16934

GPGPU Performance Estimation with Core and Memory Frequency Scaling

Qiang Wang, Xiaowen Chu
Department of Computer Science, Hong Kong Baptist University
arXiv:1701.05308 [cs.PF], (19 Jan 2017)

@article{wang2017gpgpu,

   title={GPGPU Performance Estimation with Core and Memory Frequency Scaling},

   author={Wang, Qiang and Chu, Xiaowen},

   year={2017},

   month={jan},

   archivePrefix={"arXiv"},

   primaryClass={cs.PF}

}

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Graphics Processing Units (GPUs) support dynamic voltage and frequency scaling (DVFS) in order to balance computational performance and energy consumption. However, there still lacks simple and accurate performance estimation of a given GPU kernel under different frequency settings on real hardware, which is important to decide best frequency configuration for energy saving. This paper reveals a fine-grained model to estimate the execution time of GPU kernels with both core and memory frequency scaling. Over a 2.5x range of both core and memory frequencies among 12 GPU kernels, our model achieves accurate results (within 3.5%) on real hardware. Compared with the cycle-level simulators, our model only needs some simple micro-benchmark to extract a set of hardware parameters and performance counters of the kernels to produce this high accuracy.
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