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Modeling Shared-Memory Metaheuristic Schemes for Electricity Consumption

机译：耗电量共享内存元启发式方案的建模

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This paper tackles the problem of modeling a shared-memory metaheuristic scheme. The use of a model of the execution time allows us to decide at running time the number of threads to use to obtain a reduced execution time. A parameterized metaheuristic scheme is used, so different metaheuristics and hybri-dations can be applied to a particular problem, and it is easier to obtain a satisfactory metaheuristic for the problem. The model of the execution time and consequently the optimum number of threads depend on a number of factors: the problem to be solved, the metaheuristic scheme and the implementation of the basic functions in it, the computational system where the problem is being solved, etc. So, obtaining a satisfactory model and an autotuning methodology is not an easy task. This paper presents an autotuning methodology for shared-memory parameterized metaheuristic schemes, and its application to a problem of minimization of electricity consumption in exploitation of wells. The model and the methodology work satisfactorily, which allows us to reduce the execution time and to obtain lower electricity consumptions than previously obtained.
机译：本文解决了对共享内存元启发式方案进行建模的问题。使用执行时间模型可以使我们在运行时确定用于减少执行时间的线程数。使用参数化的元启发式方案，因此可以将不同的元启发式和混合应用于特定问题，并且更容易获得该问题的令人满意的元启发式。执行时间的模型以及因此的最佳线程数取决于许多因素：要解决的问题，元启发式方案和其中的基本功能的实现，要解决问题的计算系统等因此，获得令人满意的模型和自动调整方法并非易事。本文提出了一种共享内存参数化元启发式方案的自整定方法，并将其应用于井开发中的电耗最小化问题。该模型和方法论令人满意地工作，这使我们能够减少执行时间并获得比以前获得的更低的电耗。

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