Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   design and implement an algorithm for finding the biconnected components of a given graph. Our algorithm is based on experimental evidence that finding the bridges of a graph is usually easier and faster in the parallel setting.  use this property to first decompose the graph into independent and maximal 2-edge-connected subgraphs. To identify the articulation points in these 2-edge connected subgraphs,  again convert this into a problem of finding the bridges on an auxiliary graph.     It is interesting to note that during the conversion process, the size of the graph may increase. Hover,  show that this small increase in size and the run time is offset by the consideration that finding bridges is easier in a parallel setting.  implement our algorithm on an Intel i7 980X CPU running 12 threads.  show that our algorithm is on average 2.45x faster than the best known current algorithms implemented on the same platform. Finally,  extend our approach to dense graphs by applying the sparsification technique suggested by Cong and Bader in [7].Â
['Meher Chaitanya', 'Kishore Kothapalli']
I am writing a research paper on a idea Efficient Multicore Algorithms For Identifying Biconnected Components
[{"from": "human", "value": "I am writing a research paper on a idea Efficient Multicore Algorithms For Identifying Biconnected Components"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   design and implement an algorithm for finding the biconnected components of a given graph. Our algorithm is based on experimental evidence that finding the bridges of a graph is usually easier and faster in the parallel setting.  use this property to first decompose the graph into independent and maximal 2-edge-connected subgraphs. To identify the articulation points in these 2-edge connected subgraphs,  again convert this into a problem of finding the bridges on an auxiliary graph.     It is interesting to note that during the conversion process, the size of the graph may increase. Hover,  show that this small increase in size and the run time is offset by the consideration that finding bridges is easier in a parallel setting.  implement our algorithm on an Intel i7 980X CPU running 12 threads.  show that our algorithm is on average 2.45x faster than the best known current algorithms implemented on the same platform. Finally,  extend our approach to dense graphs by applying the sparsification technique suggested by Cong and Bader in [7].\u00c2"}]