Search Results for author: Peter Sanders

Found 20 papers, 11 papers with code

Scalable Shared-Memory Hypergraph Partitioning

1 code implementation20 Oct 2020 Lars Gottesbüren, Tobias Heuer, Peter Sanders, Sebastian Schlag

With just four cores, Mt-KaHyPar is also slightly faster than the fastest sequential multilevel partitioner PaToH while producing better solutions on 83% of all instances.

Distributed, Parallel, and Cluster Computing

Engineering In-place (Shared-memory) Sorting Algorithms

3 code implementations28 Sep 2020 Michael Axtmann, Sascha Witt, Daniel Ferizovic, Peter Sanders

IPS$^4$o even outperforms the best integer sorting algorithms in a wide range of situations.

Distributed, Parallel, and Cluster Computing F.2.2

Linear Work Generation of R-MAT Graphs

2 code implementations9 May 2019 Lorenz Hübschle-Schneider, Peter Sanders

R-MAT is a simple, widely used recursive model for generating `complex network' graphs with a power law degree distribution and community structure.

Data Structures and Algorithms Distributed, Parallel, and Cluster Computing Discrete Mathematics Social and Information Networks

Network Flow-Based Refinement for Multilevel Hypergraph Partitioning

2 code implementations SEA 2018 2018 Tobias Heuer, Peter Sanders, Sebastian Schlag

We present a refinement framework for multilevel hypergraph partitioning that uses max-flow computations on pairs of blocks to improve the solution quality of a $k$-way partition.

Data Structures and Algorithms G.2.2; G.2.3

Communication-free Massively Distributed Graph Generation

1 code implementation20 Oct 2017 Daniel Funke, Sebastian Lamm, Peter Sanders, Christian Schulz, Darren Strash, Moritz von Looz

Analyzing massive complex networks yields promising insights about our everyday lives.

Distributed, Parallel, and Cluster Computing Data Structures and Algorithms Social and Information Networks

Distributed Evolutionary k-way Node Separators

no code implementations6 Feb 2017 Peter Sanders, Christian Schulz, Darren Strash, Robert Williger

Computing high quality node separators in large graphs is necessary for a variety of applications, ranging from divide-and-conquer algorithms to VLSI design.

Engineering a direct k-way Hypergraph Partitioning Algorithm

no code implementations ALENEX 2017 2017 Yaroslav Akhremtsev, Tobias Heuer, Peter Sanders, Sebastian Schlag

We also remove several further bottlenecks in processing large hyperedges, develop a faster contraction algorithm, and a new adaptive stopping rule for local search.

graph partitioning hypergraph partitioning

Efficient Random Sampling - Parallel, Vectorized, Cache-Efficient, and Online

1 code implementation17 Oct 2016 Peter Sanders, Sebastian Lamm, Lorenz Hübschle-Schneider, Emanuel Schrade, Carsten Dachsbacher

We consider the problem of sampling $n$ numbers from the range $\{1,\ldots, N\}$ without replacement on modern architectures.

Data Structures and Algorithms Distributed, Parallel, and Cluster Computing Mathematical Software G.4; G.3; G.2

Engineering a Distributed Full-Text Index

1 code implementation11 Oct 2016 Johannes Fischer, Florian Kurpicz, Peter Sanders

The result is that our index answers counting queries up to 5. 5 times faster than the distributed suffix array, while using about the same space.

Data Structures and Algorithms

Concurrent Hash Tables: Fast and General?(!)

1 code implementation15 Jan 2016 Tobias Maier, Peter Sanders, Roman Dementiev

Concurrent hash tables are one of the most important concurrent data structures with numerous applications.

Data Structures and Algorithms D.1.3; E.1; E.2

k-way Hypergraph Partitioning via n-Level Recursive Bisection

1 code implementation ALENEX 2016 2017 Sebastian Schlag, Vitali Henne, Tobias Heuer, Henning Meyerhenke, Peter Sanders, Christian Schulz

We develop a multilevel algorithm for hypergraph partitioning that contracts the vertices one at a time.

Data Structures and Algorithms

Finding Near-Optimal Independent Sets at Scale

no code implementations2 Sep 2015 Sebastian Lamm, Peter Sanders, Christian Schulz, Darren Strash, Renato F. Werneck

To avoid this problem, we recursively choose vertices that are likely to be in a large independent set (using an evolutionary approach), then further kernelize the graph.

n-Level Hypergraph Partitioning

1 code implementation4 May 2015 Vitali Henne, Henning Meyerhenke, Peter Sanders, Sebastian Schlag, Christian Schulz

Using label propagation local search is several times faster than hMetis and gives better quality than PaToH for a VLSI benchmark set.

Data Structures and Algorithms G.2.2; D.1.4

Incorporating Road Networks into Territory Design

no code implementations29 Apr 2015 Nitin Ahuja, Matthias Bender, Peter Sanders, Christian Schulz, Andreas Wagner

Given a set of basic areas, the territory design problem asks to create a predefined number of territories, each containing at least one basic area, such that an objective function is optimized.

graph partitioning

Route Planning in Transportation Networks

no code implementations20 Apr 2015 Hannah Bast, Daniel Delling, Andrew Goldberg, Matthias Müller-Hannemann, Thomas Pajor, Peter Sanders, Dorothea Wagner, Renato F. Werneck

We survey recent advances in algorithms for route planning in transportation networks.

Data Structures and Algorithms G.2.1; G.2.2; G.2.3; H.2.8; H.3.5; H.4.2

Graph Partitioning for Independent Sets

no code implementations5 Feb 2015 Sebastian Lamm, Peter Sanders, Christian Schulz

The core innovations of the algorithm are very natural combine operations based on graph partitioning and local search algorithms.

graph partitioning

Parallel Graph Partitioning for Complex Networks

no code implementations18 Apr 2014 Henning Meyerhenke, Peter Sanders, Christian Schulz

This paper addresses this problem by parallelizing and adapting the label propagation technique originally developed for graph clustering.

Graph Clustering graph partitioning

GPU sample sort

1 code implementation30 Sep 2009 Nikolaj Leischner, Vitaly Osipov, Peter Sanders

For uniformly distributed keys our sample sort is at least 25% and on average 68% faster than the best comparison-based sorting algorithm, GPU Thrust merge sort, and on average more than 2 times faster than GPU quicksort.

Data Structures and Algorithms Distributed, Parallel, and Cluster Computing

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