Due to their highly parallel multi-cores architecture, GPUs are being increasingly used in a wide range of computationally intensive applications. Compared to CPUs, GPUs can achieve higher performances at accelerating the programs' execution in an energy-efficient way. Therefore GPGPU computing is useful for high performance computing applications and in many scientific research fields. In order to bring further performance improvements, GPU clusters are increasingly adopted. The energy consumed by GPUs cannot be neglected. Therefore, an energy-efficient time scheduling of the programs that are going to be executed by the parallel GPUs based on their deadline as well as the assigned priorities could be deployed to face their energetic avidity. For this reason, we present in this paper a model enabling the measure of the power consumption and the time execution of some elementary operations running on a single GPU using a new developed energy measurement protocol. Consequently, using our methodology, energy needs of a program could be predicted, allowing a better task scheduling.
翻译：由于其高度平行的多核心结构, GPU正越来越多地用于一系列广泛的计算密集型应用。 与 CPU 相比, GPU 可以在以节能的方式加速执行程序方面取得更高绩效。 因此, GPIPU 计算对于高性能计算应用和许多科学研究领域非常有用。 为了进一步提高性能,GPU集群被日益采用。 GPU所消耗的能量不能被忽视。 因此, 平行的 GPU 所执行的程序的节能时间安排可以按照期限和指定的优先事项进行,以面对其精力充沛的热情。 为此,我们在本文件中提出一个模型,用于测量电力消耗量和一些使用新开发的能源测量协议运行于单一的GPU的初级操作的时间。 因此,可以使用我们的方法预测出一个方案的能源需求,从而可以改进任务时间安排。
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