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Additionally, the paper addresses the suitable processor utilized as a part of the energy unit application on the premise of fuel cell characteristics.
The framework automatically executes these functions (maps and reduces) by a job controller in parallel over any number of processors utilizing both multiprocessors in one node and multinodes in computers of a network cluster.
The methodology is a hybrid method in which different levels of master and slave processors are used to distribute the computational grid points and to maximize the use of multiple processors utilized in GPU.
The efficiency at n processors is defined as: <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0013999.e001.PNG" class= inline-graphic"/> where n is the number of processors utilized and t is the wallclock time of the run.
Or a larger processor might utilize "slave" processors to conduct miscellaneous housekeeping duties, such as memory management.
In addition, a conventional processor cannot utilize the pipeline effectively, in the sense that a high percentage of the processor resources are wasted.
The language processor hence utilizes a few core operations for information processing that depend on position-specific constraints.
The advent of superscalar processors with out-of-order execution makes it increasingly difficult to determine how well an application is utilizing the processor and how to adapt the application to improve its performance.
Hence, a novel generalized adaptable and flexible template processor that utilizes the existing resources without modifying the business model is proposed.
The efficiency of our approach is rooted in the ordering and distribution of the grid points on the available processors; we utilize a Cuthill McKee ordering.
The thermal energy from the fuel processor is utilized in the steam generator, reformer, and heat exchangers to balance the heat requirements of the SOFC system.
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