Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Adaptive scientific simulations require that periodic repartitioning occur dynamically throughout the course of the computation. The repartitionings should be computed so as to minimize both the inter-processor communications incurred during the iterative mesh-based computation and the data redistribution costs required to balance the load. Recently developed schemes for computing repartitionings provide the user with only a limited control of the tradeoffs among these objectives.  describes a new Unified Repartitioning Algorithm that can tradeoff one objective for the other dependent upon a user-defined parameter describing the relative costs of these objectives.  show that the Unified Repartitioning Algorithm is able to reduce the precise overheads associated with repartitioning as ll as or better than other repartitioning schemes for a variety of problems, regardless of the relative costs of performing inter-processor communication and data redistribution. Our experimental results show that this scheme is extremely fast and scalable to large problems.
['Kirk Schloegel', 'George Karypis', 'Vipin Kumar']
I am writing a research paper on a idea A Unified Algorithm for Load-balancing Adaptive Scientific Simulations
[{"from": "human", "value": "I am writing a research paper on a idea A Unified Algorithm for Load-balancing Adaptive Scientific Simulations"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Adaptive scientific simulations require that periodic repartitioning occur dynamically throughout the course of the computation. The repartitionings should be computed so as to minimize both the inter-processor communications incurred during the iterative mesh-based computation and the data redistribution costs required to balance the load. Recently developed schemes for computing repartitionings provide the user with only a limited control of the tradeoffs among these objectives.  describes a new Unified Repartitioning Algorithm that can tradeoff one objective for the other dependent upon a user-defined parameter describing the relative costs of these objectives.  show that the Unified Repartitioning Algorithm is able to reduce the precise overheads associated with repartitioning as ll as or better than other repartitioning schemes for a variety of problems, regardless of the relative costs of performing inter-processor communication and data redistribution. Our experimental results show that this scheme is extremely fast and scalable to large problems."}]