Abstract
SummaryWith the continued development of hardware and software, graphics processing unit (GPU) has been used in general purpose computational fields, while accelerating applications for CPUs. To achieve high computing performance, a GPU typically includes hundreds of computing units. The high density of computing resource on‐chip incurs high power consumption as well as engendering high performance. The power consumption problem has become one of the most important problems for the development of GPUs. Focusing on a CPU‐GPU heterogeneous parallel system, this research proposed an architecture‐level GPU energy model, with the aim of reducing system energy demand and improving system efficiency. Taking the influences of memory and temperature on GPU energy demand into account, a dynamic energy model based on division of computation and memory and a static energy model based on real‐time temperature perception were established. Validation, through the evaluation and comparative analysis of nine typical GPU programmes, demonstrated that these models could reduce chip energy demand under the performance constraint conditions imposed. Copyright © 2014 John Wiley & Sons, Ltd.
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