the gap between theory and practice in the area of algorithms. In the last decade, this approach to algorithmic research has gained increasing attention. The aim of this seminar was to bring together researchers with different backgrounds, e.g., from combinatorial optimization, algorithmic theory, and algorithm engineering, in order to strengthen and foster collaborations in the area of algorithm engineering and to identify key research directions for the future.
Cite as

Giuseppe F. Italiano, David S. Johnson, Petra Mutzel, and Peter Sanders. 10261 Executive Summary – Algorithm Engineering. In Algorithm Engineering. Dagstuhl Seminar Proceedings, Volume 10261, pp. 1-2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{italiano_et_al:DagSemProc.10261.2,
  author =	{Italiano, Giuseppe F. and Johnson, David S. and Mutzel, Petra and Sanders, Peter},
  title =	{{10261 Executive Summary – Algorithm Engineering}},
  booktitle =	{Algorithm Engineering},
  pages =	{1--2},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10261},
  editor =	{Giuseppe F. Italiano and David S. Johnson and Petra Mutzel and Peter Sanders},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10261.2},
  URN =		{urn:nbn:de:0030-drops-27966},
  doi =		{10.4230/DagSemProc.10261.2},
  annote =	{Keywords: Experimental algorithmics, Game theory, Parallel and distributed algorithms, Multi-core}
}
Document
DOI: 10.4230/DagSemProc.10261.3
An Experimental Comparison of Speed Scaling Algorithms with Deadline Feasibility Constraints

Authors: Ahmed Abousamra, David P. Bunde, and Kirk Pruhs

Abstract
We consider the first, and most well studied, speed scaling problem in the algorithmic literature: where the scheduling quality of service measure is a deadline feasibility constraint, and where the power objective is to minimize the total energy used. Four online algorithms for this problem have been proposed in the algorithmic literature. Based on the best upper bound that can be proved on the competitive ratio, the ranking of the online algorithms from best to worst is: $qOA$, $OA$, $AVR$, $BKP$. As a test case on the effectiveness of competitive analysis to predict the best online algorithm, we report on an experimental ``horse race'' between these algorithms using instances based on web server traces. Our main conclusion is that the ranking of our algorithms based on their performance in our experiments is identical to the order predicted by competitive analysis. This ranking holds over a large range of possible power functions, and even if the power objective is temperature.
Cite as

Ahmed Abousamra, David P. Bunde, and Kirk Pruhs. An Experimental Comparison of Speed Scaling Algorithms with Deadline Feasibility Constraints. In Algorithm Engineering. Dagstuhl Seminar Proceedings, Volume 10261, pp. 1-22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{abousamra_et_al:DagSemProc.10261.3,
  author =	{Abousamra, Ahmed and Bunde, David P. and Pruhs, Kirk},
  title =	{{An Experimental Comparison of Speed Scaling Algorithms with Deadline Feasibility Constraints}},
  booktitle =	{Algorithm Engineering},
  pages =	{1--22},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10261},
  editor =	{Giuseppe F. Italiano and David S. Johnson and Petra Mutzel and Peter Sanders},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10261.3},
  URN =		{urn:nbn:de:0030-drops-27971},
  doi =		{10.4230/DagSemProc.10261.3},
  annote =	{Keywords: Scheduling, Speed Scaling, Experimental Algorithms, Power Management}
}
Document
DOI: 10.4230/DagSemProc.10261.4
On Dynamic Graph Partitioning and Graph Clustering using Diffusion

Authors: Henning Meyerhenke and Joachim Gehweiler

Abstract
Load balancing is an important requirement for the efficient execution of parallel numerical simulations. In particular when the simulation domain changes over time, the mapping of computational tasks to processors needs to be modified accordingly. State-of-the-art libraries for this problem are based on graph repartitioning. They have a number of drawbacks, including the optimized metric and the difficulty of parallelizing the popular repartitioning heuristic Kernighan-Lin (KL). Here we further explore the very promising diffusion-based graph partitioning algorithm DIBAP (Meyerhenke et al., JPDC 69(9):750–761, 2009) by adapting DIBAP to the related problem of load balancing. The presented experiments with graph sequences that imitate adaptive numerical simulations demonstrate the applicability and high quality of DIBAP for load balancing by repartitioning. Compared to the faster state-of-the-art repartitioners PARMETIS and parallel JOSTLE, DIBAP’s solutions have partitions with significantly fewer external edges and boundary nodes and the resulting average migration volume in the important maximum norm is also the best in most cases. We also prove that one of DIBAP's key components optimizes a relaxed version of the minimum edge cut problem. Moreover, we hint at a distributed algorithm based on ideas used in DIBAP for clustering a virtual P2P supercomputer.
Cite as

Henning Meyerhenke and Joachim Gehweiler. On Dynamic Graph Partitioning and Graph Clustering using Diffusion. In Algorithm Engineering. Dagstuhl Seminar Proceedings, Volume 10261, pp. 1-19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{meyerhenke_et_al:DagSemProc.10261.4,
  author =	{Meyerhenke, Henning and Gehweiler, Joachim},
  title =	{{On Dynamic Graph Partitioning and Graph Clustering using Diffusion}},
  booktitle =	{Algorithm Engineering},
  pages =	{1--19},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10261},
  editor =	{Giuseppe F. Italiano and David S. Johnson and Petra Mutzel and Peter Sanders},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10261.4},
  URN =		{urn:nbn:de:0030-drops-27980},
  doi =		{10.4230/DagSemProc.10261.4},
  annote =	{Keywords: Dynamic graph partitioning/clustering, disturbed diffusion, load balancing, relaxed cut optimization}
}

Filters

  • Authors
  • A
  • Abousamra, Ahmed
  • B
  • Bunde, David P.
  • G
  • Gehweiler, Joachim
  • I
  • Italiano, Giuseppe F.
  • J
  • Johnson, David S.
  • M
  • Meyerhenke, Henning
  • Mutzel, Petra
  • P
  • Pruhs, Kirk
  • S
  • Sanders, Peter

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