I am glad to annonce a new Mesh graph feature into Meshcentral.com. When you install mesh agents in computers, the agents form a mesh, discovering and monitoring each other. Well, it's not important to know the details of how the mesh is formed, but just for fun, I added a way to visualize the mesh nodes and links between nodes.

# graph

# Parallel solution to Hosoya Index of Graph Problem (Vyukov)

The included code and white paper provides a parallel solution for the Hosoya Index problem, as described in the included problem description text file. The serial code uses the idea of a “sparsest cut” of an input graph. The sparsest cut divides edges of the graph into 3 sets: edges that are a part of the cut, and 2 mutually independent sets. From all matchings in the cut, the index value of the two other subsets can be computed recursively. Parallelism is achieved using Intel Threading Building Blocks.

# Parallel solution to Hosoya Index of Graph Problem (Uelschen)

The included code and white paper provides a parallel solution for the Hosoya Index problem, as described in the included problem description text file. Parallelism is achieved using Intel Threading Building Blocks.

DISCLAIMER: This code is provided by the author as a submitted contest entry, and is intended for educational use only. The code is not guaranteed to solve all instances of the input data sets and may require modifications to work in your own specific environment.

# Parallel solution to Hosoya Index of Graph Problem (Kuszmaul)

The included code and white paper provides a parallel solution for the Hosoya Index problem, as described in the included problem description text file. Parallelism is achieved using Cilk++.

DISCLAIMER: This code is provided by the author as a submitted contest entry, and is intended for educational use only. The code is not guaranteed to solve all instances of the input data sets and may require modifications to work in your own specific environment.

# Parallel Solution to Betweenness of graph problem (Vyukov)

Betweenness is a metric applied to a vertex within a weighted graph. For the purposes of this problem we will define betweenness of vertex T as the number of shortest paths between two vertices in the graph that includes the vertex T, but does not start or end with vertex T.

# Parallel Solution to Betweenness of graph problem (RuiDiao)

Betweenness is a metric applied to a vertex within a weighted graph. For the purposes of this problem we will define betweenness of vertex T as the number of shortest paths between two vertices in the graph that includes the vertex T, but does not start or end with vertex T.

# Parallel Solution to Betweenness of graph problem (akki)

Betweenness is a metric applied to a vertex within a weighted graph. For the purposes of this problem we will define betweenness of vertex T as the number of shortest paths between two vertices in the graph that includes the vertex T, but does not start or end with vertex T.

The included code and white paper provides a parallel solution for the betweenness computation utilizing a modified version of Dijkstra's algorithm for single source shortest paths. The parallel algorithm uses calls to Dijkstra’s algorithm from each possible source node in the graph.

# Parallel algorithm to solve Maximum Independent Set problem (Trouger, Zhejiang University)

The included source code finds a Maximum Independent Set (MIS) of a given graph, as described in the included problem description text file. The solution uses a modification to a max-clique algorithm found in a code library from University of Jilin, China. The algorithm uses a depth-first search component. This part of the algorithm is parallelized by assigning several recursive calls to the depth-first code on threads. The code is parallelized using Windows Threads.

# Parallel algorithm to solve Maximum Independent Set problem (Bradley Kuszmaul)

The included source code finds a Maximum Independent Set (MIS) of a given graph, as described in the included problem description text file. The included write-up gives an overview of Cilk++ and some of the tools available for Cilk programming. The serial algorithm is a recursive search of paths with and without nodes in the MIS. However, an upper bound on the size of the MIS and the degree of nodes is proved used within this basic algorithm. The parallelization was done with Cilk++ and involves spawning search tasks for the two recursive calls from the serial code.

# Parallel algorithm to solve a Hamiltonian Path problem variation (Travelling Baseball Fan) (Nicola Beschin)

The included source code implements a variation of the Hamiltonian Path problem, called the Travelling Baseball Fan Problem, as described in the included problem description text file. The serial algorithm is a recursive search of all potential paths. The parallelization was done with Intel Threading Building Blocks (TBB). Continuation tasks are set up for each tour day and parallel searches are executed within each new start day for the recursive algorithm to a given task generation depth. If there is no schedule for a day, the task for the next day is begun.

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