GPGPU Performance Estimation with Core and Memory Frequency Scaling

Qiang Wang, Xiaowen Chu

Abstract: Graphics processing units (GPUs) support dynamic voltage and frequency scaling to balance computational performance and energy consumption. However, simple and accurate performance estimation for a given GPU kernel under different frequency settings is still lacking for real hardware, which is important to decide the best frequency configuration for energy saving. We reveal a fine-grained analytical model to estimate the execution time of GPU kernels with both core and memory frequency scaling. Over a 2 x range of both core and memory frequencies among 20 GPU kernels, our model achieves accurate results (4.83 % error on average) with real hardware. Compared to the cycle-level simulators, our model only needs simple micro-benchmarks to extract a set of hardware parameters and kernel performance counters to produce such high accuracy without kernel source analysis.