  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.
