Abstract In this paper, we propose a novel mesh coarsening method called brick coarsening method. The proposed method can be used in conjunction with any graph partitioners and scales to very large meshes. This method reduces problem space by decomposing the original mesh into fixed-size blocks of nodes called bricks, layered in a similar way to conventional brick laying, and then assigning each node of the original mesh to appropriate brick. Our experiments indicate that the proposed method scales to very large meshes while allowing simple RCB partitioner to produce higher-quality partitions with significantly less edge cuts. Our results further indicate that the proposed brick-coarsening method allows more complicated partitioners like PT...
AbstractWe develop a partitioning algorithm to decompose complex 2D data into small simple subregion...
We develop a partitioning algorithm to decompose complex 2D data into small simple subregions for ef...
As polygonal models rapidly grow to sizes orders of magnitudes bigger than the memory of commodity w...
In this paper, we propose a novel mesh coarsening method called brick coarsening method. The propose...
Graph Partitioning is an important load balancing problem in parallel processing. The simplest case ...
In this paper, we present a fast and efficient mesh coarsening algorithm for 3D triangular meshes. T...
Abstract. The graph partitioning problem is widely used and studied in many practical and theoretica...
© 2018 Elsevier Ltd A parallel algorithm is proposed for scalable generation of large-scale tetrahed...
Mesh partitioning is often the preferred approach for solving unstructured computational mechanics p...
Parallel execution of computational mechanics codes requires efficient mesh-partitioning techniques....
Most finite element methods used nowadays utilize unstructured meshes. These meshes are often very l...
Abstract. The most commonly used method to tackle the graph partitioning problem in practice is the ...
Abstract. The graph partitioning problem is widely used and studied in many practical and theoretica...
A new method is described for optimising graph partitions which arise in mapping unstructured mesh ...
This paper describes an algorithm designed for the automatic coarsening of three-dimensional unstruc...
AbstractWe develop a partitioning algorithm to decompose complex 2D data into small simple subregion...
We develop a partitioning algorithm to decompose complex 2D data into small simple subregions for ef...
As polygonal models rapidly grow to sizes orders of magnitudes bigger than the memory of commodity w...
In this paper, we propose a novel mesh coarsening method called brick coarsening method. The propose...
Graph Partitioning is an important load balancing problem in parallel processing. The simplest case ...
In this paper, we present a fast and efficient mesh coarsening algorithm for 3D triangular meshes. T...
Abstract. The graph partitioning problem is widely used and studied in many practical and theoretica...
© 2018 Elsevier Ltd A parallel algorithm is proposed for scalable generation of large-scale tetrahed...
Mesh partitioning is often the preferred approach for solving unstructured computational mechanics p...
Parallel execution of computational mechanics codes requires efficient mesh-partitioning techniques....
Most finite element methods used nowadays utilize unstructured meshes. These meshes are often very l...
Abstract. The most commonly used method to tackle the graph partitioning problem in practice is the ...
Abstract. The graph partitioning problem is widely used and studied in many practical and theoretica...
A new method is described for optimising graph partitions which arise in mapping unstructured mesh ...
This paper describes an algorithm designed for the automatic coarsening of three-dimensional unstruc...
AbstractWe develop a partitioning algorithm to decompose complex 2D data into small simple subregion...
We develop a partitioning algorithm to decompose complex 2D data into small simple subregions for ef...
As polygonal models rapidly grow to sizes orders of magnitudes bigger than the memory of commodity w...