187 citations · 581 across the 39 of their papers we have counts for
14 papers · 1 filter
Fast Dynamic Updates and Dynamic SpGEMM on MPI-Distributed Graphs
Alexander van der Grinten, Geert Custers, Duy Le Thanh +1
Sparse matrix multiplication (SpGEMM) is a fundamental kernel used in many diverse application areas, both numerical and discrete. For example, many algebraic graph algorithms rely…
Tarema: Adaptive Resource Allocation for Scalable Scientific Workflows in Heterogeneous Clusters
Jonathan Bader, Lauritz Thamsen, Svetlana Kulagina +3
Scientific workflow management systems like Nextflow support large-scale data analysis by abstracting away the details of scientific workflows. In these systems, workflows consist…
An MPI-based Algorithm for Mapping Complex Networks onto Hierarchical Architectures
Maria Predari, Charilaos Tzovas, Christian Schulz +1
Processing massive application graphs on distributed memory systems requires to map the graphs onto the system's processing elements (PEs). This task becomes all the more important…
Distributing Sparse Matrix/Graph Applications in Heterogeneous Clusters -- an Experimental Study
Charilaos Tzovas, Maria Predari, Henning Meyerhenke
Many problems in scientific and engineering applications contain sparse matrices or graphs as main input objects, e.g. numerical simulations on meshes. Large inputs are abundant th…
Scaling Betweenness Approximation to Billions of Edges by MPI-based Adaptive Sampling
Alexander van der Grinten, Henning Meyerhenke
Betweenness centrality is one of the most popular vertex centrality measures in network analysis. Hence, many (sequential and parallel) algorithms to compute or approximate between…
Parallel Adaptive Sampling with almost no Synchronization
Alexander van der Grinten, Eugenio Angriman, Henning Meyerhenke
Approximation via sampling is a widespread technique whenever exact solutions are too expensive. In this paper, we present techniques for an efficient parallelization of adaptive (…
Balanced k-means for Parallel Geometric Partitioning
Moritz von Looz, Charilaos Tzovas, Henning Meyerhenke
Mesh partitioning is an indispensable tool for efficient parallel numerical simulations. Its goal is to minimize communication between the processes of a simulation while achieving…
Topology-induced Enhancement of Mappings
Roland Glantz, Maria Predari, Henning Meyerhenke
In this paper we propose a new method to enhance a mapping of a parallel application's computational tasks to the processing elements (PEs) of a parallel computer. The i…
Shared Memory Parallel Subgraph Enumeration
Raphael Kimmig, Henning Meyerhenke, Darren Strash
The subgraph enumeration problem asks us to find all subgraphs of a target graph that are isomorphic to a given pattern graph. Determining whether even one such isomorphic subgraph…
An Empirical Comparison of Big Graph Frameworks in the Context of Network Analysis
Jannis Koch, Christian L. Staudt, Maximilian Vogel +1
Complex networks are relational data sets commonly represented as graphs. The analysis of their intricate structure is relevant to many areas of science and commerce, and data sets…
Graphs, Matrices, and the GraphBLAS: Seven Good Reasons
Jeremy Kepner, David Bader, Aydın Buluc +3
The analysis of graphs has become increasingly important to a wide range of applications. Graph analysis presents a number of unique challenges in the areas of (1) software complex…
Parallel Graph Partitioning for Complex Networks
Henning Meyerhenke, Peter Sanders, Christian Schulz
Processing large complex networks like social networks or web graphs has recently attracted considerable interest. In order to do this in parallel, we need to partition them into p…
Partitioning Complex Networks via Size-constrained Clustering
Henning Meyerhenke, Peter Sanders, Christian Schulz
The most commonly used method to tackle the graph partitioning problem in practice is the multilevel approach. During a coarsening phase, a multilevel graph partitioning algorithm…
Engineering Parallel Algorithms for Community Detection in Massive Networks
Christian L. Staudt, Henning Meyerhenke
The amount of graph-structured data has recently experienced an enormous growth in many applications. To transform such data into useful information, fast analytics algorithms and…