Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Graph partitioning is an important preprocessing step in applications dealing with sparse-irregular data. As such, the ability to efficiently partition a graph in parallel is crucial to the performance of these applications. The number of compute cores in a compute node continues to increase, demanding ever more scalability from shared-memory graph partitioners.   present algorithmic improvements to the multithreaded graph partitioner mt-Metis.  experimentally evaluate our methods on a 36 core machine, using 20 different graphs from a variety of domains. Our improvements decrease the runtime by 1.5-11.7X and improve strong scaling by 82%.
['Dominique LaSalle', 'Md. Mostofa Ali Patwary', 'Nadathur Satish', 'Narayanan Sundaram', 'Pradeep Dubey', 'George Karypis']
I am writing a research paper on a idea Improving graph partitioning for modern graphs and architectures
[{"from": "human", "value": "I am writing a research paper on a idea Improving graph partitioning for modern graphs and architectures"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Graph partitioning is an important preprocessing step in applications dealing with sparse-irregular data. As such, the ability to efficiently partition a graph in parallel is crucial to the performance of these applications. The number of compute cores in a compute node continues to increase, demanding ever more scalability from shared-memory graph partitioners.   present algorithmic improvements to the multithreaded graph partitioner mt-Metis.  experimentally evaluate our methods on a 36 core machine, using 20 different graphs from a variety of domains. Our improvements decrease the runtime by 1.5-11.7X and improve strong scaling by 82%."}]